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Bektaş M, Pereira JK, Daams F, van der Peet DL. ChatGPT in surgery: a revolutionary innovation? Surg Today 2024; 54:964-971. [PMID: 38421439 PMCID: PMC11266448 DOI: 10.1007/s00595-024-02800-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Accepted: 12/13/2023] [Indexed: 03/02/2024]
Abstract
ChatGPT has brought about a new era of digital health, as this model has become prominent and been rapidly developing since its release. ChatGPT may be able to facilitate improvements in surgery as well; however, the influence of ChatGPT on surgery is largely unknown at present. Therefore, the present study reports on the current applications of ChatGPT in the field of surgery, evaluating its workflow, practical implementations, limitations, and future perspectives. A literature search was performed using the PubMed and Embase databases. The initial search was performed from its inception until July 2023. This study revealed that ChatGPT has promising capabilities in areas of surgical research, education, training, and practice. In daily practice, surgeons and surgical residents can be aided in performing logistics and administrative tasks, and patients can be more efficiently informed about the details of their condition. However, priority should be given to establishing proper policies and protocols to ensure the safe and reliable use of this model.
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Affiliation(s)
- Mustafa Bektaş
- Amsterdam UMC Location Vrije Universiteit Amsterdam, Surgery, De Boelelaan 1117, Amsterdam, The Netherlands.
| | - Jaime Ken Pereira
- Department of Computer Science, Vrije Universiteit Amsterdam, De Boelelaan 1105, Amsterdam, The Netherlands
| | - Freek Daams
- Amsterdam UMC Location Vrije Universiteit Amsterdam, Surgery, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Donald L van der Peet
- Amsterdam UMC Location Vrije Universiteit Amsterdam, Surgery, De Boelelaan 1117, Amsterdam, The Netherlands
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2
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Kim S, Fischetti C, Guy M, Hsu E, Fox J, Young SD. Artificial Intelligence ( AI) Applications for Point of Care Ultrasound (POCUS) in Low-Resource Settings: A Scoping Review. Diagnostics (Basel) 2024; 14:1669. [PMID: 39125545 PMCID: PMC11312308 DOI: 10.3390/diagnostics14151669] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2024] [Revised: 07/26/2024] [Accepted: 07/28/2024] [Indexed: 08/12/2024] Open
Abstract
Advancements in artificial intelligence (AI) for point-of-care ultrasound (POCUS) have ushered in new possibilities for medical diagnostics in low-resource settings. This review explores the current landscape of AI applications in POCUS across these environments, analyzing studies sourced from three databases-SCOPUS, PUBMED, and Google Scholars. Initially, 1196 records were identified, of which 1167 articles were excluded after a two-stage screening, leaving 29 unique studies for review. The majority of studies focused on deep learning algorithms to facilitate POCUS operations and interpretation in resource-constrained settings. Various types of low-resource settings were targeted, with a significant emphasis on low- and middle-income countries (LMICs), rural/remote areas, and emergency contexts. Notable limitations identified include challenges in generalizability, dataset availability, regional disparities in research, patient compliance, and ethical considerations. Additionally, the lack of standardization in POCUS devices, protocols, and algorithms emerged as a significant barrier to AI implementation. The diversity of POCUS AI applications in different domains (e.g., lung, hip, heart, etc.) illustrates the challenges of having to tailor to the specific needs of each application. By separating out the analysis by application area, researchers will better understand the distinct impacts and limitations of AI, aligning research and development efforts with the unique characteristics of each clinical condition. Despite these challenges, POCUS AI systems show promise in bridging gaps in healthcare delivery by aiding clinicians in low-resource settings. Future research endeavors should prioritize addressing the gaps identified in this review to enhance the feasibility and effectiveness of POCUS AI applications to improve healthcare outcomes in resource-constrained environments.
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Affiliation(s)
- Seungjun Kim
- Department of Informatics, University of California, Irvine, CA 92697, USA;
| | - Chanel Fischetti
- Department of Emergency Medicine, Brigham and Women’s Hospital, Boston, MA 02115, USA
| | - Megan Guy
- Department of Emergency Medicine, University of California, Irvine, CA 92697, USA; (M.G.); (E.H.); (J.F.)
| | - Edmund Hsu
- Department of Emergency Medicine, University of California, Irvine, CA 92697, USA; (M.G.); (E.H.); (J.F.)
| | - John Fox
- Department of Emergency Medicine, University of California, Irvine, CA 92697, USA; (M.G.); (E.H.); (J.F.)
| | - Sean D. Young
- Department of Informatics, University of California, Irvine, CA 92697, USA;
- Department of Emergency Medicine, University of California, Irvine, CA 92697, USA; (M.G.); (E.H.); (J.F.)
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3
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Sultan LR, Grasso V, Jose J, Al-Hasani M, Karmacharya MB, Sehgal CM. Advanced Techniques for Liver Fibrosis Detection: Spectral Photoacoustic Imaging and Superpixel Photoacoustic Unmixing Analysis for Collagen Tracking. SENSORS (BASEL, SWITZERLAND) 2024; 24:4617. [PMID: 39066017 PMCID: PMC11281248 DOI: 10.3390/s24144617] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/03/2024] [Revised: 06/30/2024] [Accepted: 07/15/2024] [Indexed: 07/28/2024]
Abstract
Liver fibrosis, a major global health issue, is marked by excessive collagen deposition that impairs liver function. Noninvasive methods for the direct visualization of collagen content are crucial for the early detection and monitoring of fibrosis progression. This study investigates the potential of spectral photoacoustic imaging (sPAI) to monitor collagen development in liver fibrosis. Utilizing a novel data-driven superpixel photoacoustic unmixing (SPAX) framework, we aimed to distinguish collagen presence and evaluate its correlation with fibrosis progression. We employed an established diethylnitrosamine (DEN) model in rats to study liver fibrosis over various time points. Our results revealed a significant correlation between increased collagen photoacoustic signal intensity and advanced fibrosis stages. Collagen abundance maps displayed dynamic changes throughout fibrosis progression. These findings underscore the potential of sPAI for the noninvasive monitoring of collagen dynamics and fibrosis severity assessment. This research advances the development of noninvasive diagnostic tools and personalized management strategies for liver fibrosis.
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Affiliation(s)
- Laith R. Sultan
- Clinical Research Core, Department of Radiology, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA;
| | - Valeria Grasso
- FUJIFILM VisualSonics, 1114 AB Amsterdam, The Netherlands; (V.G.); (J.J.)
| | - Jithin Jose
- FUJIFILM VisualSonics, 1114 AB Amsterdam, The Netherlands; (V.G.); (J.J.)
| | - Maryam Al-Hasani
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA; (M.A.-H.); (C.M.S.)
| | - Mrigendra B. Karmacharya
- Clinical Research Core, Department of Radiology, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA;
| | - Chandra M. Sehgal
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA; (M.A.-H.); (C.M.S.)
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Šarić M, Russo M, Kraljević L, Meter D. Extended Reality Telemedicine Collaboration System Using Patient Avatar Based on 3D Body Pose Estimation. SENSORS (BASEL, SWITZERLAND) 2023; 24:27. [PMID: 38202889 PMCID: PMC10780361 DOI: 10.3390/s24010027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Revised: 12/15/2023] [Accepted: 12/18/2023] [Indexed: 01/12/2024]
Abstract
Recent advances in extended reality (XR) technology have opened the possibility of significantly improving telemedicine systems. This is primarily achieved by transferring 3D information about patient state, which is utilized to create more immersive experiences on VR/AR headsets. In this paper, we propose an XR-based telemedicine collaboration system in which the patient is represented as a 3D avatar in an XR space shared by local and remote clinicians. The proposed system consists of an AR client application running on Microsoft HoloLens 2 used by a local clinician, a VR client application running on the HTC vive Pro used by a remote clinician, and a backend part running on the server. The patient is captured by a camera on the AR side, and the 3D body pose estimation is performed on frames from this camera stream to form a 3D patient avatar. Additionally, the AR and VR sides can interact with the patient avatar via virtual hands, and annotations can be performed on a 3D model. The main contribution of our work is the use of 3D body pose estimation for the creation of a 3D patient avatar. In this way, 3D body reconstruction using depth cameras is avoided, which reduces system complexity and hardware and network resources. Another contribution is the novel architecture of the proposed system, where audio and video streaming are realized using WebRTC protocol. The performance evaluation showed that the proposed system ensures high frame rates for both AR and VR client applications, while the processing latency remains at an acceptable level.
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Cohen JR, Stutts M. Interpersonal Well-Being and Suicidal Outcomes in a Nationally Representative Study of Adolescents: A Translational Study. Res Child Adolesc Psychopathol 2023; 51:1327-1341. [PMID: 37222862 DOI: 10.1007/s10802-023-01068-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/18/2023] [Indexed: 05/25/2023]
Abstract
Adolescent suicide continues to rise despite burgeoning research on interpersonal risk for suicide. This may reflect challenges in applying developmental psychopathology research into clinical settings. In response, the present study used a translational analytic plan to examine indices of social well-being most accurate and statistically fair for indexing adolescent suicide. Data from the National Comorbidity Survey Replication Adolescent Supplement were used. Adolescents aged 13-17 (N = 9,900) completed surveys on traumatic events, current relationships, and suicidal thoughts and attempts. Both frequentist (e.g., receiver operating characteristics) and Bayesian (e.g., Diagnostic Likelihood Ratios; DLRs) techniques provided insight into classification, calibration, and statistical fairness. Final algorithms were compared to a machine learning-informed algorithm. Overall, parental care and family cohesion best classified suicidal ideation, while these indices and school engagement best classified attempts. Multi-indicator algorithms suggested adolescents at high risk across these indices were approximately 3-times more likely to engage in ideation (DLR = 3.26) and 5-times more likely to engage in attempts (DLR = 4.53). Although equitable for attempts, models for ideation underperformed in non-White adolescents. Supplemental, machine learning-informed algorithms performed similarly, suggesting non-linear and interactive effects did not improve model performance. Future directions for interpersonal theories for suicide are discussed and clinical implications for suicide screening are demonstrated.
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Affiliation(s)
- Joseph R Cohen
- Department of Psychology, University of Illinois Urbana-Champaign, Champaign, IL, 61820, USA.
| | - Morgan Stutts
- Department of Psychology, University of Illinois Urbana-Champaign, Champaign, IL, 61820, USA
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Bishnoi V, Goel N. Tensor-RT-Based Transfer Learning Model for Lung Cancer Classification. J Digit Imaging 2023; 36:1364-1375. [PMID: 37059889 PMCID: PMC10407002 DOI: 10.1007/s10278-023-00822-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 03/26/2023] [Accepted: 03/28/2023] [Indexed: 04/16/2023] Open
Abstract
Cancer is a leading cause of death across the globe, in which lung cancer constitutes the maximum mortality rate. Early diagnosis through computed tomography scan imaging helps to identify the stages of lung cancer. Several deep learning-based classification methods have been employed for developing automatic systems for the diagnosis and detection of computed tomography scan lung slices. However, the diagnosis based on nodule detection is a challenging task as it requires manual annotation of nodule regions. Also, these computer-aided systems have yet not achieved the desired performance in real-time lung cancer classification. In the present paper, a high-speed real-time transfer learning-based framework is proposed for the classification of computed tomography lung cancer slices into benign and malignant. The proposed framework comprises of three modules: (i) pre-processing and segmentation of lung images using K-means clustering based on cosine distance and morphological operations; (ii) tuning and regularization of the proposed model named as weighted VGG deep network (WVDN); (iii) model inference in Nvidia tensor-RT during post-processing for the deployment in real-time applications. In this study, two pre-trained CNN models were experimented and compared with the proposed model. All the models have been trained on 19,419 computed tomography scan lung slices, which were obtained from the publicly available Lung Image Database Consortium and Image Database Resource Initiative dataset. The proposed model achieved the best classification metric, an accuracy of 0.932, precision, recall, an F1 score of 0.93, and Cohen's kappa score of 0.85. A statistical evaluation has also been performed on the classification parameters and achieved a p-value <0.0001 for the proposed model. The quantitative and statistical results validate the improved performance of the proposed model as compared to state-of-the-art methods. The proposed framework is based on complete computed tomography slices rather than the marked annotations and may help in improving clinical diagnosis.
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Affiliation(s)
- Vidhi Bishnoi
- Indira Gandhi Delhi Technical University for Women, Delhi, India
| | - Nidhi Goel
- Indira Gandhi Delhi Technical University for Women, Delhi, India
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Kierner S, Kucharski J, Kierner Z. Taxonomy of hybrid architectures involving rule-based reasoning and machine learning in clinical decision systems: A scoping review. J Biomed Inform 2023; 144:104428. [PMID: 37355025 DOI: 10.1016/j.jbi.2023.104428] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2023] [Revised: 03/28/2023] [Accepted: 06/15/2023] [Indexed: 06/26/2023]
Abstract
BACKGROUND As the application of Artificial Intelligence (AI) technologies increases in the healthcare sector, the industry faces a need to combine medical knowledge, often expressed as clinical rules, with advances in machine learning (ML), which offer high prediction accuracy at the expense of transparency of decision making. PURPOSE This paper seeks to review the present literature, identify hybrid architecture patterns that incorporate rules and machine learning, and evaluate the rationale behind their selection to inform future development and research on the design of transparent and precise clinical decision systems. METHODS PubMed, IEEE Explore, and Google Scholar were queried in search for papers from 1992 to 2022, with the keywords: "clinical decision system", "hybrid clinical architecture", "machine learning and clinical rules". Excluded articles did not use both ML and rules or did not provide any explanation of employed architecture. A proposed taxonomy was used to organize the results, analyze them, and depict them in graphical and tabular form. Two researchers, one with expertise in rule-based systems and another in ML, reviewed identified papers and discussed the work to minimize bias, and the third one re-reviewed the work to ensure consistency of reporting. RESULTS The authors screened 957 papers and reviewed 71 that met their criteria. Five distinct architecture archetypes were determined: Rules are Embedded in ML architecture (REML) (most used), ML pre-processes input data for Rule-Based inference (MLRB), Rule-Based method pre-processes input data for ML prediction (RBML), Rules influence ML training (RMLT), Parallel Ensemble of Rules and ML (PERML), which was rarely observed in clinical contexts. CONCLUSIONS Most architectures in the reviewed literature prioritize prediction accuracy over explainability and trustworthiness, which has led to more complex embedded approaches. Alternatively, parallel (PERML) architectures may be employed, allowing for a more transparent system that is easier to explain to patients and clinicians. The potential of this approach warrants further research. OTHER A limitation of the study may be that it reviews scientific literature, while algorithms implemented in clinical practice may present different distributions of motivations and implementations of hybrid architectures.
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Affiliation(s)
- Slawomir Kierner
- Lodz University of Technology, Faculty of Electrical, Electronic, Computer and Control Engineering, 27 Isabella Street, 02116 Boston, MA, USA.
| | - Jacek Kucharski
- Lodz University of Technology, Faculty of Electrical, Electronic, Computer and Control Engineering, 18/22 Stefanowskiego St., 90-924 Łodź, Poland.
| | - Zofia Kierner
- University of California, Berkeley College of Letters & Science, Berkeley, CA 94720-1786, USA.
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Janairo JIB. Sequence rules for gold-binding peptides. RSC Adv 2023; 13:21146-21152. [PMID: 37449032 PMCID: PMC10337651 DOI: 10.1039/d3ra04269c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Accepted: 07/06/2023] [Indexed: 07/18/2023] Open
Abstract
Metal-binding peptides play a central role in bionanotechnology, wherein they are responsible for directing growth and influencing the resulting properties of inorganic nanomaterials. One of the key advantages of using peptides to create nanomaterials is their versatility, wherein subtle changes in the sequence can have a dramatic effect on the structure and properties of the nanomaterial. However, precisely knowing which position and which amino acid should be modified within a given sequence to enhance a specific property can be a daunting challenge owing to combinatorial complexity. In this study, classification based on association rules was performed using 860 gold-binding peptides. Using a minimum support threshold of 0.035 and confidence of 0.9, 30 rules with confidence and lift values greater than 0.9 and 1, respectively, were extracted that can differentiate high-binding from low-binding peptides. The test performance of these rules for categorizing the peptides was found to be satisfactory, as characterized by accuracy = 0.942, F1 = 0.941, MCC = 0.884. What stands out from the extracted rules are the importance of tryptophan and arginine residues in differentiating peptides with high binding affinity from those with low affinity. In addition, the association rules revealed that positions 2 and 4 within a decapeptide are frequently involved in the rules, thus suggesting their importance in influencing peptide binding affinity to AuNPs. Collectively, this study identified sequence rules that may be used to design peptides with high binding affinity.
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Kalfas I, De Ketelaere B, Bunkens K, Saeys W. Towards automatic insect monitoring on witloof chicory fields using sticky plate image analysis. ECOL INFORM 2023. [DOI: 10.1016/j.ecoinf.2023.102037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/06/2023]
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10
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Dobrojevic M, Zivkovic M, Chhabra A, Sani NS, Bacanin N, Mohd Amin M. Addressing Internet of Things security by enhanced sine cosine metaheuristics tuned hybrid machine learning model and results interpretation based on SHAP approach. PeerJ Comput Sci 2023; 9:e1405. [PMID: 37409075 PMCID: PMC10319270 DOI: 10.7717/peerj-cs.1405] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Accepted: 04/27/2023] [Indexed: 07/07/2023]
Abstract
An ever increasing number of electronic devices integrated into the Internet of Things (IoT) generates vast amounts of data, which gets transported via network and stored for further analysis. However, besides the undisputed advantages of this technology, it also brings risks of unauthorized access and data compromise, situations where machine learning (ML) and artificial intelligence (AI) can help with detection of potential threats, intrusions and automation of the diagnostic process. The effectiveness of the applied algorithms largely depends on the previously performed optimization, i.e., predetermined values of hyperparameters and training conducted to achieve the desired result. Therefore, to address very important issue of IoT security, this article proposes an AI framework based on the simple convolutional neural network (CNN) and extreme machine learning machine (ELM) tuned by modified sine cosine algorithm (SCA). Not withstanding that many methods for addressing security issues have been developed, there is always a possibility for further improvements and proposed research tried to fill in this gap. The introduced framework was evaluated on two ToN IoT intrusion detection datasets, that consist of the network traffic data generated in Windows 7 and Windows 10 environments. The analysis of the results suggests that the proposed model achieved superior level of classification performance for the observed datasets. Additionally, besides conducting rigid statistical tests, best derived model is interpreted by SHapley Additive exPlanations (SHAP) analysis and results findings can be used by security experts to further enhance security of IoT systems.
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Affiliation(s)
- Milos Dobrojevic
- Informatics and Computing, Singidunum University, Belgrade, Serbia
| | - Miodrag Zivkovic
- Informatics and Computing, Singidunum University, Belgrade, Serbia
| | - Amit Chhabra
- Department of Computer Engineering & Technology, Guru Nanak Dev University, Amritsar, India
| | - Nor Samsiah Sani
- Center for Artificial Intelligence Technology, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia
| | - Nebojsa Bacanin
- Informatics and Computing, Singidunum University, Belgrade, Serbia
| | - Maifuza Mohd Amin
- Center for Artificial Intelligence Technology, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia
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Diaz Resendiz JL, Ponomaryov V, Reyes Reyes R, Sadovnychiy S. Expl ainable CAD System for Classification of Acute Lymphoblastic Leukemia Based on a Robust White Blood Cell Segmentation. Cancers (Basel) 2023; 15:3376. [PMID: 37444486 DOI: 10.3390/cancers15133376] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2023] [Revised: 06/25/2023] [Accepted: 06/26/2023] [Indexed: 07/15/2023] Open
Abstract
Leukemia is a significant health challenge, with high incidence and mortality rates. Computer-aided diagnosis (CAD) has emerged as a promising approach. However, deep-learning methods suffer from the "black box problem", leading to unreliable diagnoses. This research proposes an Explainable AI (XAI) Leukemia classification method that addresses this issue by incorporating a robust White Blood Cell (WBC) nuclei segmentation as a hard attention mechanism. The segmentation of WBC is achieved by combining image processing and U-Net techniques, resulting in improved overall performance. The segmented images are fed into modified ResNet-50 models, where the MLP classifier, activation functions, and training scheme have been tested for leukemia subtype classification. Additionally, we add visual explainability and feature space analysis techniques to offer an interpretable classification. Our segmentation algorithm achieves an Intersection over Union (IoU) of 0.91, in six databases. Furthermore, the deep-learning classifier achieves an accuracy of 99.9% on testing. The Grad CAM methods and clustering space analysis confirm improved network focus when classifying segmented images compared to non-segmented images. Overall, the proposed visual explainable CAD system has the potential to assist physicians in diagnosing leukemia and improving patient outcomes.
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Affiliation(s)
- Jose Luis Diaz Resendiz
- Instituto Politecnico Nacional, Escuela Superior de Ingenieria Mecanica y Electrica-Culhuacan, Av. Sta. Ana 1000, Mexico City 04440, Mexico
| | - Volodymyr Ponomaryov
- Instituto Politecnico Nacional, Escuela Superior de Ingenieria Mecanica y Electrica-Culhuacan, Av. Sta. Ana 1000, Mexico City 04440, Mexico
| | - Rogelio Reyes Reyes
- Instituto Politecnico Nacional, Escuela Superior de Ingenieria Mecanica y Electrica-Culhuacan, Av. Sta. Ana 1000, Mexico City 04440, Mexico
| | - Sergiy Sadovnychiy
- Instituto Mexicano del Petroleo, Eje Central Lazaro Cardenas Norte 152, Mexico City 07730, Mexico
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Dong T, Zhang Y, Xiao Q, Huang Y. The Control Method of Autonomous Flight Avoidance Barriers of UAVs in Confined Environments. SENSORS (BASEL, SWITZERLAND) 2023; 23:5896. [PMID: 37447745 DOI: 10.3390/s23135896] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 06/19/2023] [Accepted: 06/19/2023] [Indexed: 07/15/2023]
Abstract
This paper proposes an improved 3D-Vector Field Histogram (3D-VFH) algorithm for autonomous flight and local obstacle avoidance of multi-rotor unmanned aerial vehicles (UAVs) in a confined environment. Firstly, the method employs a target point coordinate system based on polar coordinates to convert the point cloud data, considering that long-range point cloud information has no effect on local obstacle avoidance by UAVs. This enables UAVs to effectively utilize obstacle information for obstacle avoidance and improves the real-time performance of the algorithm. Secondly, a sliding window algorithm is used to estimate the optimal flight path of the UAV and implement obstacle avoidance control, thereby maintaining the attitude stability of the UAV during obstacle avoidance flight. Finally, experimental analysis is conducted, and the results show that the UAV has good attitude stability during obstacle avoidance flight, can autonomously follow the expected trajectory, and can avoid dynamic obstacles, achieving precise obstacle avoidance.
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Affiliation(s)
- Tiantian Dong
- School of Electronics and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
- School of Microelectronics, Jiangsu Vocational College of Information Technology, Wuxi 214153, China
| | - Yonghong Zhang
- School of Electronics and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
| | - Qianyu Xiao
- School of Applied Technology, Changzhou University, Changzhou 213164, China
| | - Yi Huang
- School of Applied Technology, Changzhou University, Changzhou 213164, China
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13
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Bestion E, Raymond E, Mezouar S, Halfon P. Update on Autophagy Inhibitors in Cancer: Opening up to a Therapeutic Combination with Immune Checkpoint Inhibitors. Cells 2023; 12:1702. [PMID: 37443736 PMCID: PMC10341243 DOI: 10.3390/cells12131702] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Revised: 06/12/2023] [Accepted: 06/21/2023] [Indexed: 07/15/2023] Open
Abstract
Autophagy is a highly conserved and natural degradation process that helps maintain cell homeostasis through the elimination of old, worn, and defective cellular components, ensuring proper cell energy intake. The degradative pathway constitutes a protective barrier against diverse human diseases including cancer. Autophagy basal level has been reported to be completely dysregulated during the entire oncogenic process. Autophagy influences not only cancer initiation, development, and maintenance but also regulates cancer response to therapy. Currently, autophagy inhibitor candidates mainly target the early autophagy process without any successful preclinical/clinical development. Lessons learned from autophagy pharmaceutical manipulation as a curative option progressively help to improve drug design and to encounter new targets of interest. Combinatorial strategies with autophagy modulators are supported by abundant evidence, especially dealing with immune checkpoint inhibitors, for which encouraging preclinical results have been recently published. GNS561, a PPT1 inhibitor, is a promising autophagy modulator as it has started a phase 2 clinical trial in liver cancer indication, combined with atezolizumab and bevacizumab, an assessment without precedent in the field. This approach paves a new road, leading to the resurgence of anticancer autophagy inhibitors as an attractive therapeutic target in cancer.
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Affiliation(s)
- Eloïne Bestion
- Genoscience Pharma, 13006 Marseille, France; (E.R.); (S.M.); (P.H.)
| | - Eric Raymond
- Genoscience Pharma, 13006 Marseille, France; (E.R.); (S.M.); (P.H.)
- Department of Medical Oncology, Paris Saint-Joseph Hospital Group, 75014 Paris, France
| | - Soraya Mezouar
- Genoscience Pharma, 13006 Marseille, France; (E.R.); (S.M.); (P.H.)
- Établissement Français du Sang, Provence Alpes Côte d’Azur et Corse, Marseille, France; «Biologie des Groupes Sanguins», Aix Marseille Univ-CNRS-EFS-ADÉS, 13005 Marseille, France
| | - Philippe Halfon
- Genoscience Pharma, 13006 Marseille, France; (E.R.); (S.M.); (P.H.)
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Alajlan NN, Ibrahim DM. DDD TinyML: A TinyML-Based Driver Drowsiness Detection Model Using Deep Learning. SENSORS (BASEL, SWITZERLAND) 2023; 23:5696. [PMID: 37420860 DOI: 10.3390/s23125696] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Revised: 06/07/2023] [Accepted: 06/14/2023] [Indexed: 07/09/2023]
Abstract
Driver drowsiness is one of the main causes of traffic accidents today. In recent years, driver drowsiness detection has suffered from issues integrating deep learning (DL) with Internet-of-things (IoT) devices due to the limited resources of IoT devices, which pose a challenge to fulfilling DL models that demand large storage and computation. Thus, there are challenges to meeting the requirements of real-time driver drowsiness detection applications that need short latency and lightweight computation. To this end, we applied Tiny Machine Learning (TinyML) to a driver drowsiness detection case study. In this paper, we first present an overview of TinyML. After conducting some preliminary experiments, we proposed five lightweight DL models that can be deployed on a microcontroller. We applied three DL models: SqueezeNet, AlexNet, and CNN. In addition, we adopted two pretrained models (MobileNet-V2 and MobileNet-V3) to find the best model in terms of size and accuracy results. After that, we applied the optimization methods to DL models using quantization. Three quantization methods were applied: quantization-aware training (QAT), full-integer quantization (FIQ), and dynamic range quantization (DRQ). The obtained results in terms of the model size show that the CNN model achieved the smallest size of 0.05 MB using the DRQ method, followed by SqueezeNet, AlexNet MobileNet-V3, and MobileNet-V2, with 0.141 MB, 0.58 MB, 1.16 MB, and 1.55 MB, respectively. The result after applying the optimization method was 0.9964 accuracy using DRQ in the MobileNet-V2 model, which outperformed the other models, followed by the SqueezeNet and AlexNet models, with 0.9951 and 0.9924 accuracies, respectively, using DRQ.
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Affiliation(s)
- Norah N Alajlan
- Department of Information Technology, College of Computer, Qassim University, Buraydah 51452, Saudi Arabia
| | - Dina M Ibrahim
- Department of Information Technology, College of Computer, Qassim University, Buraydah 51452, Saudi Arabia
- Department of Computers and Control Engineering, Faculty of Engineering, Tanta University, Tanta 31733, Egypt
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15
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Cirrincione G, Cannata S, Cicceri G, Prinzi F, Currieri T, Lovino M, Militello C, Pasero E, Vitabile S. Transformer-Based Approach to Melanoma Detection. SENSORS (BASEL, SWITZERLAND) 2023; 23:5677. [PMID: 37420843 DOI: 10.3390/s23125677] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Revised: 06/09/2023] [Accepted: 06/15/2023] [Indexed: 07/09/2023]
Abstract
Melanoma is a malignant cancer type which develops when DNA damage occurs (mainly due to environmental factors such as ultraviolet rays). Often, melanoma results in intense and aggressive cell growth that, if not caught in time, can bring one toward death. Thus, early identification at the initial stage is fundamental to stopping the spread of cancer. In this paper, a ViT-based architecture able to classify melanoma versus non-cancerous lesions is presented. The proposed predictive model is trained and tested on public skin cancer data from the ISIC challenge, and the obtained results are highly promising. Different classifier configurations are considered and analyzed in order to find the most discriminating one. The best one reached an accuracy of 0.948, sensitivity of 0.928, specificity of 0.967, and AUROC of 0.948.
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Affiliation(s)
- Giansalvo Cirrincione
- Département Electronique-Electrotechnique-Automatique (EEA), University of Picardie Jules Verne, 80000 Amiens, France
| | - Sergio Cannata
- Department of Electronics and Telecommunications, Politecnico di Torino, 10129 Turin, Italy
| | - Giovanni Cicceri
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, 90127 Palermo, Italy
| | - Francesco Prinzi
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, 90127 Palermo, Italy
| | - Tiziana Currieri
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, 90127 Palermo, Italy
| | - Marta Lovino
- Department of Engineering Enzo Ferrari, University of Modena and Reggio Emilia, 41125 Modena, Italy
| | - Carmelo Militello
- Institute for High-Performance Computing and Networking (ICAR-CNR), Italian National Research Council, 90146 Palermo, Italy
| | - Eros Pasero
- Department of Electronics and Telecommunications, Politecnico di Torino, 10129 Turin, Italy
| | - Salvatore Vitabile
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, 90127 Palermo, Italy
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16
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Sfayyih AH, Sul aiman N, Sabry AH. A review on lung disease recognition by acoustic signal analysis with deep learning networks. JOURNAL OF BIG DATA 2023; 10:101. [PMID: 37333945 PMCID: PMC10259357 DOI: 10.1186/s40537-023-00762-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Accepted: 05/08/2023] [Indexed: 06/20/2023]
Abstract
Recently, assistive explanations for difficulties in the health check area have been made viable thanks in considerable portion to technologies like deep learning and machine learning. Using auditory analysis and medical imaging, they also increase the predictive accuracy for prompt and early disease detection. Medical professionals are thankful for such technological support since it helps them manage further patients because of the shortage of skilled human resources. In addition to serious illnesses like lung cancer and respiratory diseases, the plurality of breathing difficulties is gradually rising and endangering society. Because early prediction and immediate treatment are crucial for respiratory disorders, chest X-rays and respiratory sound audio are proving to be quite helpful together. Compared to related review studies on lung disease classification/detection using deep learning algorithms, only two review studies based on signal analysis for lung disease diagnosis have been conducted in 2011 and 2018. This work provides a review of lung disease recognition with acoustic signal analysis with deep learning networks. We anticipate that physicians and researchers working with sound-signal-based machine learning will find this material beneficial.
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Affiliation(s)
- Alyaa Hamel Sfayyih
- Department of Electrical and Electronic Engineering, Faculty of Engineering, Universiti Putra Malaysia, 43400 Serdang, Malaysia
| | - Nasri Sulaiman
- Department of Electrical and Electronic Engineering, Faculty of Engineering, Universiti Putra Malaysia, 43400 Serdang, Malaysia
| | - Ahmad H. Sabry
- Department of Computer Engineering, Al-Nahrain University, Al Jadriyah Bridge, 64074 Baghdad, Iraq
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17
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Ahmad SF, Han H, Alam MM, Rehmat MK, Irshad M, Arraño-Muñoz M, Ariza-Montes A. Impact of artificial intelligence on human loss in decision making, laziness and safety in education. HUMANITIES & SOCIAL SCIENCES COMMUNICATIONS 2023; 10:311. [PMID: 37325188 PMCID: PMC10251321 DOI: 10.1057/s41599-023-01787-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Accepted: 05/23/2023] [Indexed: 06/17/2023]
Abstract
This study examines the impact of artificial intelligence (AI) on loss in decision-making, laziness, and privacy concerns among university students in Pakistan and China. Like other sectors, education also adopts AI technologies to address modern-day challenges. AI investment will grow to USD 253.82 million from 2021 to 2025. However, worryingly, researchers and institutions across the globe are praising the positive role of AI but ignoring its concerns. This study is based on qualitative methodology using PLS-Smart for the data analysis. Primary data was collected from 285 students from different universities in Pakistan and China. The purposive Sampling technique was used to draw the sample from the population. The data analysis findings show that AI significantly impacts the loss of human decision-making and makes humans lazy. It also impacts security and privacy. The findings show that 68.9% of laziness in humans, 68.6% in personal privacy and security issues, and 27.7% in the loss of decision-making are due to the impact of artificial intelligence in Pakistani and Chinese society. From this, it was observed that human laziness is the most affected area due to AI. However, this study argues that significant preventive measures are necessary before implementing AI technology in education. Accepting AI without addressing the major human concerns would be like summoning the devils. Concentrating on justified designing and deploying and using AI for education is recommended to address the issue.
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18
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AlMohimeed A, Saleh H, El-Rashidy N, Saad RMA, El-Sappagh S, Mostafa S. Diagnosis of COVID-19 Using Chest X-ray Images and Disease Symptoms Based on Stacking Ensemble Deep Learning. Diagnostics (Basel) 2023; 13:diagnostics13111968. [PMID: 37296820 DOI: 10.3390/diagnostics13111968] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Revised: 05/17/2023] [Accepted: 05/30/2023] [Indexed: 06/12/2023] Open
Abstract
The COVID-19 virus is one of the most devastating illnesses humanity has ever faced. COVID-19 is an infection that is hard to diagnose until it has caused lung damage or blood clots. As a result, it is one of the most insidious diseases due to the lack of knowledge of its symptoms. Artificial intelligence (AI) technologies are being investigated for the early detection of COVID-19 using symptoms and chest X-ray images. Therefore, this work proposes stacking ensemble models using two types of COVID-19 datasets, symptoms and chest X-ray scans, to identify COVID-19. The first proposed model is a stacking ensemble model that is merged from the outputs of pre-trained models in the stacking: multi-layer perceptron (MLP), recurrent neural network (RNN), long short-term memory (LSTM), and gated recurrent unit (GRU). Stacking trains and evaluates the meta-learner as a support vector machine (SVM) to predict the final decision. Two datasets of COVID-19 symptoms are used to compare the first proposed model with MLP, RNN, LSTM, and GRU models. The second proposed model is a stacking ensemble model that is merged from the outputs of pre-trained DL models in the stacking: VGG16, InceptionV3, Resnet50, and DenseNet121; it uses stacking to train and evaluate the meta-learner (SVM) to identify the final prediction. Two datasets of COVID-19 chest X-ray images are used to compare the second proposed model with other DL models. The result has shown that the proposed models achieve the highest performance compared to other models for each dataset.
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Affiliation(s)
- Abdulaziz AlMohimeed
- College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 13318, Saudi Arabia
| | - Hager Saleh
- Faculty of Computers and Artificial Intelligence, South Valley University, Hurghada 84511, Egypt
| | - Nora El-Rashidy
- Machine Learning and Information Retrieval Department, Faculty of Artificial Intelligence, Kafrelsheiksh University, Kafrelsheiksh 13518, Egypt
| | - Redhwan M A Saad
- College of Informatics, Midocean University, Moroni 8722, Comoros
| | - Shaker El-Sappagh
- Faculty of Computer Science and Engineering, Galala University, Suez 435611, Egypt
- Information Systems Department, Faculty of Computers and Artificial Intelligence, Benha University, Banha 13518, Egypt
| | - Sherif Mostafa
- College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 13318, Saudi Arabia
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19
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Zhou L, Zhao C, Liu N, Yao X, Cheng Z. Improved LSTM-based deep learning model for COVID-19 prediction using optimized approach. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2023; 122:106157. [PMID: 36968247 PMCID: PMC10017389 DOI: 10.1016/j.engappai.2023.106157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 03/08/2023] [Accepted: 03/13/2023] [Indexed: 05/25/2023]
Abstract
Individuals in any country are badly impacted both economically and physically whenever an epidemic of infectious illnesses breaks out. A novel coronavirus strain was responsible for the outbreak of the coronavirus sickness in 2019. Corona Virus Disease 2019 (COVID-19) is the name that the World Health Organization (WHO) officially gave to the pneumonia that was caused by the novel coronavirus on February 11, 2020. The use of models that are informed by machine learning is currently a major focus of study in the field of improved forecasting. By displaying annual trends, forecasting models can be of use in performing impact assessments of potential outcomes. In this paper, proposed forecast models consisting of time series models such as long short-term memory (LSTM), bidirectional long short-term memory (Bi-LSTM), generalized regression unit (GRU), and dense-LSTM have been evaluated for time series prediction of confirmed cases, deaths, and recoveries in 12 major countries that have been affected by COVID-19. Tensorflow1.0 was used for programming. Indices known as mean absolute error (MAE), root means square error (RMSE), Median Absolute Error (MEDAE) and r2 score are utilized in the process of evaluating the performance of models. We presented various ways to time-series forecasting by making use of LSTM models (LSTM, BiLSTM), and we compared these proposed methods to other machine learning models to evaluate the performance of the models. Our study suggests that LSTM based models are among the most advanced models to forecast time series data.
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Affiliation(s)
- Luyu Zhou
- Department of Pharmacy, College of Biology, Hunan University, Changsha, Hunan 410082, China
- Institute for Translational Medicine, The Affiliated Hospital of Qingdao University, College of Medicine, Qingdao University, Qingdao 266021, China
| | - Chun Zhao
- Department of Pharmacy, College of Biology, Hunan University, Changsha, Hunan 410082, China
| | - Ning Liu
- Institute for Translational Medicine, The Affiliated Hospital of Qingdao University, College of Medicine, Qingdao University, Qingdao 266021, China
| | - Xingduo Yao
- Institute for Translational Medicine, The Affiliated Hospital of Qingdao University, College of Medicine, Qingdao University, Qingdao 266021, China
| | - Zewei Cheng
- Institute for Translational Medicine, The Affiliated Hospital of Qingdao University, College of Medicine, Qingdao University, Qingdao 266021, China
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20
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Lin CH, Nuha U. Sentiment analysis of Indonesian datasets based on a hybrid deep-learning strategy. JOURNAL OF BIG DATA 2023; 10:88. [PMID: 37274442 PMCID: PMC10226016 DOI: 10.1186/s40537-023-00782-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Accepted: 05/17/2023] [Indexed: 06/06/2023]
Abstract
Various attempts have been conducted to improve the performance of text-based sentiment analysis. These significant attempts have focused on text representation and model classifiers. This paper introduced a hybrid model based on the text representation and the classifier models, to address sentiment classification with various topics. The combination of BERT and a distilled version of BERT (DistilBERT) was selected in the representative vectors of the input sentences, while the combination of long short-term memory and temporal convolutional networks was taken to enhance the proposed model in understanding the semantics and context of each word. The experiment results showed that the proposed model outperformed various counterpart schemes in considered metrics. The reliability of the proposed model was confirmed in a mixed dataset containing nine topics.
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Affiliation(s)
- Chih-Hsueh Lin
- Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, 80778 Taiwan
| | - Ulin Nuha
- Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, 80778 Taiwan
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21
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Subramaniam K, Palanisamy N, Sinnaswamy RA, Muthusamy S, Mishra OP, Loganathan AK, Ramamoorthi P, Gnanakkan CARC, Thangavel G, Sundararajan SCM. A comprehensive review of analyzing the chest X-ray images to detect COVID-19 infections using deep learning techniques. Soft comput 2023; 27:1-22. [PMID: 37362273 PMCID: PMC10220331 DOI: 10.1007/s00500-023-08561-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/18/2023] [Indexed: 06/28/2023]
Abstract
COVID-19, a highly infectious respiratory disease a used by SARS virus, has killed millions of people across many countries. To enhance quick and accurate diagnosis of COVID-19, chest X-ray (CXR) imaging methods were commonly utilized. Identifying the infection manually by radio imaging, on the other hand, was considered, extremely difficult due to the time commitment and significant risk of human error. Emerging artificial intelligence (AI) techniques promised exploration in the development of precise and as well as automated COVID-19 detection tools. Convolution neural networks (CNN), a well performing deep learning strategy tends to gain substantial favors among AI approaches for COVID-19 classification. The preprints and published studies to diagnose COVID-19 with CXR pictures using CNN and other deep learning methodologies are reviewed and critically assessed in this research. This study focused on the methodology, algorithms, and preprocessing techniques used in various deep learning architectures, as well as datasets and performance studies of several deep learning architectures used in prediction and diagnosis. Our research concludes with a list of future research directions in COVID-19 imaging categorization.
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Affiliation(s)
- Kavitha Subramaniam
- Department of Computer Science and Engineering, Kongu Engineering College (Autonomous), Perundurai, Erode, Tamil Nadu India
| | - Natesan Palanisamy
- Department of Computer Science and Engineering, Kongu Engineering College (Autonomous), Perundurai, Erode, Tamil Nadu India
| | - Renugadevi Ammapalayam Sinnaswamy
- Department of Electronics and Communication Engineering, Kongu Engineering College (Autonomous), Perundurai, Erode, Tamil Nadu India
| | - Suresh Muthusamy
- Department of Electronics and Communication Engineering, Kongu Engineering College (Autonomous), Perundurai, Erode, Tamil Nadu India
| | - Om Prava Mishra
- Department of Electronics and Communication Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, Tamil Nadu India
| | - Ashok Kumar Loganathan
- Department of Electrical and Electronics Engineering, PSG College of Technology, Coimbatore, Tamil Nadu India
| | - Ponarun Ramamoorthi
- Department of Electrical and Electronics Engineering, Theni Kammavar Sangam College of Technology, Theni, Tamil Nadu India
| | | | - Gunasekaran Thangavel
- Department of Engineering, University of Technology and Applied Sciences, Muscat, Sultanate of Oman
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22
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Neo EX, Hasikin K, L ai KW, Mokhtar MI, Azizan MM, Hizaddin HF, Razak SA, Yanto. Artificial intelligence-assisted air quality monitoring for smart city management. PeerJ Comput Sci 2023; 9:e1306. [PMID: 37346549 PMCID: PMC10280551 DOI: 10.7717/peerj-cs.1306] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Accepted: 02/28/2023] [Indexed: 06/23/2023]
Abstract
Background The environment has been significantly impacted by rapid urbanization, leading to a need for changes in climate change and pollution indicators. The 4IR offers a potential solution to efficiently manage these impacts. Smart city ecosystems can provide well-designed, sustainable, and safe cities that enable holistic climate change and global warming solutions through various community-centred initiatives. These include smart planning techniques, smart environment monitoring, and smart governance. An air quality intelligence platform, which operates as a complete measurement site for monitoring and governing air quality, has shown promising results in providing actionable insights. This article aims to highlight the potential of machine learning models in predicting air quality, providing data-driven strategic and sustainable solutions for smart cities. Methods This study proposed an end-to-end air quality predictive model for smart city applications, utilizing four machine learning techniques and two deep learning techniques. These include Ada Boost, SVR, RF, KNN, MLP regressor and LSTM. The study was conducted in four different urban cities in Selangor, Malaysia, including Petaling Jaya, Banting, Klang, and Shah Alam. The model considered the air quality data of various pollution markers such as PM2.5, PM10, O3, and CO. Additionally, meteorological data including wind speed and wind direction were also considered, and their interactions with the pollutant markers were quantified. The study aimed to determine the correlation variance of the dependent variable in predicting air pollution and proposed a feature optimization process to reduce dimensionality and remove irrelevant features to enhance the prediction of PM2.5, improving the existing LSTM model. The study estimates the concentration of pollutants in the air based on training and highlights the contribution of feature optimization in air quality predictions through feature dimension reductions. Results In this section, the results of predicting the concentration of pollutants (PM2.5, PM10, O3, and CO) in the air are presented in R2 and RMSE. In predicting the PM10 and PM2.5concentration, LSTM performed the best overall high R2values in the four study areas with the R2 values of 0.998, 0.995, 0.918, and 0.993 in Banting, Petaling, Klang and Shah Alam stations, respectively. The study indicated that among the studied pollution markers, PM2.5,PM10, NO2, wind speed and humidity are the most important elements to monitor. By reducing the number of features used in the model the proposed feature optimization process can make the model more interpretable and provide insights into the most critical factor affecting air quality. Findings from this study can aid policymakers in understanding the underlying causes of air pollution and develop more effective smart strategies for reducing pollution levels.
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Affiliation(s)
- En Xin Neo
- Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Khairunnisa Hasikin
- Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia
- Center of Intelligent Systems for Emerging Technology (CISET), Faculty of Engineering, Kuala Lumpur, Malaysia
| | - Khin Wee Lai
- Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Mohd Istajib Mokhtar
- Department of Science and Technology Studies, Faculty of Sciences, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Muhammad Mokhzaini Azizan
- Department of Electrical and Electronic Engineering, Faculty of Engineering and Built Environment, Universiti Sains Islam Malaysia, Nilai, Negeri Sembilan, Malaysia
| | - Hanee Farzana Hizaddin
- Department of Chemical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Sarah Abdul Razak
- Institute of Biological Science, Faculty of Science, Univerisiti Malaya, Kuala Lumpur, Malaysia
| | - Yanto
- Civil Engineering Department, Jenderal Soedirman University, Purwokerto, Indonesia
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23
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Slesinski J, Wierzbicki D, Kedzierski M. Application of Multitemporal Change Detection in Radar Satellite Imagery Using REACTIV-Based Method for Geospatial Intelligence. SENSORS (BASEL, SWITZERLAND) 2023; 23:4922. [PMID: 37430836 DOI: 10.3390/s23104922] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 05/11/2023] [Accepted: 05/17/2023] [Indexed: 07/12/2023]
Abstract
Constant monitoring of airports and aviation bases has become one of the priorities in today's strategic security. It results in the necessity to develop the potential of satellite Earth observation systems and to intensify the efforts to develop the technologies of processing SAR data, in particular in the aspect of detecting changes. The aim of this work is to develop a new algorithm based on the modified core REACTIV in the multitemporal detection of changes in radar satellite imagery. For the purposes of the research works, the new algorithm implemented in the Google Earth Engine environment has been transformed so that it would meet the requirements posed by imagery intelligence. The assessment of the potential of the developed methodology was performed based on the analysis of the three main aspects of change detection: analysis of infrastructural changes, analysis of military activity, and impact effect evaluation. The proposed methodology enables automated detection of changes in multitemporal series of radar imagery. Apart from merely detecting the changes, the method also allows for the expansion of the change analysis result by adding another dimension: the determination of the time of the change.
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Affiliation(s)
- Jakub Slesinski
- Department of Imagery Intelligence, Faculty of Civil Engineering and Geodesy, Military University of Technology, 00-908 Warsaw, Poland
| | - Damian Wierzbicki
- Department of Imagery Intelligence, Faculty of Civil Engineering and Geodesy, Military University of Technology, 00-908 Warsaw, Poland
| | - Michal Kedzierski
- Department of Imagery Intelligence, Faculty of Civil Engineering and Geodesy, Military University of Technology, 00-908 Warsaw, Poland
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24
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Bo F, Li J, Wang W, Zhou K. Robust Attitude and Heading Estimation under Dynamic Motion and Magnetic Disturbance. MICROMACHINES 2023; 14:mi14051070. [PMID: 37241694 DOI: 10.3390/mi14051070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Revised: 05/02/2023] [Accepted: 05/16/2023] [Indexed: 05/28/2023]
Abstract
Robust and accurate attitude and heading estimation using Micro-Electromechanical System (MEMS) Inertial Measurement Units (IMU) is the most crucial technique that determines the accuracy of various downstream applications, especially pedestrian dead reckoning (PDR), human motion tracking, and Micro Aerial Vehicles (MAVs). However, the accuracy of the Attitude and Heading Reference System (AHRS) is often compromised by the noisy nature of low-cost MEMS-IMUs, dynamic motion-induced large external acceleration, and ubiquitous magnetic disturbance. To address these challenges, we propose a novel data-driven IMU calibration model that employs Temporal Convolutional Networks (TCNs) to model random errors and disturbance terms, providing denoised sensor data. For sensor fusion, we use an open-loop and decoupled version of the Extended Complementary Filter (ECF) to provide accurate and robust attitude estimation. Our proposed method is systematically evaluated using three public datasets, TUM VI, EuRoC MAV, and OxIOD, with different IMU devices, hardware platforms, motion modes, and environmental conditions; and it outperforms the advanced baseline data-driven methods and complementary filter on two metrics, namely absolute attitude error and absolute yaw error, by more than 23.4% and 23.9%. The generalization experiment results demonstrate the robustness of our model on different devices and using patterns.
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Affiliation(s)
- Fan Bo
- Institute of Microelectronics of the Chinese Academy of Sciences, Beijing 100029, China
- School of Microelectronics, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jia Li
- Institute of Microelectronics of the Chinese Academy of Sciences, Beijing 100029, China
- School of Microelectronics, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Weibing Wang
- Institute of Microelectronics of the Chinese Academy of Sciences, Beijing 100029, China
- School of Microelectronics, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Kaiyue Zhou
- Institute of Microelectronics of the Chinese Academy of Sciences, Beijing 100029, China
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25
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Bavle H, Sanchez-Lopez JL, Cimarelli C, Tourani A, Voos H. From SLAM to Situational Awareness: Challenges and Survey. SENSORS (BASEL, SWITZERLAND) 2023; 23:4849. [PMID: 37430762 DOI: 10.3390/s23104849] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Revised: 04/27/2023] [Accepted: 05/13/2023] [Indexed: 07/12/2023]
Abstract
The capability of a mobile robot to efficiently and safely perform complex missions is limited by its knowledge of the environment, namely the situation. Advanced reasoning, decision-making, and execution skills enable an intelligent agent to act autonomously in unknown environments. Situational Awareness (SA) is a fundamental capability of humans that has been deeply studied in various fields, such as psychology, military, aerospace, and education. Nevertheless, it has yet to be considered in robotics, which has focused on single compartmentalized concepts such as sensing, spatial perception, sensor fusion, state estimation, and Simultaneous Localization and Mapping (SLAM). Hence, the present research aims to connect the broad multidisciplinary existing knowledge to pave the way for a complete SA system for mobile robotics that we deem paramount for autonomy. To this aim, we define the principal components to structure a robotic SA and their area of competence. Accordingly, this paper investigates each aspect of SA, surveying the state-of-the-art robotics algorithms that cover them, and discusses their current limitations. Remarkably, essential aspects of SA are still immature since the current algorithmic development restricts their performance to only specific environments. Nevertheless, Artificial Intelligence (AI), particularly Deep Learning (DL), has brought new methods to bridge the gap that maintains these fields apart from the deployment to real-world scenarios. Furthermore, an opportunity has been discovered to interconnect the vastly fragmented space of robotic comprehension algorithms through the mechanism of Situational Graph (S-Graph), a generalization of the well-known scene graph. Therefore, we finally shape our vision for the future of robotic situational awareness by discussing interesting recent research directions.
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Affiliation(s)
- Hriday Bavle
- Interdisciplinary Center for Security Reliability and Trust (SnT), University of Luxembourg, 1855 Luxembourg, Luxembourg
| | - Jose Luis Sanchez-Lopez
- Interdisciplinary Center for Security Reliability and Trust (SnT), University of Luxembourg, 1855 Luxembourg, Luxembourg
| | - Claudio Cimarelli
- Interdisciplinary Center for Security Reliability and Trust (SnT), University of Luxembourg, 1855 Luxembourg, Luxembourg
| | - Ali Tourani
- Interdisciplinary Center for Security Reliability and Trust (SnT), University of Luxembourg, 1855 Luxembourg, Luxembourg
| | - Holger Voos
- Interdisciplinary Center for Security Reliability and Trust (SnT), University of Luxembourg, 1855 Luxembourg, Luxembourg
- Department of Engineering, Faculty of Science, Technology, and Medicine (FSTM), University of Luxembourg, 1359 Luxembourg, Luxembourg
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26
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Sfayyih AH, Sabry AH, Jameel SM, Sul aiman N, Raafat SM, Humaidi AJ, Kubaiaisi YMA. Acoustic-Based Deep Learning Architectures for Lung Disease Diagnosis: A Comprehensive Overview. Diagnostics (Basel) 2023; 13:diagnostics13101748. [PMID: 37238233 DOI: 10.3390/diagnostics13101748] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 05/04/2023] [Accepted: 05/11/2023] [Indexed: 05/28/2023] Open
Abstract
Lung auscultation has long been used as a valuable medical tool to assess respiratory health and has gotten a lot of attention in recent years, notably following the coronavirus epidemic. Lung auscultation is used to assess a patient's respiratory role. Modern technological progress has guided the growth of computer-based respiratory speech investigation, a valuable tool for detecting lung abnormalities and diseases. Several recent studies have reviewed this important area, but none are specific to lung sound-based analysis with deep-learning architectures from one side and the provided information was not sufficient for a good understanding of these techniques. This paper gives a complete review of prior deep-learning-based architecture lung sound analysis. Deep-learning-based respiratory sound analysis articles are found in different databases including the Plos, ACM Digital Libraries, Elsevier, PubMed, MDPI, Springer, and IEEE. More than 160 publications were extracted and submitted for assessment. This paper discusses different trends in pathology/lung sound, the common features for classifying lung sounds, several considered datasets, classification methods, signal processing techniques, and some statistical information based on previous study findings. Finally, the assessment concludes with a discussion of potential future improvements and recommendations.
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Affiliation(s)
- Alyaa Hamel Sfayyih
- Department of Electrical and Electronic Engineering, Faculty of Engineering, University Putra Malaysia, Serdang 43400, Malaysia
| | - Ahmad H Sabry
- Department of Computer Engineering, Al-Nahrain University Al Jadriyah Bridge, Baghdad 64074, Iraq
| | | | - Nasri Sulaiman
- Department of Electrical and Electronic Engineering, Faculty of Engineering, University Putra Malaysia, Serdang 43400, Malaysia
| | - Safanah Mudheher Raafat
- Department of Control and Systems Engineering, University of Technology, Baghdad 10011, Iraq
| | - Amjad J Humaidi
- Department of Control and Systems Engineering, University of Technology, Baghdad 10011, Iraq
| | - Yasir Mahmood Al Kubaiaisi
- Department of Sustainability Management, Dubai Academic Health Corporation, Dubai 4545, United Arab Emirates
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27
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Khan F, Zafar N, Tahir MN, Aqib M, Waheed H, Haroon Z. A mobile-based system for m aize plant leaf disease detection and classification using deep learning. FRONTIERS IN PLANT SCIENCE 2023; 14:1079366. [PMID: 37255561 PMCID: PMC10226393 DOI: 10.3389/fpls.2023.1079366] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Accepted: 03/24/2023] [Indexed: 06/01/2023]
Abstract
Artificial Intelligence has been used for many applications such as medical, communication, object detection, and object tracking. Maize crop, which is the major crop in the world, is affected by several types of diseases which lower its yield and affect the quality. This paper focuses on this issue and provides an application for the detection and classification of diseases in maize crop using deep learning models. In addition to this, the developed application also returns the segmented images of affected leaves and thus enables us to track the disease spots on each leaf. For this purpose, a dataset of three maize crop diseases named Blight, Sugarcane Mosaic virus, and Leaf Spot is collected from the University Research Farm Koont, PMAS-AAUR at different growth stages on contrasting weather conditions. This data was used for training different prediction models including YOLOv3-tiny, YOLOv4, YOLOv5s, YOLOv7s, and YOLOv8n and the reported prediction accuracy was 69.40%, 97.50%, 88.23%, 93.30%, and 99.04% respectively. Results demonstrate that the prediction accuracy of the YOLOv8n model is higher than the other applied models. This model has shown excellent results while localizing the affected area of the leaf accurately with a higher confidence score. YOLOv8n is the latest model used for the detection of diseases as compared to the other approaches in the available literature. Also, worked on sugarcane mosaic virus using deep learning models has also been reported for the first time. Further, the models with high accuracy have been embedded in a mobile application to provide a real-time disease detection facility for end users within a few seconds.
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Affiliation(s)
- Faiza Khan
- University Institute of Information Technology, Pir Meh Ali Shah (PMAS)-Arid Agriculture University, Rawalpindi, Pakistan
- Data Driven Smart Decision Platform for Increased Agriculture Productivity, Pir Meh Ali Shah (PMAS)-Arid Agriculture University, Rawalpindi, Pakistan
| | - Noureen Zafar
- University Institute of Information Technology, Pir Meh Ali Shah (PMAS)-Arid Agriculture University, Rawalpindi, Pakistan
- Data Driven Smart Decision Platform for Increased Agriculture Productivity, Pir Meh Ali Shah (PMAS)-Arid Agriculture University, Rawalpindi, Pakistan
| | - Muhammad Naveed Tahir
- Data Driven Smart Decision Platform for Increased Agriculture Productivity, Pir Meh Ali Shah (PMAS)-Arid Agriculture University, Rawalpindi, Pakistan
- Department of Agronomy, Pir Meh Ali Shah (PMAS)-Arid Agriculture University, Rawalpindi, Pakistan
| | - Muhammad Aqib
- University Institute of Information Technology, Pir Meh Ali Shah (PMAS)-Arid Agriculture University, Rawalpindi, Pakistan
- National Center of Industrial Biotechnology, Pir Meh Ali Shah (PMAS)-Arid Agriculture University, Rawalpindi, Pakistan
| | - Hamna Waheed
- University Institute of Information Technology, Pir Meh Ali Shah (PMAS)-Arid Agriculture University, Rawalpindi, Pakistan
| | - Zainab Haroon
- Data Driven Smart Decision Platform for Increased Agriculture Productivity, Pir Meh Ali Shah (PMAS)-Arid Agriculture University, Rawalpindi, Pakistan
- Department of Land and Water Conservation Engineering, Faculty of Agricultural Engineering and Technology, Pir Meh Ali Shah (PMAS)-Arid Agriculture University, Rawalpindi, Pakistan
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28
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Khalid A, Senan EM, Al-Wagih K, Al-Azzam MMA, Alkhr aisha ZM. Automatic Analysis of MRI Images for Early Prediction of Alzheimer's Disease Stages Based on Hybrid Features of CNN and Handcrafted Features. Diagnostics (Basel) 2023; 13:diagnostics13091654. [PMID: 37175045 PMCID: PMC10178535 DOI: 10.3390/diagnostics13091654] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 05/02/2023] [Accepted: 05/05/2023] [Indexed: 05/15/2023] Open
Abstract
Alzheimer's disease (AD) is considered one of the challenges facing health care in the modern century; until now, there has been no effective treatment to cure it, but there are drugs to slow its progression. Therefore, early detection of Alzheimer's is vital to take needful measures before it develops into brain damage which cannot be treated. Magnetic resonance imaging (MRI) techniques have contributed to the diagnosis and prediction of its progression. MRI images require highly experienced doctors and radiologists, and the analysis of MRI images takes time to analyze each slice. Thus, deep learning techniques play a vital role in analyzing a huge amount of MRI images with high accuracy to detect Alzheimer's and predict its progression. Because of the similarities in the characteristics of the early stages of Alzheimer's, this study aimed to extract the features in several methods and integrate the features extracted from more than one method into the same features matrix. This study contributed to the development of three methodologies, each with two systems, with all systems aimed at achieving satisfactory accuracy for the detection of AD and predicting the stages of its progression. The first methodology is by Feed Forward Neural Network (FFNN) with the features of GoogLeNet and DenseNet-121 models separately. The second methodology is by FFNN network with combined features between GoogLeNet and Dense-121 models before and after high-dimensionality reduction of features using the Principal Component Analysis (PCA) algorithm. The third methodology is by FFNN network with combined features between GoogLeNet and Dense-121 models separately and features extracted by Discrete Wavelet Transform (DWT), Local Binary Pattern (LBP) and Gray Level Co-occurrence Matrix (GLCM) methods called handcrafted features. All systems yielded super results in detecting AD and predicting the stages of its progression. With the combined features of the DenseNet-121 and handcrafted, the FFNN achieved an accuracy of 99.7%, sensitivity of 99.64%, AUC of 99.56%, precision of 99.63%, and a specificity of 99.67%.
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Affiliation(s)
- Ahmed Khalid
- Computer Department, Applied College, Najran University, Najran 66462, Saudi Arabia
| | - Ebrahim Mohammed Senan
- Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, Alrazi University, Sana'a, Yemen
| | - Khalil Al-Wagih
- Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, Alrazi University, Sana'a, Yemen
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29
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Wei Z, Dong S, Wang X. Petrochemical Equipment Tracking by Improved Yolov7 Network and Hybrid Matching in Moving Scenes. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23094546. [PMID: 37177751 PMCID: PMC10181657 DOI: 10.3390/s23094546] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Revised: 04/22/2023] [Accepted: 05/02/2023] [Indexed: 05/15/2023]
Abstract
Petrochemical equipment tracking is a fundamental and important technology in petrochemical industry security monitoring, equipment working risk analysis, and other applications. In complex scenes where the multiple pipelines present different directions and many kinds of equipment have huge scale and shape variation in seriously mutual occlusions captured by moving cameras, the accuracy and speed of petrochemical equipment tracking would be limited because of the false and missed tracking of equipment with extreme sizes and severe occlusion, due to image quality, equipment scale, light, and other factors. In this paper, a new multiple petrochemical equipment tracking method is proposed by combining an improved Yolov7 network with attention mechanism and small target perceive layer and a hybrid matching that incorporates deep feature and traditional texture and location feature. The model incorporates the advantages of channel and spatial attention module into the improved Yolov7 detector and Siamese neural network for similarity matching. The proposed model is validated on the self-built petrochemical equipment video data set and the experimental results show it achieves a competitive performance in comparison with the related state-of-the-art tracking algorithms.
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Affiliation(s)
- Zhenqiang Wei
- College of Safety and Ocean Engineering, China University of Petroleum, Beijing 102249, China
- CNPC Research Institute of Safety & Environment Technology, Beijing 102206, China
| | - Shaohua Dong
- College of Safety and Ocean Engineering, China University of Petroleum, Beijing 102249, China
| | - Xuchu Wang
- Key Laboratory of Optoelectronic Technology and Systems of Ministry of Education, Chongqing University, Chongqing 400040, China
- College of Optoelectronic Engineering, Chongqing University, Chongqing 400040, China
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Sun X, Cao J, Sun P, Yang H, Li H, Ma W, Wu X, He X, Li J, Li Z, Huang J. Pretreatment soluble Siglec-5 protein predicts early progression and R-CHOP efficacy in diffuse large B-cell lymphoma. Biomark Med 2023; 17:143-158. [PMID: 37097021 DOI: 10.2217/bmm-2022-0764] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/26/2023] Open
Abstract
Aims: To explore the clinical association between soluble Siglec-5/CD163 and clinical feature and prognosis in peripheral blood samples of patients with diffuse large B-cell lymphoma. Method: Significantly elevated cytokines in peripheral blood were characterized by cytokines array and validated by ELISA. Results: Compared with CD163, Siglec-5 exhibited superiority in discriminating patients into low- and high-risk subgroups based on overall survival and progression-free survival. In addition, Siglec-5 was an indicator of rituximab plus cyclophosphamide, doxorubicin, vincristine and prednisone (R-CHOP) treatment efficacy. Conclusion: Siglec-5 may be applied as a reliable independent immune indicator for overall survival and progression-free survival. It may also predict R-CHOP efficacy in diffuse large B-cell lymphoma.
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Affiliation(s)
- Xiaoqing Sun
- Department of Intensive Care Unit(ICU), State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Guangzhou, 510060, PR China
| | - Jianghua Cao
- Department of Medical Oncology, State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Guangzhou, 510060, PR China
| | - Peng Sun
- Department of Medical Oncology, State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Guangzhou, 510060, PR China
| | - Hang Yang
- Department of Medical Oncology, State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Guangzhou, 510060, PR China
| | - Huan Li
- Department of Intensive Care Unit(ICU), State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Guangzhou, 510060, PR China
| | - Wenjuan Ma
- Department of Intensive Care Unit(ICU), State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Guangzhou, 510060, PR China
| | - Xianqiu Wu
- Department of Hepatobiliary Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
| | - Xiaohua He
- Department of Medical Oncology, State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Guangzhou, 510060, PR China
| | - Jing Li
- Department of Medical Oncology, State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Guangzhou, 510060, PR China
| | - Zhiming Li
- Department of Medical Oncology, State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Guangzhou, 510060, PR China
| | - Jiajia Huang
- Department of Medical Oncology, State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Guangzhou, 510060, PR China
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31
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Visheratina A, Visheratin A, Kumar P, Veksler M, Kotov NA. Chirality Analysis of Complex Microparticles using Deep Learning on Realistic Sets of Microscopy Images. ACS NANO 2023; 17:7431-7442. [PMID: 37058327 DOI: 10.1021/acsnano.2c12056] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Nanoscale chirality is an actively growing research field spurred by the giant chiroptical activity, enantioselective biological activity, and asymmetric catalytic activity of chiral nanostructures. Compared to chiral molecules, the handedness of chiral nano- and microstructures can be directly established via electron microscopy, which can be utilized for the automatic analysis of chiral nanostructures and prediction of their properties. However, chirality in complex materials may have multiple geometric forms and scales. Computational identification of chirality from electron microscopy images rather than optical measurements is convenient but is fundamentally challenging, too, because (1) image features differentiating left- and right-handed particles can be ambiguous and (2) three-dimensional structure essential for chirality is 'flattened' into two-dimensional projections. Here, we show that deep learning algorithms can identify twisted bowtie-shaped microparticles with nearly 100% accuracy and classify them as left- and right-handed with as high as 99% accuracy. Importantly, such accuracy was achieved with as few as 30 original electron microscopy images of bowties. Furthermore, after training on bowtie particles with complex nanostructured features, the model can recognize other chiral shapes with different geometries without retraining for their specific chiral geometry with 93% accuracy, indicating the true learning abilities of the employed neural networks. These findings indicate that our algorithm trained on a practically feasible set of experimental data enables automated analysis of microscopy data for the accelerated discovery of chiral particles and their complex systems for multiple applications.
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Affiliation(s)
- Anastasia Visheratina
- Department of Chemical Engineering and Biointerfaces Institute, University of Michigan, Ann Arbor, Michigan 48109, United States
| | | | - Prashant Kumar
- Department of Chemical Engineering and Biointerfaces Institute, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Michael Veksler
- Department of Chemical Engineering and Biointerfaces Institute, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Nicholas A Kotov
- Department of Chemical Engineering and Biointerfaces Institute, University of Michigan, Ann Arbor, Michigan 48109, United States
- Department of Aeronautics, Faculty of Engineering, Imperial College London, South Kensington Campus London, SW7 2AZ, United Kingdom
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32
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Nazir S, Kaleem M. Federated Learning for Medical Image Analysis with Deep Neural Networks. Diagnostics (Basel) 2023; 13:diagnostics13091532. [PMID: 37174925 PMCID: PMC10177193 DOI: 10.3390/diagnostics13091532] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2023] [Revised: 04/14/2023] [Accepted: 04/21/2023] [Indexed: 05/15/2023] Open
Abstract
Medical image analysis using deep neural networks (DNN) has demonstrated state-of-the-art performance in image classification and segmentation tasks, aiding disease diagnosis. The accuracy of the DNN is largely governed by the quality and quantity of the data used to train the model. However, for the medical images, the critical security and privacy concerns regarding sharing of local medical data across medical establishments precludes exploiting the full DNN potential for clinical diagnosis. The federated learning (FL) approach enables the use of local model's parameters to train a global model, while ensuring data privacy and security. In this paper, we review the federated learning applications in medical image analysis with DNNs, highlight the security concerns, cover some efforts to improve FL model performance, and describe the challenges and future research directions.
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Affiliation(s)
- Sajid Nazir
- Department of Computing, Glasgow Caledonian University, Glasgow G4 0BA, UK
| | - Mohammad Kaleem
- Department of Electrical and Computer Engineering, COMSATS University Islamabad, Islamabad 45550, Pakistan
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33
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Abu Al-Haija Q, Al-Fayoumi M. An intelligent identification and classification system for malicious uniform resource locators (URLs). Neural Comput Appl 2023; 35:1-17. [PMID: 37362563 PMCID: PMC10117275 DOI: 10.1007/s00521-023-08592-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Accepted: 04/05/2023] [Indexed: 06/28/2023]
Abstract
Uniform Resource Locator (URL) is a unique identifier composed of protocol and domain name used to locate and retrieve a resource on the Internet. Like any Internet service, URLs (also called websites) are vulnerable to compromise by attackers to develop Malicious URLs that can exploit/devastate the user's information and resources. Malicious URLs are usually designed with the intention of promoting cyber-attacks such as spam, phishing, malware, and defacement. These websites usually require action on the user's side and can reach users across emails, text messages, pop-ups, or devious advertisements. They have a potential impact that can reach, in some cases, to compromise the machine or network of the user, especially those arriving by email. Therefore, developing systems to detect malicious URLs is of great interest nowadays. This paper proposes a high-performance machine learning-based detection system to identify Malicious URLs. The proposed system provides two layers of detection. Firstly, we identify the URLs as either benign or malware using a binary classifier. Secondly, we classify the URL classes based on their feature into five classes: benign, spam, phishing, malware, and defacement. Specifically, we report on four ensemble learning approaches, viz. the ensemble of bagging trees (En_Bag) approach, the ensemble of k-nearest neighbor (En_kNN) approach, and the ensemble of boosted decision trees (En_Bos) approach, and the ensemble of subspace discriminator (En_Dsc) approach. The developed approaches have been evaluated on an inclusive and contemporary dataset for uniform resource locators (ISCX-URL2016). ISCX-URL2016 provides a lightweight dataset for detecting and categorizing malicious URLs according to their attack type and lexical analysis. Conventional machine learning evaluation measurements are used to evaluate the detection accuracy, precision, recall, F Score, and detection time. Our experiential assessment indicates that the ensemble of bagging trees (En_Bag) approach provides better performance rates than other ensemble methods. Alternatively, the ensemble of the k-nearest neighbor (En_kNN) approach provides the highest inference speed. We also contrast our En_Bag model with state-of-the-art solutions and show its superiority in binary classification and multi-classification with accuracy rates of 99.3% and 97.92%, respectively.
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Affiliation(s)
- Qasem Abu Al-Haija
- Department of Cybersecurity, Princess Sumaya University for Technology (PSUT), Amman, Jordan
| | - Mustafa Al-Fayoumi
- Department of Cybersecurity, Princess Sumaya University for Technology (PSUT), Amman, Jordan
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34
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Murugappan M, Bourisly AK, Prakash NB, Sumithra MG, Acharya UR. Automated semantic lung segmentation in chest CT images using deep neural network. Neural Comput Appl 2023; 35:15343-15364. [PMID: 37273912 PMCID: PMC10088735 DOI: 10.1007/s00521-023-08407-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Accepted: 02/13/2023] [Indexed: 06/06/2023]
Abstract
Lung segmentation algorithms play a significant role in segmenting theinfected regions in the lungs. This work aims to develop a computationally efficient and robust deep learning model for lung segmentation using chest computed tomography (CT) images with DeepLabV3 + networks for two-class (background and lung field) and four-class (ground-glass opacities, background, consolidation, and lung field). In this work, we investigate the performance of the DeepLabV3 + network with five pretrained networks: Xception, ResNet-18, Inception-ResNet-v2, MobileNet-v2 and ResNet-50. A publicly available database for COVID-19 that contains 750 chest CT images and corresponding pixel-labeled images are used to develop the deep learning model. The segmentation performance has been assessed using five performance measures: Intersection of Union (IoU), Weighted IoU, Balance F1 score, pixel accu-racy, and global accuracy. The experimental results of this work confirm that the DeepLabV3 + network with ResNet-18 and a batch size of 8 have a higher performance for two-class segmentation. DeepLabV3 + network coupled with ResNet-50 and a batch size of 16 yielded better results for four-class segmentation compared to other pretrained networks. Besides, the ResNet with a fewer number of layers is highly adequate for developing a more robust lung segmentation network with lesser computational complexity compared to the conventional DeepLabV3 + network with Xception. This present work proposes a unified DeepLabV3 + network to delineate the two and four different regions automatically using CT images for CoVID-19 patients. Our developed automated segmented model can be further developed to be used as a clinical diagnosis system for CoVID-19 as well as assist clinicians in providing an accurate second opinion CoVID-19 diagnosis.
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Affiliation(s)
- M. Murugappan
- Intelligent Signal Processing (ISP) Research Lab, Department of Electronics and Communication Engineering, Kuwait College of Science and Technology, Block 4, Doha, Kuwait
- Department of Electronics and Communication Engineering, School of Engineering, Vels Institute of Science, Technology, and Advanced Studies, Chennai, India
- Centre of Excellence for Unmanned Aerial Systems (CoEUAS), Universiti Malaysia Perlis, 02600 Perlis, Malaysia
| | - Ali K. Bourisly
- Department of Physiology, Kuwait University, Kuwait City, Kuwait
| | - N. B. Prakash
- Department of Electrical and Electronics and Engineering, National Engineering College, Kovilpatti, Tamil Nadu India
| | - M. G. Sumithra
- Department of Biomedical Engineering, Dr. N. G. P. Institute of Technology, Coimbatore, Tamilnadu India
| | - U. Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore, Singapore
- Department of Biomedical Engineering, School of Science and Technology, Singapore School of Social Sciences, Singapore, Singapore
- Department of Bioinformatics and Medical Engineering, Asia University, Taichung, Taiwan
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35
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Tahir M, Naeem A, Malik H, Tanveer J, Naqvi RA, Lee SW. DSCC_Net: Multi-Classification Deep Learning Models for Diagnosing of Skin Cancer Using Dermoscopic Images. Cancers (Basel) 2023; 15:cancers15072179. [PMID: 37046840 PMCID: PMC10093058 DOI: 10.3390/cancers15072179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 04/04/2023] [Accepted: 04/04/2023] [Indexed: 04/08/2023] Open
Abstract
Skin cancer is one of the most lethal kinds of human illness. In the present state of the health care system, skin cancer identification is a time-consuming procedure and if it is not diagnosed initially then it can be threatening to human life. To attain a high prospect of complete recovery, early detection of skin cancer is crucial. In the last several years, the application of deep learning (DL) algorithms for the detection of skin cancer has grown in popularity. Based on a DL model, this work intended to build a multi-classification technique for diagnosing skin cancers such as melanoma (MEL), basal cell carcinoma (BCC), squamous cell carcinoma (SCC), and melanocytic nevi (MN). In this paper, we have proposed a novel model, a deep learning-based skin cancer classification network (DSCC_Net) that is based on a convolutional neural network (CNN), and evaluated it on three publicly available benchmark datasets (i.e., ISIC 2020, HAM10000, and DermIS). For the skin cancer diagnosis, the classification performance of the proposed DSCC_Net model is compared with six baseline deep networks, including ResNet-152, Vgg-16, Vgg-19, Inception-V3, EfficientNet-B0, and MobileNet. In addition, we used SMOTE Tomek to handle the minority classes issue that exists in this dataset. The proposed DSCC_Net obtained a 99.43% AUC, along with a 94.17%, accuracy, a recall of 93.76%, a precision of 94.28%, and an F1-score of 93.93% in categorizing the four distinct types of skin cancer diseases. The rates of accuracy for ResNet-152, Vgg-19, MobileNet, Vgg-16, EfficientNet-B0, and Inception-V3 are 89.32%, 91.68%, 92.51%, 91.12%, 89.46% and 91.82%, respectively. The results showed that our proposed DSCC_Net model performs better as compared to baseline models, thus offering significant support to dermatologists and health experts to diagnose skin cancer.
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Affiliation(s)
- Maryam Tahir
- Department of Computer Science, National College of Business Administration & Economics Lahore, Multan Sub Campus, Multan 60000, Pakistan
| | - Ahmad Naeem
- Department of Computer Science, University of Management and Technology, Lahore 54000, Pakistan
| | - Hassaan Malik
- Department of Computer Science, National College of Business Administration & Economics Lahore, Multan Sub Campus, Multan 60000, Pakistan
- Department of Computer Science, University of Management and Technology, Lahore 54000, Pakistan
| | - Jawad Tanveer
- Department of Computer Science and Engineering, Sejong University, Seoul 05006, Republic of Korea
| | - Rizwan Ali Naqvi
- Department of Intelligent Mechatronics Engineering, Sejong University, Seoul 05006, Republic of Korea
| | - Seung-Won Lee
- School of Medicine, Sungkyunkwan University, Suwon 16419, Republic of Korea
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36
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Chow JCL, Sanders L, Li K. Impact of ChatGPT on medical chatbots as a disruptive technology. Front Artif Intell 2023; 6:1166014. [PMID: 37091303 PMCID: PMC10113434 DOI: 10.3389/frai.2023.1166014] [Citation(s) in RCA: 36] [Impact Index Per Article: 36.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Accepted: 03/23/2023] [Indexed: 04/09/2023] Open
Affiliation(s)
- James C. L. Chow
- Radiation Medicine Program, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada
- *Correspondence: James C. L. Chow
| | - Leslie Sanders
- Department of Humanities, York University, Toronto, ON, Canada
| | - Kay Li
- Department of English, University of Toronto, Toronto, ON, Canada
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37
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Khattab R, Abdelmaksoud IR, Abdelrazek S. Deep Convolutional Neural Networks for Detecting COVID-19 Using Medical Images: A Survey. NEW GENERATION COMPUTING 2023; 41:343-400. [PMID: 37229176 PMCID: PMC10071474 DOI: 10.1007/s00354-023-00213-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Accepted: 02/23/2023] [Indexed: 05/27/2023]
Abstract
Coronavirus Disease 2019 (COVID-19), which is caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2), surprised the world in December 2019 and has threatened the lives of millions of people. Countries all over the world closed worship places and shops, prevented gatherings, and implemented curfews to stand against the spread of COVID-19. Deep Learning (DL) and Artificial Intelligence (AI) can have a great role in detecting and fighting this disease. Deep learning can be used to detect COVID-19 symptoms and signs from different imaging modalities, such as X-Ray, Computed Tomography (CT), and Ultrasound Images (US). This could help in identifying COVID-19 cases as a first step to curing them. In this paper, we reviewed the research studies conducted from January 2020 to September 2022 about deep learning models that were used in COVID-19 detection. This paper clarified the three most common imaging modalities (X-Ray, CT, and US) in addition to the DL approaches that are used in this detection and compared these approaches. This paper also provided the future directions of this field to fight COVID-19 disease.
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Affiliation(s)
- Rana Khattab
- Information Systems Department, Faculty of Computers and Information, Mansoura University, Mansoura, Egypt
| | - Islam R. Abdelmaksoud
- Information Systems Department, Faculty of Computers and Information, Mansoura University, Mansoura, Egypt
| | - Samir Abdelrazek
- Information Systems Department, Faculty of Computers and Information, Mansoura University, Mansoura, Egypt
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Wali A, Ahmad M, Naseer A, Tamoor M, Gilani S. StynMedGAN: Medical images augmentation using a new GAN model for improved diagnosis of diseases. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023. [DOI: 10.3233/jifs-223996] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/07/2023]
Abstract
Deep networks require a considerable amount of training data otherwise these networks generalize poorly. Data Augmentation techniques help the network generalize better by providing more variety in the training data. Standard data augmentation techniques such as flipping, and scaling, produce new data that is a modified version of the original data. Generative Adversarial networks (GANs) have been designed to generate new data that can be exploited. In this paper, we propose a new GAN model, named StynMedGAN for synthetically generating medical images to improve the performance of classification models. StynMedGAN builds upon the state-of-the-art styleGANv2 that has produced remarkable results generating all kinds of natural images. We introduce a regularization term that is a normalized loss factor in the existing discriminator loss of styleGANv2. It is used to force the generator to produce normalized images and penalize it if it fails. Medical imaging modalities, such as X-Rays, CT-Scans, and MRIs are different in nature, we show that the proposed GAN extends the capacity of styleGANv2 to handle medical images in a better way. This new GAN model (StynMedGAN) is applied to three types of medical imaging: X-Rays, CT scans, and MRI to produce more data for the classification tasks. To validate the effectiveness of the proposed model for the classification, 3 classifiers (CNN, DenseNet121, and VGG-16) are used. Results show that the classifiers trained with StynMedGAN-augmented data outperform other methods that only used the original data. The proposed model achieved 100%, 99.6%, and 100% for chest X-Ray, Chest CT-Scans, and Brain MRI respectively. The results are promising and favor a potentially important resource that can be used by practitioners and radiologists to diagnose different diseases.
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Affiliation(s)
- Aamir Wali
- Department of Computer Science, National University of Computer and Emerging Science, Faisal Town, Lahore, Pakistan
| | - Muzammil Ahmad
- Department of Computer Science, National University of Computer and Emerging Science, Faisal Town, Lahore, Pakistan
| | - Asma Naseer
- Department of Computer Science, National University of Computer and Emerging Science, Faisal Town, Lahore, Pakistan
| | - Maria Tamoor
- Department of Computer Science, Forman Christian College University, Zahoor Ilahi Road, Lahore, Pakistan
| | - S.A.M. Gilani
- Department of Computer Science, National University of Computer and Emerging Science, Faisal Town, Lahore, Pakistan
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Olayah F, Senan EM, Ahmed IA, Awaji B. AI Techniques of Dermoscopy Image Analysis for the Early Detection of Skin Lesions Based on Combined CNN Features. Diagnostics (Basel) 2023; 13:diagnostics13071314. [PMID: 37046532 PMCID: PMC10093624 DOI: 10.3390/diagnostics13071314] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Revised: 03/23/2023] [Accepted: 03/29/2023] [Indexed: 04/05/2023] Open
Abstract
Melanoma is one of the deadliest types of skin cancer that leads to death if not diagnosed early. Many skin lesions are similar in the early stages, which causes an inaccurate diagnosis. Accurate diagnosis of the types of skin lesions helps dermatologists save patients’ lives. In this paper, we propose hybrid systems based on the advantages of fused CNN models. CNN models receive dermoscopy images of the ISIC 2019 dataset after segmenting the area of lesions and isolating them from healthy skin through the Geometric Active Contour (GAC) algorithm. Artificial neural network (ANN) and Random Forest (Rf) receive fused CNN features and classify them with high accuracy. The first methodology involved analyzing the area of skin lesions and diagnosing their type early using the hybrid models CNN-ANN and CNN-RF. CNN models (AlexNet, GoogLeNet and VGG16) receive lesions area only and produce high depth feature maps. Thus, the deep feature maps were reduced by the PCA and then classified by ANN and RF networks. The second methodology involved analyzing the area of skin lesions and diagnosing their type early using the hybrid CNN-ANN and CNN-RF models based on the features of the fused CNN models. It is worth noting that the features of the CNN models were serially integrated after reducing their high dimensions by Principal Component Analysis (PCA). Hybrid models based on fused CNN features achieved promising results for diagnosing dermatoscopic images of the ISIC 2019 data set and distinguishing skin cancer from other skin lesions. The AlexNet-GoogLeNet-VGG16-ANN hybrid model achieved an AUC of 94.41%, sensitivity of 88.90%, accuracy of 96.10%, precision of 88.69%, and specificity of 99.44%.
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Affiliation(s)
- Fekry Olayah
- Department of Information System, Faculty Computer Science and Information System, Najran University, Najran 66462, Saudi Arabia
| | - Ebrahim Mohammed Senan
- Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, Alrazi University, Sana’a, Yemen
| | | | - Bakri Awaji
- Department of Computer Science, Faculty of Computer Science and Information System, Najran University, Najran 66462, Saudi Arabia
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Taha A, Barakat B, Taha MMA, Shawky MA, L ai CS, Hussain S, Abideen MZ, Abbasi QH. A Comparative Study of Single and Multi-Stage Forecasting Algorithms for the Prediction of Electricity Consumption Using a UK-National Health Service (NHS) Hospital Dataset. FUTURE INTERNET 2023. [DOI: 10.3390/fi15040134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/03/2023] Open
Abstract
Accurately looking into the future was a significantly major challenge prior to the era of big data, but with rapid advancements in the Internet of Things (IoT), Artificial Intelligence (AI), and the data availability around us, this has become relatively easier. Nevertheless, in order to ensure high-accuracy forecasting, it is crucial to consider suitable algorithms and the impact of the extracted features. This paper presents a framework to evaluate a total of nine forecasting algorithms categorised into single and multistage models, constructed from the Prophet, Support Vector Regression (SVR), Long Short-Term Memory (LSTM), and the Least Absolute Shrinkage and Selection Operator (LASSO) approaches, applied to an electricity demand dataset from an NHS hospital. The aim is to see such techniques widely used in accurately predicting energy consumption, limiting the negative impacts of future waste on energy, and making a contribution towards the 2050 net zero carbon target. The proposed method accounts for patterns in demand and temperature to accurately forecast consumption. The Coefficient of Determination (R2), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) were used to evaluate the algorithms’ performance. The results show the superiority of the Long Short-Term Memory (LSTM) model and the multistage Facebook Prophet model, with R2 values of 87.20% and 68.06%, respectively.
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Challenges, opportunities, and advances related to COVID-19 classification based on deep learning. DATA SCIENCE AND MANAGEMENT 2023. [PMCID: PMC10063459 DOI: 10.1016/j.dsm.2023.03.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/03/2023]
Abstract
The novel coronavirus disease, or COVID-19, is a hazardous disease. It is endangering the lives of many people living in more than two hundred countries. It directly affects the lungs. In general, two main imaging modalities: - computed tomography (CT) and chest x-ray (CXR) are used to achieve a speedy and reliable medical diagnosis. Identifying the coronavirus in medical images is exceedingly difficult for diagnosis, assessment, and treatment. It is demanding, time-consuming, and subject to human mistakes. In biological disciplines, excellent performance can be achieved by employing artificial intelligence (AI) models. As a subfield of AI, deep learning (DL) networks have drawn considerable attention than standard machine learning (ML) methods. DL models automatically carry out all the steps of feature extraction, feature selection, and classification. This study has performed comprehensive analysis of coronavirus classification using CXR and CT imaging modalities using DL architectures. Additionally, we have discussed how transfer learning is helpful in this regard. Finally, the problem of designing and implementing a system using computer-aided diagnostic (CAD) to find COVID-19 using DL approaches is highlighted a future research possibility.
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Aria R, Archer N, Khanlari M, Shah B. Influential Factors in the Design and Development of a Sust ainable Web3/Metaverse and Its Applications. FUTURE INTERNET 2023. [DOI: 10.3390/fi15040131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/01/2023] Open
Abstract
This paper summarizes the work of many different authors, industries, and countries by introducing important and influential factors that will help in the development, successful adoption, and sustainable use of the Web3/metaverse and its applications. We introduce a few important factors derived from the current state-of-the-art literature, including four essential elements including (1) appropriate decentralization, (2) good user experience, (3) appropriate translation and synchronization to the real world, and (4) a viable economy, which are required for appropriate implementation of a metaverse and its applications. The future of Web3 is all about decentralization, and blockchain can play a significant part in the development of the Metaverse. This paper also sheds light on some of the most relevant open issues and challenges currently facing the Web3/metaverse and its applications, with the hope that this discourse will help to encourage the development of appropriate solutions.
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Colaco SJ, Kim JH, Poulose A, Neethirajan S, Han DS. DISubNet: Depthwise Separable Inception Subnetwork for Pig Treatment Classification Using Thermal Data. Animals (Basel) 2023; 13:ani13071184. [PMID: 37048439 PMCID: PMC10093577 DOI: 10.3390/ani13071184] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Revised: 03/19/2023] [Accepted: 03/23/2023] [Indexed: 03/31/2023] Open
Abstract
Thermal imaging is increasingly used in poultry, swine, and dairy animal husbandry to detect disease and distress. In intensive pig production systems, early detection of health and welfare issues is crucial for timely intervention. Using thermal imaging for pig treatment classification can improve animal welfare and promote sustainable pig production. In this paper, we present a depthwise separable inception subnetwork (DISubNet), a lightweight model for classifying four pig treatments. Based on the modified model architecture, we propose two DISubNet versions: DISubNetV1 and DISubNetV2. Our proposed models are compared to other deep learning models commonly employed for image classification. The thermal dataset captured by a forward-looking infrared (FLIR) camera is used to train these models. The experimental results demonstrate that the proposed models for thermal images of various pig treatments outperform other models. In addition, both proposed models achieve approximately 99.96–99.98% classification accuracy with fewer parameters.
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Affiliation(s)
- Savina Jassica Colaco
- School of Electronic and Electrical Engineering, Kyungpook National University, Daegu 41566, Republic of Korea; (S.J.C.); (J.H.K.)
| | - Jung Hwan Kim
- School of Electronic and Electrical Engineering, Kyungpook National University, Daegu 41566, Republic of Korea; (S.J.C.); (J.H.K.)
| | - Alwin Poulose
- School of Data Science, Indian Institute of Science Education and Research (IISER), Thiruvananthapuram 695551, India;
| | | | - Dong Seog Han
- School of Electronic and Electrical Engineering, Kyungpook National University, Daegu 41566, Republic of Korea; (S.J.C.); (J.H.K.)
- Correspondence: ; Tel.: +82-53-950-6609
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Almuayqil S, Abd El-Ghany S, Shehab A. Multimodality Imaging of COVID-19 Using Fine-Tuned Deep Learning Models. Diagnostics (Basel) 2023; 13:diagnostics13071268. [PMID: 37046486 PMCID: PMC10093688 DOI: 10.3390/diagnostics13071268] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 03/22/2023] [Accepted: 03/23/2023] [Indexed: 03/30/2023] Open
Abstract
In the face of the COVID-19 pandemic, many studies have been undertaken to provide assistive recommendations to patients to help overcome the burden of the expected shortage in clinicians. Thus, this study focused on diagnosing the COVID-19 virus using a set of fine-tuned deep learning models to overcome the latency in virus checkups. Five recent deep learning algorithms (EfficientB0, VGG-19, DenseNet121, EfficientB7, and MobileNetV2) were utilized to label both CT scan and chest X-ray images as positive or negative for COVID-19. The experimental results showed the superiority of the proposed method compared to state-of-the-art methods in terms of precision, sensitivity, specificity, F1 score, accuracy, and data access time.
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Rangu S, Veramalla R, Salkuti SR, Kalagadda B. Efficient Approach to Color Image Segmentation Based on Multilevel Thresholding Using EMO Algorithm by Considering Spatial Contextual Information. J Imaging 2023; 9:jimaging9040074. [PMID: 37103225 PMCID: PMC10145584 DOI: 10.3390/jimaging9040074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 03/15/2023] [Accepted: 03/17/2023] [Indexed: 04/28/2023] Open
Abstract
The process of image segmentation is partitioning an image into its constituent parts and is a significant approach for extracting interesting features from images. Over a couple of decades, many efficient image segmentation approaches have been formulated for various applications. Still, it is a challenging and complex issue, especially for color image segmentation. To moderate this difficulty, a novel multilevel thresholding approach is proposed in this paper based on the electromagnetism optimization (EMO) technique with an energy curve, named multilevel thresholding based on EMO and energy curve (MTEMOE). To compute the optimized threshold values, Otsu's variance and Kapur's entropy are deployed as fitness functions; both values should be maximized to locate optimal threshold values. In both Kapur's and Otsu's methods, the pixels of an image are classified into different classes based on the threshold level selected on the histogram. Optimal threshold levels give higher efficiency of segmentation; the EMO technique is used to find optimal thresholds in this research. The methods based on an image's histograms do not possess the spatial contextual information for finding the optimal threshold levels. To abolish this deficiency an energy curve is used instead of the histogram and this curve can establish the spatial relationship of pixels with their neighbor pixels. To study the experimental results of the proposed scheme, several color benchmark images are considered at various threshold levels and compared with other meta-heuristic algorithms: multi-verse optimization, whale optimization algorithm, and so on. The investigational results are illustrated in terms of mean square error, peak signal-to-noise ratio, the mean value of fitness reach, feature similarity, structural similarity, variation of information, and probability rand index. The results reveal that the proposed MTEMOE approach overtops other state-of-the-art algorithms to solve engineering problems in various fields.
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Affiliation(s)
- Srikanth Rangu
- Department of ECE, Kakatiya Institute of Technology and Science, Warangal 506015, India
| | - Rajagopal Veramalla
- Department of ECE, Kakatiya Institute of Technology and Science, Warangal 506015, India
| | - Surender Reddy Salkuti
- Department of Railroad and Electrical Engineering, Woosong University, Daejeon 34606, Republic of Korea
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Mesías-Ruiz GA, Pérez-Ortiz M, Dorado J, de Castro AI, Peña JM. Boosting precision crop protection towards agriculture 5.0 via machine learning and emerging technologies: A contextual review. FRONTIERS IN PLANT SCIENCE 2023; 14:1143326. [PMID: 37056493 PMCID: PMC10088868 DOI: 10.3389/fpls.2023.1143326] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Accepted: 03/01/2023] [Indexed: 06/19/2023]
Abstract
Crop protection is a key activity for the sustainability and feasibility of agriculture in a current context of climate change, which is causing the destabilization of agricultural practices and an increase in the incidence of current or invasive pests, and a growing world population that requires guaranteeing the food supply chain and ensuring food security. In view of these events, this article provides a contextual review in six sections on the role of artificial intelligence (AI), machine learning (ML) and other emerging technologies to solve current and future challenges of crop protection. Over time, crop protection has progressed from a primitive agriculture 1.0 (Ag1.0) through various technological developments to reach a level of maturity closelyin line with Ag5.0 (section 1), which is characterized by successfully leveraging ML capacity and modern agricultural devices and machines that perceive, analyze and actuate following the main stages of precision crop protection (section 2). Section 3 presents a taxonomy of ML algorithms that support the development and implementation of precision crop protection, while section 4 analyses the scientific impact of ML on the basis of an extensive bibliometric study of >120 algorithms, outlining the most widely used ML and deep learning (DL) techniques currently applied in relevant case studies on the detection and control of crop diseases, weeds and plagues. Section 5 describes 39 emerging technologies in the fields of smart sensors and other advanced hardware devices, telecommunications, proximal and remote sensing, and AI-based robotics that will foreseeably lead the next generation of perception-based, decision-making and actuation systems for digitized, smart and real-time crop protection in a realistic Ag5.0. Finally, section 6 highlights the main conclusions and final remarks.
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Affiliation(s)
- Gustavo A. Mesías-Ruiz
- Institute of Agricultural Sciences (ICA), Spanish National Research Council (CSIC), Madrid, Spain
- Escuela Técnica Superior de Ingeniería Agronómica, Alimentaria y de Biosistemas (ETSIAAB), Universidad Politécnica de Madrid, Madrid, Spain
| | - María Pérez-Ortiz
- Centre for Artificial Intelligence, University College London, London, United Kingdom
| | - José Dorado
- Institute of Agricultural Sciences (ICA), Spanish National Research Council (CSIC), Madrid, Spain
| | - Ana I. de Castro
- Environment and Agronomy Department, National Institute for Agricultural and Food Research and Technology (INIA), Spanish National Research Council (CSIC), Madrid, Spain
| | - José M. Peña
- Institute of Agricultural Sciences (ICA), Spanish National Research Council (CSIC), Madrid, Spain
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Reis HC, Turk V, Khoshelham K, Kaya S. MediNet: transfer learning approach with MediNet medical visual database. MULTIMEDIA TOOLS AND APPLICATIONS 2023; 82:1-44. [PMID: 37362724 PMCID: PMC10025796 DOI: 10.1007/s11042-023-14831-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Revised: 04/06/2022] [Accepted: 02/06/2023] [Indexed: 06/28/2023]
Abstract
The rapid development of machine learning has increased interest in the use of deep learning methods in medical research. Deep learning in the medical field is used in disease detection and classification problems in the clinical decision-making process. Large amounts of labeled datasets are often required to train deep neural networks; however, in the medical field, the lack of a sufficient number of images in datasets and the difficulties encountered during data collection are among the main problems. In this study, we propose MediNet, a new 10-class visual dataset consisting of Rontgen (X-ray), Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Ultrasound, and Histopathological images such as calcaneal normal, calcaneal tumor, colon benign colon adenocarcinoma, brain normal, brain tumor, breast benign, breast malignant, chest normal, chest pneumonia. AlexNet, VGG19-BN, Inception V3, DenseNet 121, ResNet 101, EfficientNet B0, Nested-LSTM + CNN, and proposed RdiNet deep learning algorithms are used in the transfer learning for pre-training and classification application. Transfer learning aims to apply previously learned knowledge in a new task. Seven algorithms were trained with the MediNet dataset, and the models obtained from these algorithms, namely feature vectors, were recorded. Pre-training models were used for classification studies on chest X-ray images, diabetic retinopathy, and Covid-19 datasets with the transfer learning technique. In performance measurement, an accuracy of 94.84% was obtained in the traditional classification study for the InceptionV3 model in the classification study performed on the Chest X-Ray Images dataset, and the accuracy was increased 98.71% after the transfer learning technique was applied. In the Covid-19 dataset, the classification success of the DenseNet121 model before pre-trained was 88%, while the performance after the transfer application with MediNet was 92%. In the Diabetic retinopathy dataset, the classification success of the Nested-LSTM + CNN model before pre-trained was 79.35%, while the classification success was 81.52% after the transfer application with MediNet. The comparison of results obtained from experimental studies observed that the proposed method produced more successful results. Graphical abstract
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Affiliation(s)
- Hatice Catal Reis
- Department of Geomatics Engineering, Gumushane University, 2900 Gumushane, Turkey
| | - Veysel Turk
- Department of Computer Engineering, University of Harran, Sanliurfa, Turkey
| | - Kourosh Khoshelham
- Department of Infrastructure Engineering, The University of Melbourne, Parkville, 3052 Australia
| | - Serhat Kaya
- Department of Mining Engineering, Dicle University, Diyarbakir, Turkey
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Brotchie J, Li W, Greentree AD, Kealy A. RIOT: Recursive Inertial Odometry Transformer for Localisation from Low-Cost IMU Measurements. SENSORS (BASEL, SWITZERLAND) 2023; 23:3217. [PMID: 36991926 PMCID: PMC10057007 DOI: 10.3390/s23063217] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Revised: 03/10/2023] [Accepted: 03/14/2023] [Indexed: 06/19/2023]
Abstract
Inertial localisation is an important technique as it enables ego-motion estimation in conditions where external observers are unavailable. However, low-cost inertial sensors are inherently corrupted by bias and noise, which lead to unbound errors, making straight integration for position intractable. Traditional mathematical approaches are reliant on prior system knowledge, geometric theories and are constrained by predefined dynamics. Recent advances in deep learning, which benefit from ever-increasing volumes of data and computational power, allow for data-driven solutions that offer more comprehensive understanding. Existing deep inertial odometry solutions rely on estimating the latent states, such as velocity, or are dependent on fixed-sensor positions and periodic motion patterns. In this work, we propose taking the traditional state estimation recursive methodology and applying it in the deep learning domain. Our approach, which incorporates the true position priors in the training process, is trained on inertial measurements and ground truth displacement data, allowing recursion and learning both motion characteristics and systemic error bias and drift. We present two end-to-end frameworks for pose invariant deep inertial odometry that utilises self-attention to capture both spatial features and long-range dependencies in inertial data. We evaluate our approaches against a custom 2-layer Gated Recurrent Unit, trained in the same manner on the same data, and tested each approach on a number of different users, devices and activities. Each network had a sequence length weighted relative trajectory error mean ≤0.4594 m, highlighting the effectiveness of our learning process used in the development of the models.
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Affiliation(s)
- James Brotchie
- School of Science, RMIT University, Melbourne, VIC 3001, Australia
| | - Wenchao Li
- School of Science, RMIT University, Melbourne, VIC 3001, Australia
| | - Andrew D. Greentree
- ARC Centre of Excellence for Nanoscale BioPhotonics, School of Science, RMIT University, Melbourne, VIC 3001, Australia
| | - Allison Kealy
- School of Science, RMIT University, Melbourne, VIC 3001, Australia
- Victorian Department of Environment, Land, Water and Planning, Melbourne, VIC 3000, Australia
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Introducing a F air Tax Method to Harden Industrial Blockchain Applications against Network Attacks: A Game Theory Approach. COMPUTERS 2023. [DOI: 10.3390/computers12030064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2023]
Abstract
Industrial Internet of Things (IIoT) systems are enhancing the delivery of services and boosting productivity in a wide array of industries, from manufacturing to healthcare. However, IIoT devices are susceptible to cyber-threats such as the leaking of important information, products becoming compromised, and damage to industrial controls. Recently, blockchain technology has been used to increase the trust between stakeholders collaborating in the supply chain in order to preserve privacy, ensure the provenance of material, provide machine-led maintenance, etc. In all cases, such industrial blockchains establish a novel foundation of trust for business transactions which could potentially streamline and expedite economic processes to a significant extent. This paper presents an examination of “Schloss”, an industrial blockchain system architecture designed for multi-factory environments. It proposes an innovative solution to increase trust in industrial networks by incorporating a fairness concept as a subsystem of an industrial blockchain. The proposed mechanism leverages the concept of taxes imposed on blockchain nodes to enforce ethical conduct and discipline among participants. In this paper, we propose a game theory-based mechanism to address security and trust difficulties in industrial networks. The mechanism, inspired by the ultimatum game, progressively punishes malicious actors to increase the cost of fraud, improve the compensation system, and utilise the reward reporting capabilities of blockchain technology to further discourage fraudulent activities. Furthermore, the blockchain’s incentive structure is utilised to reduce collusion and speed up the process of reaching equilibrium, thereby promoting a secure and trustworthy environment for industrial collaboration. The objective of this paper is to address lack of trust among industrial partners and introduce a solution that brings security and trust to the forefront of industrial blockchain applications.
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Dai D, Dong C, Li Z, Xu S. MS-Net: Learning to assess the malignant status of a lung nodule by a radiologist and her peers. J Appl Clin Med Phys 2023:e13964. [PMID: 36929569 DOI: 10.1002/acm2.13964] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2022] [Revised: 01/04/2023] [Accepted: 02/27/2023] [Indexed: 03/18/2023] Open
Abstract
BACKGROUND Automatically assessing the malignant status of lung nodules based on CTscan images can help reduce the workload of radiologists while improving their diagnostic accuracy. PURPOSE Despite remarkable progress in the automatic diagnosis of pulmonary nodules by deep learning technologies, two significant problems remain outstanding. First, end-to-end deep learning solutions tend to neglect the empirical (semantic) features accumulated by radiologists and only rely on automatic features discovered by neural networks to provide the final diagnostic results, leading to questionable reliability, and interpretability. Second, inconsistent diagnosis between radiologists, a widely acknowledged phenomenon in clinical settings, is rarely examined and quantitatively explored by existing machine learning approaches. This paper solves these problems. METHODS We propose a novel deep neural network called MS-Net, which comprises two sequential modules: A feature derivation and initial diagnosis module (FDID), followed by a diagnosis refinement module (DR). Specifically, to take advantage of accumulated empirical features and discovered automatic features, the FDID model of MS-Net first derives a range of perceptible features and provides two initial diagnoses for lung nodules; then, these results are fed to the subsequent DR module to refine the diagnoses further. In addition, to fully consider the individual and panel diagnosis opinions, we propose a new loss function called collaborative loss, which can collaboratively optimize the individual and her peers' opinions to provide a more accurate diagnosis. RESULTS We evaluate the performance of the proposed MS-Net on the Lung Image Database Consortium image collection (LIDC-IDRI). It achieves 92.4% of accuracy, 92.9% of sensitivity, and 92.0% of specificity when panel labels are the ground truth, which is superior to other state-of-the-art diagnosis models. As a byproduct, the MS-Net can automatically derive a range of semantic features of lung nodules, increasing the interpretability of the final diagnoses. CONCLUSIONS The proposed MS-Net can provide an automatic and accurate diagnosis of lung nodules, meeting the need for a reliable computer-aided diagnosis system in clinical practice.
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Affiliation(s)
- Duwei Dai
- Institute of Medical Artificial Intelligence, The Second Affiliated Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Caixia Dong
- Institute of Medical Artificial Intelligence, The Second Affiliated Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Zongfang Li
- Institute of Medical Artificial Intelligence, The Second Affiliated Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Songhua Xu
- Institute of Medical Artificial Intelligence, The Second Affiliated Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi, China
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