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Alsulami AA, Abu Al-Haija Q, Alturki B, Alqahtani A, Binzagr F, Alghamdi B, Alsemmeari RA. Exploring the efficacy of GRU model in classifying the signal to noise ratio of microgrid model. Sci Rep 2024; 14:15591. [PMID: 38971840 PMCID: PMC11227591 DOI: 10.1038/s41598-024-66387-1] [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: 05/02/2024] [Accepted: 07/01/2024] [Indexed: 07/08/2024] Open
Abstract
Microgrids are small-scale energy system that supplies power to homes, businesses, and industries. Microgrids can be considered as a trending technology in energy fields due to their power to supply reliable and sustainable energy. Microgrids have a mode called the island, in this mode, microgrids are disconnected from the major grid and keep providing energy in the situation of an energy outage. Therefore, they help the main grid during peak energy demand times. The microgrids can be connected to the network, which is called networked microgrids. It is possible to have flexible energy resources by using their enhanced energy management systems. However, connection microgrid systems to the communication network introduces various challenges, including increased in systems complicity and noise interference. Integrating network communication into a microgrid system causes the system to be susceptible to noise, potentially disrupting the critical control signals that ensure smooth operation. Therefore, there is a need for predicting noise caused by communication network to ensure the operation stability of microgrids. In addition, there is a need for a simulation model that includes communication network and can generate noise to simulate real scenarios. This paper proposes a classifying model named Noise Classification Simulation Model (NCSM) that exploits the potential of deep learning to predict noise levels by classifying the values of signal-to-noise ratio (SNR) in real-time network traffic of microgrid system. This is accomplished by initially applying Gaussian white noise into the data that is generated by microgrid model. Then, the data has noise and data without noise is transmitted through serial communication to simulate real world scenario. At the end, a Gated Recurrent Unit (GRU) model is implemented to predict SNR values for the network traffic data. Our findings show that the proposed model produced promising results in predicting noise. In addition, the classification performance of the proposed model is compared with well-known machine learning models and according to the experimental results, our proposed model has noticeable performance, which achieved 99.96% classification accuracy.
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Affiliation(s)
- Abdulaziz A Alsulami
- Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, 21589, Jeddah, Saudi Arabia
| | - Qasem Abu Al-Haija
- Department of Cybersecurity, Faculty of Computer & Information Technology, Jordan University of Science and Technology, PO Box 3030, Irbid, 22110, Jordan.
| | - Badraddin Alturki
- Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, 21589, Jeddah, Saudi Arabia
| | - Ali Alqahtani
- Department of Networks and Communications Engineering, College of Computer Science and Information Systems, Najran University, 61441, Najran, Saudi Arabia
| | - Faisal Binzagr
- Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, P.O. Box 344, 21911, Rabigh, Saudi Arabia
| | - Bandar Alghamdi
- Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, 21589, Jeddah, Saudi Arabia
| | - Rayan A Alsemmeari
- Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, 21589, Jeddah, Saudi Arabia
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Kolhar M, Al Rajeh AM, Kazi RNA. Augmenting Radiological Diagnostics with AI for Tuberculosis and COVID-19 Disease Detection: Deep Learning Detection of Chest Radiographs. Diagnostics (Basel) 2024; 14:1334. [PMID: 39001228 PMCID: PMC11240993 DOI: 10.3390/diagnostics14131334] [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: 05/11/2024] [Revised: 06/18/2024] [Accepted: 06/21/2024] [Indexed: 07/16/2024] Open
Abstract
In this research, we introduce a network that can identify pneumonia, COVID-19, and tuberculosis using X-ray images of patients' chests. The study emphasizes tuberculosis, COVID-19, and healthy lung conditions, discussing how advanced neural networks, like VGG16 and ResNet50, can improve the detection of lung issues from images. To prepare the images for the model's input requirements, we enhanced them through data augmentation techniques for training purposes. We evaluated the model's performance by analyzing the precision, recall, and F1 scores across training, validation, and testing datasets. The results show that the ResNet50 model outperformed VGG16 with accuracy and resilience. It displayed superior ROC AUC values in both validation and test scenarios. Particularly impressive were ResNet50's precision and recall rates, nearing 0.99 for all conditions in the test set. On the hand, VGG16 also performed well during testing-detecting tuberculosis with a precision of 0.99 and a recall of 0.93. Our study highlights the performance of our deep learning method by showcasing the effectiveness of ResNet50 over traditional approaches like VGG16. This progress utilizes methods to enhance classification accuracy by augmenting data and balancing them. This positions our approach as an advancement in using state-of-the-art deep learning applications in imaging. By enhancing the accuracy and reliability of diagnosing ailments such as COVID-19 and tuberculosis, our models have the potential to transform care and treatment strategies, highlighting their role in clinical diagnostics.
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Affiliation(s)
- Manjur Kolhar
- Department Health Informatics, College of Applied Medical Sciences, King Faisal University, Al-Hofuf 31982, Saudi Arabia
| | - Ahmed M Al Rajeh
- College of Applied Medical Sciences, King Faisal University, Al-Hofuf 31982, Saudi Arabia
| | - Raisa Nazir Ahmed Kazi
- College of Applied Medical Sciences, King Faisal University, Al-Hofuf 31982, Saudi Arabia
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Naeem A, Anees T. DVFNet: A deep feature fusion-based model for the multiclassification of skin cancer utilizing dermoscopy images. PLoS One 2024; 19:e0297667. [PMID: 38507348 PMCID: PMC10954125 DOI: 10.1371/journal.pone.0297667] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Accepted: 01/11/2024] [Indexed: 03/22/2024] Open
Abstract
Skin cancer is a common cancer affecting millions of people annually. Skin cells inside the body that grow in unusual patterns are a sign of this invasive disease. The cells then spread to other organs and tissues through the lymph nodes and destroy them. Lifestyle changes and increased solar exposure contribute to the rise in the incidence of skin cancer. Early identification and staging are essential due to the high mortality rate associated with skin cancer. In this study, we presented a deep learning-based method named DVFNet for the detection of skin cancer from dermoscopy images. To detect skin cancer images are pre-processed using anisotropic diffusion methods to remove artifacts and noise which enhances the quality of images. A combination of the VGG19 architecture and the Histogram of Oriented Gradients (HOG) is used in this research for discriminative feature extraction. SMOTE Tomek is used to resolve the problem of imbalanced images in the multiple classes of the publicly available ISIC 2019 dataset. This study utilizes segmentation to pinpoint areas of significantly damaged skin cells. A feature vector map is created by combining the features of HOG and VGG19. Multiclassification is accomplished by CNN using feature vector maps. DVFNet achieves an accuracy of 98.32% on the ISIC 2019 dataset. Analysis of variance (ANOVA) statistical test is used to validate the model's accuracy. Healthcare experts utilize the DVFNet model to detect skin cancer at an early clinical stage.
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Affiliation(s)
- Ahmad Naeem
- Department of Computer Science, School of Systems and Technology, University of Management and Technology, Lahore, Pakistan
| | - Tayyaba Anees
- Department of Software Engineering, School of Systems and Technology, University of Management and Technology, Lahore, Pakistan
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Zolya MA, Baltag C, Bratu DV, Coman S, Moraru SA. COVID-19 Detection and Diagnosis Model on CT Scans Based on AI Techniques. Bioengineering (Basel) 2024; 11:79. [PMID: 38247956 PMCID: PMC10813639 DOI: 10.3390/bioengineering11010079] [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: 12/12/2023] [Revised: 12/31/2023] [Accepted: 01/08/2024] [Indexed: 01/23/2024] Open
Abstract
The end of 2019 could be mounted in a rudimentary framing of a new medical problem, which globally introduces into the discussion a fulminant outbreak of coronavirus, consequently spreading COVID-19 that conducted long-lived and persistent repercussions. Hence, the theme proposed to be solved arises from the field of medical imaging, where a pulmonary CT-based standardized reporting system could be addressed as a solution. The core of it focuses on certain impediments such as the overworking of doctors, aiming essentially to solve a classification problem using deep learning techniques, namely, if a patient suffers from COVID-19, viral pneumonia, or is healthy from a pulmonary point of view. The methodology's approach was a meticulous one, denoting an empirical character in which the initial stage, given using data processing, performs an extraction of the lung cavity from the CT scans, which is a less explored approach, followed by data augmentation. The next step is comprehended by developing a CNN in two scenarios, one in which there is a binary classification (COVID and non-COVID patients), and the other one is represented by a three-class classification. Moreover, viral pneumonia is addressed. To obtain an efficient version, architectural changes were gradually made, involving four databases during this process. Furthermore, given the availability of pre-trained models, the transfer learning technique was employed by incorporating the linear classifier from our own convolutional network into an existing model, with the result being much more promising. The experimentation encompassed several models including MobileNetV1, ResNet50, DenseNet201, VGG16, and VGG19. Through a more in-depth analysis, using the CAM technique, MobilneNetV1 differentiated itself via the detection accuracy of possible pulmonary anomalies. Interestingly, this model stood out as not being among the most used in the literature. As a result, the following values of evaluation metrics were reached: loss (0.0751), accuracy (0.9744), precision (0.9758), recall (0.9742), AUC (0.9902), and F1 score (0.9750), from 1161 samples allocated for each of the three individual classes.
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Affiliation(s)
- Maria-Alexandra Zolya
- Department of Automatics and Information Technology, Transilvania University of Brasov, 500036 Brașov, Romania; (C.B.); (D.-V.B.); (S.C.); (S.-A.M.)
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Luo G, Li Z, Ge W, Ji Z, Qiao S, Pan S. Residual networks models detection of atrial septal defect from chest radiographs. LA RADIOLOGIA MEDICA 2024; 129:48-55. [PMID: 38082195 PMCID: PMC10808252 DOI: 10.1007/s11547-023-01744-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Accepted: 10/19/2023] [Indexed: 01/25/2024]
Abstract
OBJECT The purpose of this study was to explore a machine learning-based residual networks (ResNets) model to detect atrial septal defect (ASD) on chest radiographs. METHODS This retrospective study included chest radiographs consecutively collected at our hospital from June 2017 to May 2022. Qualified chest radiographs were obtained from patients who had finished echocardiography. These chest radiographs were labeled as positive or negative for ASD based on the echocardiographic reports and were divided into training, validation, and test dataset. Six ResNets models were employed to examine and compare by using the training dataset and was tuned using the validation dataset. The area under the curve, recall, precision and F1-score were taken as the evaluation metrics for classification result in the test dataset. Visualizing regions of interest for the ResNets models using heat maps. RESULTS This study included a total of 2105 chest radiographs of children with ASD (mean age 4.14 ± 2.73 years, 54% male), patients were randomly assigned to training, validation, and test dataset with an 8:1:1 ratio. Healthy children's images were supplemented to three datasets in a 1:1 ratio with ASD patients. Following the training, ResNet-10t and ResNet-18D have a better estimation performance, with precision, recall, accuracy, F1-score, and the area under the curve being (0.92, 0.93), (0.91, 0.91), (0.90, 0.90), (0.91, 0.91) and (0.97, 0.96), respectively. Compared to ResNet-18D, ResNet-10t was more focused on the distribution of the heat map of the interest region for most chest radiographs from ASD patients. CONCLUSION The ResNets model is feasible for identifying ASD through children's chest radiographs. ResNet-10t stands out as the preferable estimation model, providing exceptional performance and clear interpretability.
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Affiliation(s)
- Gang Luo
- Heart Center, Women and Children's Hospital, Qingdao University, 6, Tongfu Road, Qingdao, 266034, China
| | - Zhixin Li
- Heart Center, Women and Children's Hospital, Qingdao University, 6, Tongfu Road, Qingdao, 266034, China
| | - Wen Ge
- Department of Radiology, Women and Children's Hospital, Qingdao University, Qingdao, 266034, China
| | - Zhixian Ji
- Heart Center, Women and Children's Hospital, Qingdao University, 6, Tongfu Road, Qingdao, 266034, China
| | - Sibo Qiao
- The School of Computer Science and Technology, China University of Petroleum, Qingdao, 266580, China
| | - Silin Pan
- Heart Center, Women and Children's Hospital, Qingdao University, 6, Tongfu Road, Qingdao, 266034, China.
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Malik H, Anees T, Al-Shamaylehs AS, Alharthi SZ, Khalil W, Akhunzada A. Deep Learning-Based Classification of Chest Diseases Using X-rays, CT Scans, and Cough Sound Images. Diagnostics (Basel) 2023; 13:2772. [PMID: 37685310 PMCID: PMC10486427 DOI: 10.3390/diagnostics13172772] [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: 07/31/2023] [Revised: 08/14/2023] [Accepted: 08/21/2023] [Indexed: 09/10/2023] Open
Abstract
Chest disease refers to a variety of lung disorders, including lung cancer (LC), COVID-19, pneumonia (PNEU), tuberculosis (TB), and numerous other respiratory disorders. The symptoms (i.e., fever, cough, sore throat, etc.) of these chest diseases are similar, which might mislead radiologists and health experts when classifying chest diseases. Chest X-rays (CXR), cough sounds, and computed tomography (CT) scans are utilized by researchers and doctors to identify chest diseases such as LC, COVID-19, PNEU, and TB. The objective of the work is to identify nine different types of chest diseases, including COVID-19, edema (EDE), LC, PNEU, pneumothorax (PNEUTH), normal, atelectasis (ATE), and consolidation lung (COL). Therefore, we designed a novel deep learning (DL)-based chest disease detection network (DCDD_Net) that uses a CXR, CT scans, and cough sound images for the identification of nine different types of chest diseases. The scalogram method is used to convert the cough sounds into an image. Before training the proposed DCDD_Net model, the borderline (BL) SMOTE is applied to balance the CXR, CT scans, and cough sound images of nine chest diseases. The proposed DCDD_Net model is trained and evaluated on 20 publicly available benchmark chest disease datasets of CXR, CT scan, and cough sound images. The classification performance of the DCDD_Net is compared with four baseline models, i.e., InceptionResNet-V2, EfficientNet-B0, DenseNet-201, and Xception, as well as state-of-the-art (SOTA) classifiers. The DCDD_Net achieved an accuracy of 96.67%, a precision of 96.82%, a recall of 95.76%, an F1-score of 95.61%, and an area under the curve (AUC) of 99.43%. The results reveal that DCDD_Net outperformed the other four baseline models in terms of many performance evaluation metrics. Thus, the proposed DCDD_Net model can provide significant assistance to radiologists and medical experts. Additionally, the proposed model was also shown to be resilient by statistical evaluations of the datasets using McNemar and ANOVA tests.
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Affiliation(s)
- Hassaan Malik
- School of Systems and Technology, University of Management and Technology, Lahore 54770, Pakistan; (H.M.); (T.A.)
| | - Tayyaba Anees
- School of Systems and Technology, University of Management and Technology, Lahore 54770, Pakistan; (H.M.); (T.A.)
| | - Ahmad Sami Al-Shamaylehs
- Department of Networks and Cybersecurity, Faculty of Information Technology, Al-Ahliyya Amman University, Amman 19328, Jordan;
| | - Salman Z. Alharthi
- Department of Information System, College of Computers and Information Systems, Al-Lith Campus, Umm AL-Qura University, P.O. Box 7745, AL-Lith 21955, Saudi Arabia
| | - Wajeeha Khalil
- Department of Computer Science and Information Technology, University of Engineering and Technology Peshawar, Peshawar 25000, Pakistan;
| | - Adnan Akhunzada
- College of Computing & IT, University of Doha for Science and Technology, Doha P.O. Box 24449, Qatar;
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Feyisa DW, Ayano YM, Debelee TG, Schwenker F. Weak Localization of Radiographic Manifestations in Pulmonary Tuberculosis from Chest X-ray: A Systematic Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:6781. [PMID: 37571564 PMCID: PMC10422452 DOI: 10.3390/s23156781] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 07/03/2023] [Accepted: 07/14/2023] [Indexed: 08/13/2023]
Abstract
Pulmonary tuberculosis (PTB) is a bacterial infection that affects the lung. PTB remains one of the infectious diseases with the highest global mortalities. Chest radiography is a technique that is often employed in the diagnosis of PTB. Radiologists identify the severity and stage of PTB by inspecting radiographic features in the patient's chest X-ray (CXR). The most common radiographic features seen on CXRs include cavitation, consolidation, masses, pleural effusion, calcification, and nodules. Identifying these CXR features will help physicians in diagnosing a patient. However, identifying these radiographic features for intricate disorders is challenging, and the accuracy depends on the radiologist's experience and level of expertise. So, researchers have proposed deep learning (DL) techniques to detect and mark areas of tuberculosis infection in CXRs. DL models have been proposed in the literature because of their inherent capacity to detect diseases and segment the manifestation regions from medical images. However, fully supervised semantic segmentation requires several pixel-by-pixel labeled images. The annotation of such a large amount of data by trained physicians has some challenges. First, the annotation requires a significant amount of time. Second, the cost of hiring trained physicians is expensive. In addition, the subjectivity of medical data poses a difficulty in having standardized annotation. As a result, there is increasing interest in weak localization techniques. Therefore, in this review, we identify methods employed in the weakly supervised segmentation and localization of radiographic manifestations of pulmonary tuberculosis from chest X-rays. First, we identify the most commonly used public chest X-ray datasets for tuberculosis identification. Following that, we discuss the approaches for weakly localizing tuberculosis radiographic manifestations in chest X-rays. The weakly supervised localization of PTB can highlight the region of the chest X-ray image that contributed the most to the DL model's classification output and help pinpoint the diseased area. Finally, we discuss the limitations and challenges of weakly supervised techniques in localizing TB manifestations regions in chest X-ray images.
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Affiliation(s)
- Degaga Wolde Feyisa
- Ethiopian Artificial Intelligence Institute, Addis Ababa P.O. Box 40782, Ethiopia; (D.W.F.); (Y.M.A.); (T.G.D.)
| | - Yehualashet Megersa Ayano
- Ethiopian Artificial Intelligence Institute, Addis Ababa P.O. Box 40782, Ethiopia; (D.W.F.); (Y.M.A.); (T.G.D.)
| | - Taye Girma Debelee
- Ethiopian Artificial Intelligence Institute, Addis Ababa P.O. Box 40782, Ethiopia; (D.W.F.); (Y.M.A.); (T.G.D.)
- Department of Electrical and Computer Engineering, Addis Ababa Science and Technology University, Addis Ababa P.O. Box 120611, Ethiopia
| | - Friedhelm Schwenker
- Institute of Neural Information Processing, Ulm University, 89069 Ulm, Germany
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Lin KH, Lu NH, Okamoto T, Huang YH, Liu KY, Matsushima A, Chang CC, Chen TB. Fusion-Extracted Features by Deep Networks for Improved COVID-19 Classification with Chest X-ray Radiography. Healthcare (Basel) 2023; 11:healthcare11101367. [PMID: 37239653 DOI: 10.3390/healthcare11101367] [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: 03/29/2023] [Revised: 05/01/2023] [Accepted: 05/08/2023] [Indexed: 05/28/2023] Open
Abstract
Convolutional neural networks (CNNs) have shown promise in accurately diagnosing coronavirus disease 2019 (COVID-19) and bacterial pneumonia using chest X-ray images. However, determining the optimal feature extraction approach is challenging. This study investigates the use of fusion-extracted features by deep networks to improve the accuracy of COVID-19 and bacterial pneumonia classification with chest X-ray radiography. A Fusion CNN method was developed using five different deep learning models after transferred learning to extract image features (Fusion CNN). The combined features were used to build a support vector machine (SVM) classifier with a RBF kernel. The performance of the model was evaluated using accuracy, Kappa values, recall rate, and precision scores. The Fusion CNN model achieved an accuracy and Kappa value of 0.994 and 0.991, with precision scores for normal, COVID-19, and bacterial groups of 0.991, 0.998, and 0.994, respectively. The results indicate that the Fusion CNN models with the SVM classifier provided reliable and accurate classification performance, with Kappa values no less than 0.990. Using a Fusion CNN approach could be a possible solution to enhance accuracy further. Therefore, the study demonstrates the potential of deep learning and fusion-extracted features for accurate COVID-19 and bacterial pneumonia classification with chest X-ray radiography.
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Affiliation(s)
- Kuo-Hsuan Lin
- Department of Information Engineering, I-Shou University, Kaohsiung City 82445, Taiwan
- Department of Emergency Medicine, E-DA Hospital, I-Shou University, Kaohsiung City 82445, Taiwan
| | - Nan-Han Lu
- Department of Pharmacy, Tajen University, Pingtung City 90741, Taiwan
- Department of Radiology, E-DA Cancer Hospital, I-Shou University, No. 1, Yida Road, Jiao-su Village, Yan-Chao District, Kaohsiung City 82445, Taiwan
- Department of Medical Imaging and Radiological Science, I-Shou University, Kaohsiung City 82445, Taiwan
| | - Takahide Okamoto
- Department of Radiological Technology, Faculty of Medical Technology, Teikyo University, Tokyo 173-8605, Japan
| | - Yung-Hui Huang
- Department of Medical Imaging and Radiological Science, I-Shou University, Kaohsiung City 82445, Taiwan
| | - Kuo-Ying Liu
- Department of Radiology, E-DA Cancer Hospital, I-Shou University, No. 1, Yida Road, Jiao-su Village, Yan-Chao District, Kaohsiung City 82445, Taiwan
- Department of Medical Imaging and Radiological Science, I-Shou University, Kaohsiung City 82445, Taiwan
| | - Akari Matsushima
- Department of Radiological Technology, Faculty of Medical Technology, Teikyo University, Tokyo 173-8605, Japan
| | - Che-Cheng Chang
- Department of Radiology, E-DA Hospital, I-Shou University, Kaohsiung City 82445, Taiwan
| | - Tai-Been Chen
- Department of Medical Imaging and Radiological Science, I-Shou University, Kaohsiung City 82445, Taiwan
- Institute of Statistics, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan
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Oh J, Park C, Lee H, Rim B, Kim Y, Hong M, Lyu J, Han S, Choi S. OView-AI Supporter for Classifying Pneumonia, Pneumothorax, Tuberculosis, Lung Cancer Chest X-ray Images Using Multi-Stage Superpixels Classification. Diagnostics (Basel) 2023; 13:diagnostics13091519. [PMID: 37174910 PMCID: PMC10177540 DOI: 10.3390/diagnostics13091519] [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: 03/30/2023] [Revised: 04/20/2023] [Accepted: 04/20/2023] [Indexed: 05/15/2023] Open
Abstract
The deep learning approach has recently attracted much attention for its outstanding performance to assist in clinical diagnostic tasks, notably in computer-aided solutions. Computer-aided solutions are being developed using chest radiography to identify lung diseases. A chest X-ray image is one of the most often utilized diagnostic imaging modalities in computer-aided solutions since it produces non-invasive standard-of-care data. However, the accurate identification of a specific illness in chest X-ray images still poses a challenge due to their high inter-class similarities and low intra-class variant abnormalities, especially given the complex nature of radiographs and the complex anatomy of the chest. In this paper, we proposed a deep-learning-based solution to classify four lung diseases (pneumonia, pneumothorax, tuberculosis, and lung cancer) and healthy lungs using chest X-ray images. In order to achieve a high performance, the EfficientNet B7 model with the pre-trained weights of ImageNet trained by Noisy Student was used as a backbone model, followed by our proposed fine-tuned layers and hyperparameters. Our study achieved an average test accuracy of 97.42%, sensitivity of 95.93%, and specificity of 99.05%. Additionally, our findings were utilized as diagnostic supporting software in OView-AI system (computer-aided application). We conducted 910 clinical trials and achieved an AUC confidence interval (95% CI) of the diagnostic results in the OView-AI system of 97.01%, sensitivity of 95.68%, and specificity of 99.34%.
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Affiliation(s)
- Joonho Oh
- Department of Mechanical System Engineering, Chosun University, Gwangju 61452, Republic of Korea
- OTOM, Co., Ltd., Gwangju 61042, Republic of Korea
| | - Chanho Park
- Department of Radiology, Soonchunhyang University Cheonan Hospital, Cheonan 31151, Republic of Korea
| | - Hongchang Lee
- Haewootech Co., Ltd., Busan 46742, Republic of Korea
| | | | - Younggyu Kim
- OTOM, Co., Ltd., Gwangju 61042, Republic of Korea
| | - Min Hong
- Department of Computer Software Engineering, Soonchunhyang University, Asan 31538, Republic of Korea
| | - Jiwon Lyu
- Division of Respiratory Medicine, Department of Internal Medicine, Soonchunhyang University Cheonan Hospital, Cheonan 31151, Republic of Korea
| | - Suha Han
- Department of Nursing, Soonchunhyang University Cheonan Hospital, Cheonan 31151, Republic of Korea
| | - Seongjun Choi
- Department of Otolaryngology-Head and Neck Surgery, Cheonan Hospital, Soonchunhyang University College of Medicine, Cheonan 31151, Republic of Korea
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Malik H, Anees T, Naeem A, Naqvi RA, Loh WK. Blockchain-Federated and Deep-Learning-Based Ensembling of Capsule Network with Incremental Extreme Learning Machines for Classification of COVID-19 Using CT Scans. Bioengineering (Basel) 2023; 10:bioengineering10020203. [PMID: 36829697 PMCID: PMC9952069 DOI: 10.3390/bioengineering10020203] [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: 01/16/2023] [Revised: 01/30/2023] [Accepted: 02/01/2023] [Indexed: 02/09/2023] Open
Abstract
Due to the rapid rate of SARS-CoV-2 dissemination, a conversant and effective strategy must be employed to isolate COVID-19. When it comes to determining the identity of COVID-19, one of the most significant obstacles that researchers must overcome is the rapid propagation of the virus, in addition to the dearth of trustworthy testing models. This problem continues to be the most difficult one for clinicians to deal with. The use of AI in image processing has made the formerly insurmountable challenge of finding COVID-19 situations more manageable. In the real world, there is a problem that has to be handled about the difficulties of sharing data between hospitals while still honoring the privacy concerns of the organizations. When training a global deep learning (DL) model, it is crucial to handle fundamental concerns such as user privacy and collaborative model development. For this study, a novel framework is designed that compiles information from five different databases (several hospitals) and edifies a global model using blockchain-based federated learning (FL). The data is validated through the use of blockchain technology (BCT), and FL trains the model on a global scale while maintaining the secrecy of the organizations. The proposed framework is divided into three parts. First, we provide a method of data normalization that can handle the diversity of data collected from five different sources using several computed tomography (CT) scanners. Second, to categorize COVID-19 patients, we ensemble the capsule network (CapsNet) with incremental extreme learning machines (IELMs). Thirdly, we provide a strategy for interactively training a global model using BCT and FL while maintaining anonymity. Extensive tests employing chest CT scans and a comparison of the classification performance of the proposed model to that of five DL algorithms for predicting COVID-19, while protecting the privacy of the data for a variety of users, were undertaken. Our findings indicate improved effectiveness in identifying COVID-19 patients and achieved an accuracy of 98.99%. Thus, our model provides substantial aid to medical practitioners in their diagnosis of COVID-19.
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Affiliation(s)
- Hassaan Malik
- Department of Computer Science, University of Management and Technology, Lahore 54000, Pakistan
| | - Tayyaba Anees
- Department of Software Engineering, University of Management and Technology, Lahore 54000, Pakistan
| | - Ahmad Naeem
- Department of Computer Science, University of Management and Technology, Lahore 54000, Pakistan
| | - Rizwan Ali Naqvi
- Department of Unmanned Vehicle Engineering, Sejong University, Seoul 05006, Republic of Korea
- Correspondence: (R.A.N.); (W.-K.L.)
| | - Woong-Kee Loh
- School of Computing, Gachon University, Seongnam 13120, Republic of Korea
- Correspondence: (R.A.N.); (W.-K.L.)
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11
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Malik H, Naeem A, Naqvi RA, Loh WK. DMFL_Net: A Federated Learning-Based Framework for the Classification of COVID-19 from Multiple Chest Diseases Using X-rays. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23020743. [PMID: 36679541 PMCID: PMC9864925 DOI: 10.3390/s23020743] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 01/02/2023] [Accepted: 01/03/2023] [Indexed: 05/14/2023]
Abstract
Coronavirus Disease 2019 (COVID-19) is still a threat to global health and safety, and it is anticipated that deep learning (DL) will be the most effective way of detecting COVID-19 and other chest diseases such as lung cancer (LC), tuberculosis (TB), pneumothorax (PneuTh), and pneumonia (Pneu). However, data sharing across hospitals is hampered by patients' right to privacy, leading to unexpected results from deep neural network (DNN) models. Federated learning (FL) is a game-changing concept since it allows clients to train models together without sharing their source data with anybody else. Few studies, however, focus on improving the model's accuracy and stability, whereas most existing FL-based COVID-19 detection techniques aim to maximize secondary objectives such as latency, energy usage, and privacy. In this work, we design a novel model named decision-making-based federated learning network (DMFL_Net) for medical diagnostic image analysis to distinguish COVID-19 from four distinct chest disorders including LC, TB, PneuTh, and Pneu. The DMFL_Net model that has been suggested gathers data from a variety of hospitals, constructs the model using the DenseNet-169, and produces accurate predictions from information that is kept secure and only released to authorized individuals. Extensive experiments were carried out with chest X-rays (CXR), and the performance of the proposed model was compared with two transfer learning (TL) models, i.e., VGG-19 and VGG-16 in terms of accuracy (ACC), precision (PRE), recall (REC), specificity (SPF), and F1-measure. Additionally, the DMFL_Net model is also compared with the default FL configurations. The proposed DMFL_Net + DenseNet-169 model achieves an accuracy of 98.45% and outperforms other approaches in classifying COVID-19 from four chest diseases and successfully protects the privacy of the data among diverse clients.
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Affiliation(s)
- Hassaan Malik
- Department of Computer Science, University of Management and Technology, Lahore 54000, Pakistan
| | - Ahmad Naeem
- Department of Computer Science, University of Management and Technology, Lahore 54000, Pakistan
| | - Rizwan Ali Naqvi
- Department of Unmanned Vehicle Engineering, Sejong University, Seoul 05006, Republic of Korea
- Correspondence: (R.A.N.); (W.-K.L.)
| | - Woong-Kee Loh
- School of Computing, Gachon University, Seongnam 13120, Republic of Korea
- Correspondence: (R.A.N.); (W.-K.L.)
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12
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Zhang HT, Sun ZY, Zhou J, Gao S, Dong JH, Liu Y, Bai X, Ma JL, Li M, Li G, Cai JM, Sheng FG. Computed tomography-based COVID-19 triage through a deep neural network using mask-weighted global average pooling. Front Cell Infect Microbiol 2023; 13:1116285. [PMID: 36936770 PMCID: PMC10020619 DOI: 10.3389/fcimb.2023.1116285] [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: 12/05/2022] [Accepted: 02/13/2023] [Indexed: 03/06/2023] Open
Abstract
Background There is an urgent need to find an effective and accurate method for triaging coronavirus disease 2019 (COVID-19) patients from millions or billions of people. Therefore, this study aimed to develop a novel deep-learning approach for COVID-19 triage based on chest computed tomography (CT) images, including normal, pneumonia, and COVID-19 cases. Methods A total of 2,809 chest CT scans (1,105 COVID-19, 854 normal, and 850 non-3COVID-19 pneumonia cases) were acquired for this study and classified into the training set (n = 2,329) and test set (n = 480). A U-net-based convolutional neural network was used for lung segmentation, and a mask-weighted global average pooling (GAP) method was proposed for the deep neural network to improve the performance of COVID-19 classification between COVID-19 and normal or common pneumonia cases. Results The results for lung segmentation reached a dice value of 96.5% on 30 independent CT scans. The performance of the mask-weighted GAP method achieved the COVID-19 triage with a sensitivity of 96.5% and specificity of 87.8% using the testing dataset. The mask-weighted GAP method demonstrated 0.9% and 2% improvements in sensitivity and specificity, respectively, compared with the normal GAP. In addition, fusion images between the CT images and the highlighted area from the deep learning model using the Grad-CAM method, indicating the lesion region detected using the deep learning method, were drawn and could also be confirmed by radiologists. Conclusions This study proposed a mask-weighted GAP-based deep learning method and obtained promising results for COVID-19 triage based on chest CT images. Furthermore, it can be considered a convenient tool to assist doctors in diagnosing COVID-19.
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Affiliation(s)
- Hong-Tao Zhang
- Department of Radiology, the Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Ze-Yu Sun
- Algorithm Center, Keya Medical Technology Co., Ltd, Shenzhen, China
| | - Juan Zhou
- Department of Radiology, the Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Shen Gao
- Department of Radiology, the Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Jing-Hui Dong
- Department of Radiology, the Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Yuan Liu
- Department of Radiology, the Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Xu Bai
- Department of Radiology, the Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Jin-Lin Ma
- Department of Radiology, the Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Ming Li
- Department of Radiology, the Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Guang Li
- Algorithm Center, Keya Medical Technology Co., Ltd, Shenzhen, China
| | - Jian-Ming Cai
- Department of Radiology, the Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
- *Correspondence: Jian-Ming Cai, ; Fu-Geng Sheng,
| | - Fu-Geng Sheng
- Department of Radiology, the Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
- *Correspondence: Jian-Ming Cai, ; Fu-Geng Sheng,
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