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Xie Y, Wang Z, Chen Q, Tang H, Huang J, Liang P. Enhancing substance identification by Raman spectroscopy using deep neural convolutional networks with an attention mechanism. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2024. [PMID: 39140306 DOI: 10.1039/d4ay00602j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/15/2024]
Abstract
Raman spectroscopy is widely used for substance identification, providing molecular information from various components along with noise and instrument interference. Consequently, identifying components based on Raman spectra remains challenging. In this study, we collected Raman spectral data of 474 hazardous chemical substances using a portable Raman spectrometer, resulting in a dataset of 59 468 spectra. Our research employed a deep neural convolutional network based on the ResNet architecture, incorporating an attention mechanism called the SE module. By enhancing the weighting of certain spectral features, the performance of the model was significantly improved. We also investigated the classification predictive performance of the model under small-sample conditions, facilitating the addition of new hazardous chemical categories for future deployment on mobile devices. Additionally, we explored the features extracted by the convolutional neural network from Raman spectra, considering both Raman intensity and Raman shift aspects. We discovered that the neural network did not solely rely on intensity or shift for substance classification, but rather effectively combined both aspects. This research contributes to the advancement of Raman spectroscopy applications for hazardous chemical identification, particularly in scenarios with limited data availability. The findings shed light on the significance of spectral features in the model's decision-making process and have implications for broader applications of deep learning techniques in Raman spectroscopy-based substance identification.
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Affiliation(s)
- Yuhao Xie
- College of Optical and Electronic Technology, China Jiliang University, 310018, Hangzhou, China.
- Xiamen Palantier Technology Co., Ltd, Xiamen, 361115, China
| | - Zilong Wang
- College of Optical and Electronic Technology, China Jiliang University, 310018, Hangzhou, China.
- Xiamen Palantier Technology Co., Ltd, Xiamen, 361115, China
| | - Qiang Chen
- College of Metrology and Measurement Engineering, China Jiliang University, Hangzhou 310018, China
| | - Heshan Tang
- Xiamen Palantier Technology Co., Ltd, Xiamen, 361115, China
| | - Jie Huang
- College of Optical and Electronic Technology, China Jiliang University, 310018, Hangzhou, China.
| | - Pei Liang
- College of Optical and Electronic Technology, China Jiliang University, 310018, Hangzhou, China.
- Xiamen Palantier Technology Co., Ltd, Xiamen, 361115, China
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Ma J, Choi SJ, Kim S, Hong M. Performance Comparison of Convolutional Neural Network-Based Hearing Loss Classification Model Using Auditory Brainstem Response Data. Diagnostics (Basel) 2024; 14:1232. [PMID: 38928647 PMCID: PMC11202863 DOI: 10.3390/diagnostics14121232] [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: 05/08/2024] [Revised: 06/06/2024] [Accepted: 06/09/2024] [Indexed: 06/28/2024] Open
Abstract
This study evaluates the efficacy of several Convolutional Neural Network (CNN) models for the classification of hearing loss in patients using preprocessed auditory brainstem response (ABR) image data. Specifically, we employed six CNN architectures-VGG16, VGG19, DenseNet121, DenseNet-201, AlexNet, and InceptionV3-to differentiate between patients with hearing loss and those with normal hearing. A dataset comprising 7990 preprocessed ABR images was utilized to assess the performance and accuracy of these models. Each model was systematically tested to determine its capability to accurately classify hearing loss. A comparative analysis of the models focused on metrics of accuracy and computational efficiency. The results indicated that the AlexNet model exhibited superior performance, achieving an accuracy of 95.93%. The findings from this research suggest that deep learning models, particularly AlexNet in this instance, hold significant potential for automating the diagnosis of hearing loss using ABR graph data. Future work will aim to refine these models to enhance their diagnostic accuracy and efficiency, fostering their practical application in clinical settings.
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Affiliation(s)
- Jun Ma
- Department of Software Convergence, Soonchunhyang University, Asan 31538, Republic of Korea;
| | - Seong Jun Choi
- Department of Otorhinolaryngology—Head and Neck Surgery, College of Medicine, Soonchunhyang University Cheonan Hospital, Cheonan 31151, Republic of Korea;
| | - Sungyeup Kim
- Insitute for Artificial Intelligence and Software, Soonchunhyang University, Asan 31538, Republic of Korea;
| | - Min Hong
- Department of Computer Software Engineering, Soonchunhyang University, Asan 31538, Republic of Korea
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3
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Saha A, Ganie SM, Dutta Pramanik PK, Yadav RK, Mallik S, Zhao Z. Correction: VER-Net: a hybrid transfer learning model for lung cancer detection using CT scan images. BMC Med Imaging 2024; 24:128. [PMID: 38822231 PMCID: PMC11140995 DOI: 10.1186/s12880-024-01315-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/02/2024] Open
Affiliation(s)
- Anindita Saha
- Department of Computing Science and Engineering, IFTM University, Moradabad, Uttar Pradesh, India
| | - Shahid Mohammad Ganie
- AI Research Centre, Department of Analytics, School of Business, Woxsen University, Hyderabad, Telangana, 502345, India
| | - Pijush Kanti Dutta Pramanik
- School of Computer Applications and Technology, Galgotias University, Greater Noida, Uttar Pradesh, 203201, India.
| | - Rakesh Kumar Yadav
- Department of Computer Science & Engineering, MSOET, Maharishi University of Information Technology, Lucknow, Uttar Pradesh, India
| | - Saurav Mallik
- Department of Environmental Health, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Zhongming Zhao
- Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA.
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Saha A, Ganie SM, Pramanik PKD, Yadav RK, Mallik S, Zhao Z. VER-Net: a hybrid transfer learning model for lung cancer detection using CT scan images. BMC Med Imaging 2024; 24:120. [PMID: 38789925 PMCID: PMC11127393 DOI: 10.1186/s12880-024-01238-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2023] [Accepted: 03/05/2024] [Indexed: 05/26/2024] Open
Abstract
BACKGROUND Lung cancer is the second most common cancer worldwide, with over two million new cases per year. Early identification would allow healthcare practitioners to handle it more effectively. The advancement of computer-aided detection systems significantly impacted clinical analysis and decision-making on human disease. Towards this, machine learning and deep learning techniques are successfully being applied. Due to several advantages, transfer learning has become popular for disease detection based on image data. METHODS In this work, we build a novel transfer learning model (VER-Net) by stacking three different transfer learning models to detect lung cancer using lung CT scan images. The model is trained to map the CT scan images with four lung cancer classes. Various measures, such as image preprocessing, data augmentation, and hyperparameter tuning, are taken to improve the efficacy of VER-Net. All the models are trained and evaluated using multiclass classifications chest CT images. RESULTS The experimental results confirm that VER-Net outperformed the other eight transfer learning models compared with. VER-Net scored 91%, 92%, 91%, and 91.3% when tested for accuracy, precision, recall, and F1-score, respectively. Compared to the state-of-the-art, VER-Net has better accuracy. CONCLUSION VER-Net is not only effectively used for lung cancer detection but may also be useful for other diseases for which CT scan images are available.
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Affiliation(s)
- Anindita Saha
- Department of Computing Science and Engineering, IFTM University, Moradabad, Uttar Pradesh, India
| | - Shahid Mohammad Ganie
- AI Research Centre, Department of Analytics, School of Business, Woxsen University, Hyderabad, Telangana, 502345, India
| | - Pijush Kanti Dutta Pramanik
- School of Computer Applications and Technology, Galgotias University, Greater Noida, Uttar Pradesh, 203201, India.
| | - Rakesh Kumar Yadav
- Department of Computer Science & Engineering, MSOET, Maharishi University of Information Technology, Lucknow, Uttar Pradesh, India
| | - Saurav Mallik
- Department of Environmental Health, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Zhongming Zhao
- Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA.
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Pradeep V, Jayachandra AB, Askar SS, Abouhawwash M. Hyperparameter tuning using Lévy flight and interactive crossover-based reptile search algorithm for eye movement event classification. Front Physiol 2024; 15:1366910. [PMID: 38812881 PMCID: PMC11134024 DOI: 10.3389/fphys.2024.1366910] [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: 01/07/2024] [Accepted: 04/10/2024] [Indexed: 05/31/2024] Open
Abstract
Introduction: Eye movement is one of the cues used in human-machine interface technologies for predicting the intention of users. The developing application in eye movement event detection is the creation of assistive technologies for paralyzed patients. However, developing an effective classifier is one of the main issues in eye movement event detection. Methods: In this paper, bidirectional long short-term memory (BILSTM) is proposed along with hyperparameter tuning for achieving effective eye movement event classification. The Lévy flight and interactive crossover-based reptile search algorithm (LICRSA) is used for optimizing the hyperparameters of BILSTM. The issues related to overfitting are avoided by using fuzzy data augmentation (FDA), and a deep neural network, namely, VGG-19, is used for extracting features from eye movements. Therefore, the optimization of hyperparameters using LICRSA enhances the classification of eye movement events using BILSTM. Results and Discussion: The proposed BILSTM-LICRSA is evaluated by using accuracy, precision, sensitivity, F1-score, area under the receiver operating characteristic (AUROC) curve measure, and area under the precision-recall curve (AUPRC) measure for four datasets, namely, Lund2013, collected dataset, GazeBaseR, and UTMultiView. The gazeNet, human manual classification (HMC), and multi-source information-embedded approach (MSIEA) are used for comparison with the BILSTM-LICRSA. The F1-score of BILSTM-LICRSA for the GazeBaseR dataset is 98.99%, which is higher than that of the MSIEA.
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Affiliation(s)
- V. Pradeep
- Department of Information Science and Engineering, Alva’s Institute of Engineering and Technology, Mangaluru, India
| | - Ananda Babu Jayachandra
- Department of Information Science and Engineering, Malnad College of Engineering, Hassan, India
| | - S. S. Askar
- Department of Statistics and Operations Research, College of Science, King Saud University, Riyadh, Saudi Arabia
| | - Mohamed Abouhawwash
- Department of Mathematics, Faculty of Science, Mansoura University, Mansoura, Egypt
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Chilukoti SV, Shan L, Tida VS, Maida AS, Hei X. A reliable diabetic retinopathy grading via transfer learning and ensemble learning with quadratic weighted kappa metric. BMC Med Inform Decis Mak 2024; 24:37. [PMID: 38321416 PMCID: PMC11323616 DOI: 10.1186/s12911-024-02446-x] [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: 12/15/2022] [Accepted: 01/29/2024] [Indexed: 02/08/2024] Open
Abstract
The most common eye infection in people with diabetes is diabetic retinopathy (DR). It might cause blurred vision or even total blindness. Therefore, it is essential to promote early detection to prevent or alleviate the impact of DR. However, due to the possibility that symptoms may not be noticeable in the early stages of DR, it is difficult for doctors to identify them. Therefore, numerous predictive models based on machine learning (ML) and deep learning (DL) have been developed to determine all stages of DR. However, existing DR classification models cannot classify every DR stage or use a computationally heavy approach. Common metrics such as accuracy, F1 score, precision, recall, and AUC-ROC score are not reliable for assessing DR grading. This is because they do not account for two key factors: the severity of the discrepancy between the assigned and predicted grades and the ordered nature of the DR grading scale. This research proposes computationally efficient ensemble methods for the classification of DR. These methods leverage pre-trained model weights, reducing training time and resource requirements. In addition, data augmentation techniques are used to address data limitations, improve features, and improve generalization. This combination offers a promising approach for accurate and robust DR grading. In particular, we take advantage of transfer learning using models trained on DR data and employ CLAHE for image enhancement and Gaussian blur for noise reduction. We propose a three-layer classifier that incorporates dropout and ReLU activation. This design aims to minimize overfitting while effectively extracting features and assigning DR grades. We prioritize the Quadratic Weighted Kappa (QWK) metric due to its sensitivity to label discrepancies, which is crucial for an accurate diagnosis of DR. This combined approach achieves state-of-the-art QWK scores (0.901, 0.967 and 0.944) in the Eyepacs, Aptos, and Messidor datasets.
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Affiliation(s)
- Sai Venkatesh Chilukoti
- School of Computing and Informatics, University of Louisiana at Lafayette, Lafayette, 70503, LA, USA
| | - Liqun Shan
- School of Computing and Informatics, University of Louisiana at Lafayette, Lafayette, 70503, LA, USA
| | - Vijay Srinivas Tida
- Department of Computer Science, College of Saint Benedict and Saint John's University, St. Joseph, MN, 56374, USA
| | - Anthony S Maida
- School of Computing and Informatics, University of Louisiana at Lafayette, Lafayette, 70503, LA, USA
| | - Xiali Hei
- School of Computing and Informatics, University of Louisiana at Lafayette, Lafayette, 70503, LA, USA.
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Ochoa-Astorga JE, Wang L, Du W, Peng Y. A Straightforward Bifurcation Pattern-Based Fundus Image Registration Method. SENSORS (BASEL, SWITZERLAND) 2023; 23:7809. [PMID: 37765866 PMCID: PMC10534639 DOI: 10.3390/s23187809] [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: 07/10/2023] [Revised: 08/23/2023] [Accepted: 09/08/2023] [Indexed: 09/29/2023]
Abstract
Fundus image registration is crucial in eye disease examination, as it enables the alignment of overlapping fundus images, facilitating a comprehensive assessment of conditions like diabetic retinopathy, where a single image's limited field of view might be insufficient. By combining multiple images, the field of view for retinal analysis is extended, and resolution is enhanced through super-resolution imaging. Moreover, this method facilitates patient follow-up through longitudinal studies. This paper proposes a straightforward method for fundus image registration based on bifurcations, which serve as prominent landmarks. The approach aims to establish a baseline for fundus image registration using these landmarks as feature points, addressing the current challenge of validation in this field. The proposed approach involves the use of a robust vascular tree segmentation method to detect feature points within a specified range. The method involves coarse vessel segmentation to analyze patterns in the skeleton of the segmentation foreground, followed by feature description based on the generation of a histogram of oriented gradients and determination of image relation through a transformation matrix. Image blending produces a seamless registered image. Evaluation on the FIRE dataset using registration error as the key parameter for accuracy demonstrates the method's effectiveness. The results show the superior performance of the proposed method compared to other techniques using vessel-based feature extraction or partially based on SURF, achieving an area under the curve of 0.526 for the entire FIRE dataset.
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Affiliation(s)
| | - Linni Wang
- Retina & Neuron-Ophthalmology, Tianjin Medical University Eye Hospital, Tianjin 300084, China
| | - Weiwei Du
- Information and Human Science, Kyoto Institute of Technology University, Kyoto 6068585, Japan;
| | - Yahui Peng
- School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China;
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8
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Lu J, Tong X, Wu H, Liu Y, Ouyang H, Zeng Q. Image classification and auxiliary diagnosis system for hyperpigmented skin diseases based on deep learning. Heliyon 2023; 9:e20186. [PMID: 37809588 PMCID: PMC10559947 DOI: 10.1016/j.heliyon.2023.e20186] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2022] [Revised: 09/11/2023] [Accepted: 09/13/2023] [Indexed: 10/10/2023] Open
Abstract
Background and aim Melasma (ML), naevus fusco-caeruleus zygomaticus (NZ), freckles (FC), cafe-au-lait spots (CS), nevus of ota (NO), and lentigo simplex (LS), are common skin diseases causing hyperpigmentation. Deep learning algorithms learn the inherent laws and representation levels of sample data and can analyze the internal details of the image and classify it objectively to be used for image diagnosis. However, deep learning algorithms that can assist clinicians in diagnosing skin hyperpigmentation conditions are lacking. Methods The optimal deep-learning image recognition algorithm was explored for the auxiliary diagnosis of hyperpigmented skin disease. Pretrained models, such as VGG-19, GoogLeNet, InceptionV3, ResNet50V2, ResNet101V2, ResNet152V2, InceptionResNetV2, DesseNet201, MobileNet, and NASNetMobile were used to classify images of six common hyperpigmented skin diseases. The best deep learning algorithm for developing an online clinical diagnosis system was selected by using accuracy and area under curve (AUC) as evaluation indicators. Results In this research, the parameters of the above-mentioned ten deep learning algorithms were 18333510, 5979702, 21815078, 23577094, 42638854, 58343942, 54345958, 18333510, 3235014, and 4276058, respectively, and their training time was 380, 162, 199, 188, 315, 511, 471, 697, 101, and 144 min respectively. The respective accuracies of the training set were 85.94%, 99.72%, 99.61%, 99.52%, 99.52%, 98.84%, 99.61%, 99.13%, 99.52%, and 99.61%. The accuracy rates of the test set data were 73.28%, 57.40%, 70.04%, 71.48%, 68.23%, 71.11%, 71.84%, 73.28%, 70.39%, and 43.68%, respectively. Finally, the areas of AUC curves were 0.93, 0.86, 0.93, 0.91, 0.91, 0.92, 0.93, 0.92, 0.93, and 0.82, respectively. Conclusions The experimental parameters, training time, accuracy, and AUC of the above models suggest that MobileNet provides a good clinical application prospect in the auxiliary diagnosis of hyperpigmented skin.
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Affiliation(s)
- Jianyun Lu
- Department of Dermatology, Third Xiangya Hospital, Central South University, Changsha 410013, PR China
| | - Xiaoliang Tong
- Department of Dermatology, Third Xiangya Hospital, Central South University, Changsha 410013, PR China
| | - Hongping Wu
- Vocational Teachers College, Jiangxi Agricultural University, NanChang 330045, PR China
| | - Yaoxinchuan Liu
- Vocational Teachers College, Jiangxi Agricultural University, NanChang 330045, PR China
| | - Huidan Ouyang
- Vocational Teachers College, Jiangxi Agricultural University, NanChang 330045, PR China
| | - Qinghai Zeng
- Department of Dermatology, Third Xiangya Hospital, Central South University, Changsha 410013, PR China
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Aziz RM, Mahto R, Das A, Ahmed SU, Roy P, Mallik S, Li A. CO-WOA: Novel Optimization Approach for Deep Learning Classification of Fish Image. Chem Biodivers 2023; 20:e202201123. [PMID: 37394680 DOI: 10.1002/cbdv.202201123] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 06/28/2023] [Accepted: 06/29/2023] [Indexed: 07/04/2023]
Abstract
The most significant groupings of cold-blooded creatures are the fish family. It is crucial to recognize and categorize the most significant species of fish since various species of seafood diseases and decay exhibit different symptoms. Systems based on enhanced deep learning can replace the area's currently cumbersome and sluggish traditional approaches. Although it seems straightforward, classifying fish images is a complex procedure. In addition, the scientific study of population distribution and geographic patterns is important for advancing the field's present advancements. The goal of the proposed work is to identify the best performing strategy using cutting-edge computer vision, the Chaotic Oppositional Based Whale Optimization Algorithm (CO-WOA), and data mining techniques. Performance comparisons with leading models, such as Convolutional Neural Networks (CNN) and VGG-19, are made to confirm the applicability of the suggested method. The suggested feature extraction approach with Proposed Deep Learning Model was used in the research, yielding accuracy rates of 100 %. The performance was also compared to cutting-edge image processing models with an accuracy of 98.48 %, 98.58 %, 99.04 %, 98.44 %, 99.18 % and 99.63 % such as Convolutional Neural Networks, ResNet150V2, DenseNet, Visual Geometry Group-19, Inception V3, Xception. Using an empirical method leveraging artificial neural networks, the Proposed Deep Learning model was shown to be the best model.
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Affiliation(s)
- Rabia Musheer Aziz
- Mathematics division, School of Advanced Sciences and Languages, VIT Bhopal University, Kothrikalan, Sehore, 466116, M.P., India
| | - Rajul Mahto
- School of Computing Science and Engineering, VIT Bhopal University, Kothrikalan, Sehore, 466116, M.P., India
| | - Aryan Das
- School of Computing Science and Engineering, VIT Bhopal University, Kothrikalan, Sehore, 466116, M.P., India
| | - Saboor Uddin Ahmed
- School of Computing Science and Engineering, VIT Bhopal University, Kothrikalan, Sehore, 466116, M.P., India
| | - Priyanka Roy
- Mathematics division, School of Advanced Sciences and Languages, VIT Bhopal University, Kothrikalan, Sehore, 466116, M.P., India
| | - Saurav Mallik
- Molecular and Integrative Physiological Sciences, Department of Environmental health, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA
- Department of Pharmacology & Toxicology, University of Arizona, Tucson, AZ 85721, USA
| | - Aimin Li
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
- School of Computer Science and Engineering, Xi'an University of Technology, Shaanxi, 710048, China
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Kheir AM, Elnashar A, Mosad A, Govind A. An improved deep learning procedure for statistical downscaling of climate data. Heliyon 2023; 9:e18200. [PMID: 37539241 PMCID: PMC10393634 DOI: 10.1016/j.heliyon.2023.e18200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 07/11/2023] [Accepted: 07/11/2023] [Indexed: 08/05/2023] Open
Abstract
Recent climate change (CC) scenarios from the Coupled Model Intercomparison Project Phase 6 (CMIP6) have just been released in coarse resolution. Deep learning (DL) based on statistical downscaling has recently been used, but more research is needed, particularly in arid regions, because little is known about their suitability for extrapolating future CC scenarios. Here we analyzed this issue by downscaling maximum, and minimum temperature over the Egyptian domain based on one General Circulation Model (GCM) as CanESM5 and two shared socioeconomic pathways (SSPs) as SSP4.5 and SSP8.5 from CMIP6 using Convolutional Neural Network (CNN) herein after called CNNSD. The downscaled maximum and minimum temperatures based CNNSD was able to reproduce the observed climate over historical and future periods at a finer resolution (0.1°), reducing the biases exhibited by the original scenario. To the best of our knowledge, this is the first time CNN has been used to downscale CMIP6 scenarios, particularly in arid regions. The downscaled analysis showed that maximum and minimum temperatures are expected to rise by 4.8 °C and 4.0 °C, respectively, in the future (2015-2100), compared to the historical period, under the moderate scenario (SSP4.5). Meanwhile, under the Fossil-fueled Development scenario (SSP8.5), these values will rise by 6.3 °C and 4.2 °C, respectively as analyzed by the CNNSD. The developed approach could be used not only in Egypt but also in other developing countries, which are especially vulnerable to climate change and has a scarcity of related research. The established downscaled approach's supply can be used to provide climate services, as a driver for impact studies and adaptation decisions, and as information for policy development. More research is needed, however, to include multi-GCMs to quantify the uncertainties between GCMs and SSPs, improving the outputs for use in climate change impacts and adaptations for food and nutrition security.
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Affiliation(s)
- Ahmed M.S. Kheir
- International Center for Agricultural Research in the Dry Areas (ICARDA), Maadi 11728, Egypt
- Soils, Water and Environment Research Institute, Agricultural Research Center, 9 Cairo University Street, Giza 12112, Egypt
| | - Abdelrazek Elnashar
- Department of Natural Resources, Faculty of African Postgraduate Studies, Cairo University, Giza 12613, Egypt
| | - Alaa Mosad
- International Center for Agricultural Research in the Dry Areas (ICARDA), Maadi 11728, Egypt
- Soils, Water and Environment Research Institute, Agricultural Research Center, 9 Cairo University Street, Giza 12112, Egypt
| | - Ajit Govind
- International Center for Agricultural Research in the Dry Areas (ICARDA), Maadi 11728, Egypt
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Yoo S, Lee H, Kim J. Deep Learning for Identifying Promising Drug Candidates in Drug-Phospholipid Complexes. Molecules 2023; 28:4821. [PMID: 37375375 DOI: 10.3390/molecules28124821] [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: 05/17/2023] [Revised: 06/14/2023] [Accepted: 06/15/2023] [Indexed: 06/29/2023] Open
Abstract
Drug-phospholipid complexing is a promising formulation technology for improving the low bioavailability of active pharmaceutical ingredients (APIs). However, identifying whether phospholipid and candidate drug can form a complex through in vitro tests can be costly and time-consuming due to the physicochemical properties and experimental environment. In a previous study, the authors developed seven machine learning models to predict drug-phospholipid complex formation, and the lightGBM model demonstrated the best performance. However, the previous study was unable to sufficiently address the degradation of test performance caused by the small size of the training data with class imbalance, and it had the limitation of considering only machine learning techniques. To overcome these limitations, we propose a new deep learning-based prediction model that employs variational autoencoder (VAE) and principal component analysis (PCA) techniques to improve prediction performance. The model uses a multi-layer one-dimensional convolutional neural network (CNN) with a skip connection to effectively capture the complex relationship between drugs and lipid molecules. The computer simulation results demonstrate that our proposed model performs better than the previous model in all performance metrics.
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Affiliation(s)
- Soyoung Yoo
- Department of Artificial Intelligence, Sejong University, Seoul 05006, Republic of Korea
| | - Hanbyul Lee
- Department of Artificial Intelligence, Sejong University, Seoul 05006, Republic of Korea
| | - Junghyun Kim
- Department of Artificial Intelligence, Sejong University, Seoul 05006, Republic of Korea
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12
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Xie X, Xia F, Wu Y, Liu S, Yan K, Xu H, Ji Z. A Novel Feature Selection Strategy Based on Salp Swarm Algorithm for Plant Disease Detection. PLANT PHENOMICS (WASHINGTON, D.C.) 2023; 5:0039. [PMID: 37228513 PMCID: PMC10204742 DOI: 10.34133/plantphenomics.0039] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Accepted: 02/28/2023] [Indexed: 05/27/2023]
Abstract
Deep learning has been widely used for plant disease recognition in smart agriculture and has proven to be a powerful tool for image classification and pattern recognition. However, it has limited interpretability for deep features. With the transfer of expert knowledge, handcrafted features provide a new way for personalized diagnosis of plant diseases. However, irrelevant and redundant features lead to high dimensionality. In this study, we proposed a swarm intelligence algorithm for feature selection [salp swarm algorithm for feature selection (SSAFS)] in image-based plant disease detection. SSAFS is employed to determine the ideal combination of handcrafted features to maximize classification success while minimizing the number of features. To verify the effectiveness of the developed SSAFS algorithm, we conducted experimental studies using SSAFS and 5 metaheuristic algorithms. Several evaluation metrics were used to evaluate and analyze the performance of these methods on 4 datasets from the UCI machine learning repository and 6 plant phenomics datasets from PlantVillage. Experimental results and statistical analyses validated the outstanding performance of SSAFS compared to existing state-of-the-art algorithms, confirming the superiority of SSAFS in exploring the feature space and identifying the most valuable features for diseased plant image classification. This computational tool will allow us to explore an optimal combination of handcrafted features to improve plant disease recognition accuracy and processing time.
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Affiliation(s)
- Xiaojun Xie
- College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, Jiangsu 210095, China
- Center for Data Science and Intelligent Computing, Nanjing Agricultural University, Nanjing, Jiangsu 210095, China
| | - Fei Xia
- College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, Jiangsu 210095, China
| | - Yufeng Wu
- State Key Laboratory for Crop Genetics and Germplasm Enhancement, Bioinformatics Center, Academy for Advanced Interdisciplinary Studies, Nanjing Agricultural University, Nanjing, Jiangsu 210095, China
| | - Shouyang Liu
- Academy for Advanced Interdisciplinary Studies, Nanjing Agricultural University, Nanjing, Jiangsu 210095, China
| | - Ke Yan
- Department of the Built Environment, College of Design and Engineering, National University of Singapore, 4 Architecture Drive, Singapore 117566, Singapore
| | - Huanliang Xu
- College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, Jiangsu 210095, China
| | - Zhiwei Ji
- College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, Jiangsu 210095, China
- Center for Data Science and Intelligent Computing, Nanjing Agricultural University, Nanjing, Jiangsu 210095, China
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13
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Muchuchuti S, Viriri S. Retinal Disease Detection Using Deep Learning Techniques: A Comprehensive Review. J Imaging 2023; 9:84. [PMID: 37103235 PMCID: PMC10145952 DOI: 10.3390/jimaging9040084] [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: 02/28/2023] [Revised: 04/02/2023] [Accepted: 04/07/2023] [Indexed: 04/28/2023] Open
Abstract
Millions of people are affected by retinal abnormalities worldwide. Early detection and treatment of these abnormalities could arrest further progression, saving multitudes from avoidable blindness. Manual disease detection is time-consuming, tedious and lacks repeatability. There have been efforts to automate ocular disease detection, riding on the successes of the application of Deep Convolutional Neural Networks (DCNNs) and vision transformers (ViTs) for Computer-Aided Diagnosis (CAD). These models have performed well, however, there remain challenges owing to the complex nature of retinal lesions. This work reviews the most common retinal pathologies, provides an overview of prevalent imaging modalities and presents a critical evaluation of current deep-learning research for the detection and grading of glaucoma, diabetic retinopathy, Age-Related Macular Degeneration and multiple retinal diseases. The work concluded that CAD, through deep learning, will increasingly be vital as an assistive technology. As future work, there is a need to explore the potential impact of using ensemble CNN architectures in multiclass, multilabel tasks. Efforts should also be expended on the improvement of model explainability to win the trust of clinicians and patients.
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Affiliation(s)
| | - Serestina Viriri
- School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Durban 4001, South Africa
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14
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You H, Yu L, Tian S, Cai W. A stereo spatial decoupling network for medical image classification. COMPLEX INTELL SYST 2023; 9:1-10. [PMID: 37361963 PMCID: PMC10107597 DOI: 10.1007/s40747-023-01049-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Accepted: 03/09/2023] [Indexed: 06/28/2023]
Abstract
Deep convolutional neural network (CNN) has made great progress in medical image classification. However, it is difficult to establish effective spatial associations, and always extracts similar low-level features, resulting in redundancy of information. To solve these limitations, we propose a stereo spatial discoupling network (TSDNets), which can leverage the multi-dimensional spatial details of medical images. Then, we use an attention mechanism to progressively extract the most discriminative features from three directions: horizontal, vertical, and depth. Moreover, a cross feature screening strategy is used to divide the original feature maps into three levels: important, secondary and redundant. Specifically, we design a cross feature screening module (CFSM) and a semantic guided decoupling module (SGDM) to model multi-dimension spatial relationships, thereby enhancing the feature representation capabilities. The extensive experiments conducted on multiple open source baseline datasets demonstrate that our TSDNets outperforms previous state-of-the-art models.
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Affiliation(s)
- Hongfeng You
- School of Information Science and Engineering, Xinjiang University, Urumqi, 830000 China
| | - Long Yu
- Network Center, Xinjiang University, Urumqi, 830000 China
| | - Shengwei Tian
- Software College, Xinjiang University, Urumqi, 830000 China
| | - Weiwei Cai
- School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, 214122 China
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15
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Türk F, Kökver Y. Detection of Lung Opacity and Treatment Planning with Three-Channel Fusion CNN Model. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2023:1-13. [PMID: 37361471 PMCID: PMC10103673 DOI: 10.1007/s13369-023-07843-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/01/2022] [Accepted: 03/20/2023] [Indexed: 06/28/2023]
Abstract
Lung opacities are extremely important for physicians to monitor and can have irreversible consequences for patients if misdiagnosed or confused with other findings. Therefore, long-term monitoring of the regions of lung opacity is recommended by physicians. Tracking the regional dimensions of images and classifying differences from other lung cases can provide significant ease to physicians. Deep learning methods can be easily used for the detection, classification, and segmentation of lung opacity. In this study, a three-channel fusion CNN model is applied to effectively detect lung opacity on a balanced dataset compiled from public datasets. The MobileNetV2 architecture is used in the first channel, the InceptionV3 model in the second channel, and the VGG19 architecture in the third channel. The ResNet architecture is used for feature transfer from the previous layer to the current layer. In addition to being easy to implement, the proposed approach can also provide significant cost and time advantages to physicians. Our accuracy values for two, three, four, and five classes on the newly compiled dataset for lung opacity classifications are found to be 92.52%, 92.44%, 87.12%, and 91.71%, respectively.
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Affiliation(s)
- Fuat Türk
- Department of Computer Engineering, Çankırı Karatekin University, 18100 Çankırı, Turkey
| | - Yunus Kökver
- Department of Computer Technologies, Elmadağ Vocational School, Ankara University, 06780 Ankara, Turkey
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16
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Butuner R, Cinar I, Taspinar YS, Kursun R, Calp MH, Koklu M. Classification of deep image features of lentil varieties with machine learning techniques. Eur Food Res Technol 2023. [DOI: 10.1007/s00217-023-04214-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/19/2023]
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17
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Comparing Deep Feature Extraction Strategies for Diabetic Retinopathy Stage Classification from Fundus Images. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2023. [DOI: 10.1007/s13369-022-07547-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
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18
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Analysis of Diabetic Retinopathy (DR) Based on the Deep Learning. INFORMATION 2023. [DOI: 10.3390/info14010030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Abstract
If Diabetic Retinopathy (DR) patients do not receive quick diagnosis and treatment, they may lose vision. DR, an eye disorder caused by high blood glucose, is becoming more prevalent worldwide. Once early warning signs are detected, the severity of the disease must be validated before choosing the best treatment. In this research, a deep learning network is used to automatically detect and classify DR fundus images depending on severity using AlexNet and Resnet101-based feature extraction. Interconnected layers helps to identify the critical features or characteristics; in addition, Ant Colony systems also help choose the characteristics. Passing these chosen attributes through SVM with multiple kernels yielded the final classification model with promising accuracy. The experiment based on 750 features proves that the proposed approach has achieved an accuracy of 93%.
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19
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Mahmood T, Choi J, Ryoung Park K. Artificial Intelligence-based Classification of Pollen Grains Using Attention-guided Pollen Features Aggregation Network. JOURNAL OF KING SAUD UNIVERSITY - COMPUTER AND INFORMATION SCIENCES 2023. [DOI: 10.1016/j.jksuci.2023.01.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
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20
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Dalvi PP, Edla DR, Purushothama BR. Diagnosis of Coronavirus Disease From Chest X-Ray Images Using DenseNet-169 Architecture. SN COMPUTER SCIENCE 2023; 4:214. [PMID: 36811126 PMCID: PMC9936468 DOI: 10.1007/s42979-022-01627-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Accepted: 12/17/2022] [Indexed: 02/19/2023]
Abstract
The coronavirus disease (COVID-19) is a very contagious and dangerous disease that affects the human respiratory system. Early detection of this disease is very crucial to contain the further spread of the virus. In this paper, we proposed a methodology using DenseNet-169 architecture for diagnosing the disease from chest X-ray images of the patients. We used a pretrained neural network and then utilised the transfer learning method for training on our dataset. We also used Nearest-Neighbour interpolation technique for data preprocessing and Adam Optimizer at the end for optimization. Our methodology achieved 96.37 % accuracy which was better than that obtained using other deep learning models like AlexNet, ResNet-50, VGG-16, and VGG-19.
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Affiliation(s)
- Pooja Pradeep Dalvi
- Department of Computer Science and Engineering, National Institute of Technology, Goa, India
| | - Damodar Reddy Edla
- Department of Computer Science and Engineering, National Institute of Technology, Goa, India
| | - B. R. Purushothama
- Department of Computer Science and Engineering, National Institute of Technology, Goa, India
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21
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Yao X, Wang X, Wang SH, Zhang YD. A comprehensive survey on convolutional neural network in medical image analysis. MULTIMEDIA TOOLS AND APPLICATIONS 2022; 81:41361-41405. [DOI: 10.1007/s11042-020-09634-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/21/2020] [Revised: 07/30/2020] [Accepted: 08/13/2020] [Indexed: 08/30/2023]
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22
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Lanjewar MG, Shaikh AY, Parab J. Cloud-based COVID-19 disease prediction system from X-Ray images using convolutional neural network on smartphone. MULTIMEDIA TOOLS AND APPLICATIONS 2022; 82:1-30. [PMID: 36467434 PMCID: PMC9684956 DOI: 10.1007/s11042-022-14232-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Revised: 11/01/2022] [Accepted: 11/04/2022] [Indexed: 06/17/2023]
Abstract
COVID-19 has engulfed over 200 nations through human-to-human transmission, either directly or indirectly. Reverse Transcription-polymerase Chain Reaction (RT-PCR) has been endorsed as a standard COVID-19 diagnostic procedure but has caveats such as low sensitivity, the need for a skilled workforce, and is time-consuming. Coronaviruses show significant manifestation in Chest X-Ray (CX-Ray) images and, thus, can be a viable option for an alternate COVID-19 diagnostic strategy. An automatic COVID-19 detection system can be developed to detect the disease, thus reducing strain on the healthcare system. This paper discusses a real-time Convolutional Neural Network (CNN) based system for COVID-19 illness prediction from CX-Ray images on the cloud. The implemented CNN model displays exemplary results, with training accuracy being 99.94% and validation accuracy reaching 98.81%. The confusion matrix was utilized to assess the models' outcome and achieved 99% precision, 98% recall, 99% F1 score, 100% training area under the curve (AUC) and 98.3% validation AUC. The same CX-Ray dataset was also employed to predict the COVID-19 disease with deep Convolution Neural Networks (DCNN), such as ResNet50, VGG19, InceptonV3, and Xception. The prediction outcome demonstrated that the present CNN was more capable than the DCNN models. The efficient CNN model was deployed to the Platform as a Service (PaaS) cloud.
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Affiliation(s)
- Madhusudan G. Lanjewar
- School of Physical and Applied Sciences, Goa University, Taleigao Plateau, Goa, 403206 India
| | - Arman Yusuf Shaikh
- School of Physical and Applied Sciences, Goa University, Taleigao Plateau, Goa, 403206 India
| | - Jivan Parab
- School of Physical and Applied Sciences, Goa University, Taleigao Plateau, Goa, 403206 India
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23
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Pterygium Screening and Lesion Area Segmentation Based on Deep Learning. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:3942110. [DOI: 10.1155/2022/3942110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Accepted: 04/18/2022] [Indexed: 11/23/2022]
Abstract
A two-category model and a segmentation model of pterygium were proposed to assist ophthalmologists in establishing the diagnosis of ophthalmic diseases. A total of 367 normal anterior segment images and 367 pterygium anterior segment images were collected at the Affiliated Eye Hospital of Nanjing Medical University. AlexNet, VGG16, ResNet18, and ResNet50 models were used to train the two-category pterygium models. A total of 150 normal and 150 pterygium anterior segment images were used to test the models, and the results were compared. The main evaluation indicators, including sensitivity, specificity, area under the curve, kappa value, and receiver operator characteristic curves of the four models, were compared. Simultaneously, 367 pterygium anterior segment images were used to train two improved pterygium segmentation models based on PSPNet. A total of 150 pterygium images were used to test the models, and the results were compared with those of the other four segmentation models. The main evaluation indicators included mean intersection over union (MIOU), IOU, mean average precision (MPA), and PA. Among the two-category models of pterygium, the best diagnostic result was obtained using the VGG16 model. The diagnostic accuracy, kappa value, diagnostic sensitivity of pterygium, diagnostic specificity of pterygium, and F1-score were 99%, 98%, 98.67%, 99.33%, and 99%, respectively. Among the pterygium segmentation models, the double phase-fusion PSPNet model had the best results, with MIOU, IOU, MPA, and PA of 86.57%, 78.1%, 92.3%, and 86.96%, respectively. This study designed a pterygium two-category model and a pterygium segmentation model for the images of the normal anterior and pterygium anterior segments, which could help patients self-screen easily and assist ophthalmologists in establishing the diagnosis of ophthalmic diseases and marking the actual scope of surgery.
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24
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Novel pruning and truncating of the mixture of vine copula clustering models. Sci Rep 2022; 12:19815. [PMID: 36396705 PMCID: PMC9671921 DOI: 10.1038/s41598-022-24274-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Accepted: 11/14/2022] [Indexed: 11/18/2022] Open
Abstract
The mixture of the vine copula densities allows selecting the vine structure, the most appropriate type of parametric marginal distributions, and the pair-copulas individually for each cluster. Therefore, complex hidden dependence structures can be fully uncovered and captured by the mixture of vine copula models without restriction to the parametric shape of margins or dependency patterns. However, this flexibility comes with the cost of dramatic increases in the number of model parameters as the dimension increases. Pruning and truncating each cluster of the mixture model will dramatically reduce the number of model parameters. This paper, therefore, introduced the first pruning and truncating techniques for the model-based clustering algorithm using the vine copula model, providing a significant contribution to the state-of-the-art. We apply the proposed methods to a number of well-known data sets with different dimensions. The results show that the performance of the individual pruning and truncation for each model cluster is superior to an existing vine copula clustering model.
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25
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Rasheed HA, Davis T, Morales E, Fei Z, Grassi L, De Gainza A, Nouri-Mahdavi K, Caprioli J. DDLSNet: A Novel Deep Learning-Based System for Grading Funduscopic Images for Glaucomatous Damage. OPHTHALMOLOGY SCIENCE 2022; 3:100255. [PMID: 36619716 PMCID: PMC9813574 DOI: 10.1016/j.xops.2022.100255] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Revised: 10/03/2022] [Accepted: 11/03/2022] [Indexed: 11/13/2022]
Abstract
Purpose To report an image analysis pipeline, DDLSNet, consisting of a rim segmentation (RimNet) branch and a disc size classification (DiscNet) branch to automate estimation of the disc damage likelihood scale (DDLS). Design Retrospective observational. Participants RimNet and DiscNet were developed with 1208 and 11 536 optic disc photographs (ODPs), respectively. DDLSNet performance was evaluated on 120 ODPs from the RimNet test set, for which the DDLS scores were graded by clinicians. Reproducibility was evaluated on a group of 781 eyes, each with 2 ODPs taken within 4 years apart. Methods Disc damage likelihood scale calculation requires estimation of optic disc size, provided by DiscNet (VGG19 network), and the minimum rim-to-disc ratio (mRDR) or absent rim width (ARW), provided by RimNet (InceptionV3/LinkNet segmentation model). To build RimNet's dataset, glaucoma specialists marked optic disc rim and cup boundaries on ODPs. The "ground truth" mRDR or ARW was calculated. For DiscNet's dataset, corresponding OCT images provided "ground truth" disc size. Optic disc photographs were split into 80/10/10 for training, validation, and testing, respectively, for RimNet and DiscNet. DDLSNet estimation was tested against manual grading of DDLS by clinicians with the average score used as "ground truth." Reproducibility of DDLSNet grading was evaluated by repeating DDLS estimation on a dataset of nonprogressing paired ODPs taken at separate times. Main Outcome Measures The main outcome measure was a weighted kappa score between clinicians and the DDLSNet pipeline with agreement defined as ± 1 DDLS score difference. Results RimNet achieved an mRDR mean absolute error (MAE) of 0.04 (± 0.03) and an ARW MAE of 48.9 (± 35.9) degrees when compared to clinician segmentations. DiscNet achieved 73% (95% confidence interval [CI]: 70%, 75%) classification accuracy. DDLSNet achieved an average weighted kappa agreement of 0.54 (95% CI: 0.40, 0.68) compared to clinicians. Average interclinician agreement was 0.52 (95% CI: 0.49, 0.56). Reproducibility testing demonstrated that 96% of ODP pairs had a difference of ≤ 1 DDLS score. Conclusions DDLSNet achieved moderate agreement with clinicians for DDLS grading. This novel approach illustrates the feasibility of automated ODP grading for assessing glaucoma severity. Further improvements may be achieved by increasing the number of incomplete rims sample size, expanding the hyperparameter search, and increasing the agreement of clinicians grading ODPs.
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Affiliation(s)
- Haroon Adam Rasheed
- University of California Los Angeles David Geffen School of Medicine, Los Angeles, California
| | - Tyler Davis
- Department of Computer Science, University of California Los Angeles, Los Angeles, California
| | - Esteban Morales
- Glaucoma Division, Jules Stein Eye Institute, Los Angeles, California
| | - Zhe Fei
- University of California Los Angeles Jonathan and Karin Fielding School of Public Health, Los Angeles, California,Department of Biostatistics, University of California Los Angeles, Los Angeles, California
| | - Lourdes Grassi
- Glaucoma Division, Jules Stein Eye Institute, Los Angeles, California
| | | | | | - Joseph Caprioli
- Glaucoma Division, Jules Stein Eye Institute, Los Angeles, California,Correspondence: Joseph Caprioli, MD, Glaucoma Division, Jules Stein Eye Institute, 100 Stein Plaza, Los Angeles, CA 90095.
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26
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Hassan D, Gill HM, Happe M, Bhatwadekar AD, Hajrasouliha AR, Janga SC. Combining transfer learning with retinal lesion features for accurate detection of diabetic retinopathy. Front Med (Lausanne) 2022; 9:1050436. [PMID: 36425113 PMCID: PMC9681494 DOI: 10.3389/fmed.2022.1050436] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Accepted: 10/24/2022] [Indexed: 11/11/2022] Open
Abstract
Diabetic retinopathy (DR) is a late microvascular complication of Diabetes Mellitus (DM) that could lead to permanent blindness in patients, without early detection. Although adequate management of DM via regular eye examination can preserve vision in in 98% of the DR cases, DR screening and diagnoses based on clinical lesion features devised by expert clinicians; are costly, time-consuming and not sufficiently accurate. This raises the requirements for Artificial Intelligent (AI) systems which can accurately detect DR automatically and thus preventing DR before affecting vision. Hence, such systems can help clinician experts in certain cases and aid ophthalmologists in rapid diagnoses. To address such requirements, several approaches have been proposed in the literature that use Machine Learning (ML) and Deep Learning (DL) techniques to develop such systems. However, these approaches ignore the highly valuable clinical lesion features that could contribute significantly to the accurate detection of DR. Therefore, in this study we introduce a framework called DR-detector that employs the Extreme Gradient Boosting (XGBoost) ML model trained via the combination of the features extracted by the pretrained convolutional neural networks commonly known as transfer learning (TL) models and the clinical retinal lesion features for accurate detection of DR. The retinal lesion features are extracted via image segmentation technique using the UNET DL model and captures exudates (EXs), microaneurysms (MAs), and hemorrhages (HEMs) that are relevant lesions for DR detection. The feature combination approach implemented in DR-detector has been applied to two common TL models in the literature namely VGG-16 and ResNet-50. We trained the DR-detector model using a training dataset comprising of 1,840 color fundus images collected from e-ophtha, retinal lesions and APTOS 2019 Kaggle datasets of which 920 images are healthy. To validate the DR-detector model, we test the model on external dataset that consists of 81 healthy images collected from High-Resolution Fundus (HRF) dataset and MESSIDOR-2 datasets and 81 images with DR signs collected from Indian Diabetic Retinopathy Image Dataset (IDRID) dataset annotated for DR by expert. The experimental results show that the DR-detector model achieves a testing accuracy of 100% in detecting DR after training it with the combination of ResNet-50 and lesion features and 99.38% accuracy after training it with the combination of VGG-16 and lesion features. More importantly, the results also show a higher contribution of specific lesion features toward the performance of the DR-detector model. For instance, using only the hemorrhages feature to train the model, our model achieves an accuracy of 99.38 in detecting DR, which is higher than the accuracy when training the model with the combination of all lesion features (89%) and equal to the accuracy when training the model with the combination of all lesions and VGG-16 features together. This highlights the possibility of using only the clinical features, such as lesions that are clinically interpretable, to build the next generation of robust artificial intelligence (AI) systems with great clinical interpretability for DR detection. The code of the DR-detector framework is available on GitHub at https://github.com/Janga-Lab/DR-detector and can be readily employed for detecting DR from retinal image datasets.
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Affiliation(s)
- Doaa Hassan
- Department of BioHealth Informatics, School of Informatics and Computing, Indiana University Purdue University, Indianapolis, IN, United States
- Computers and Systems Department, National Telecommunication Institute, Cairo, Egypt
| | - Hunter Mathias Gill
- Department of BioHealth Informatics, School of Informatics and Computing, Indiana University Purdue University, Indianapolis, IN, United States
| | - Michael Happe
- Department of Ophthalmology, Glick Eye Institute, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Ashay D. Bhatwadekar
- Department of Ophthalmology, Glick Eye Institute, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Amir R. Hajrasouliha
- Department of Ophthalmology, Glick Eye Institute, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Sarath Chandra Janga
- Department of BioHealth Informatics, School of Informatics and Computing, Indiana University Purdue University, Indianapolis, IN, United States
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Medical Research and Library Building, Indianapolis, IN, United States
- Centre for Computational Biology and Bioinformatics, Indiana University School of Medicine, 5021 Health Information and Translational Sciences (HITS), Indianapolis, IN, United States
- *Correspondence: Sarath Chandra Janga
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27
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Kuanr M, Mohapatra P, Mittal S, Maindarkar M, Fouda MM, Saba L, Saxena S, Suri JS. Recommender System for the Efficient Treatment of COVID-19 Using a Convolutional Neural Network Model and Image Similarity. Diagnostics (Basel) 2022; 12:2700. [PMID: 36359545 PMCID: PMC9689970 DOI: 10.3390/diagnostics12112700] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2022] [Revised: 10/30/2022] [Accepted: 11/03/2022] [Indexed: 09/09/2023] Open
Abstract
Background: Hospitals face a significant problem meeting patients' medical needs during epidemics, especially when the number of patients increases rapidly, as seen during the recent COVID-19 pandemic. This study designs a treatment recommender system (RS) for the efficient management of human capital and resources such as doctors, medicines, and resources in hospitals. We hypothesize that a deep learning framework, when combined with search paradigms in an image framework, can make the RS very efficient. Methodology: This study uses a Convolutional neural network (CNN) model for the feature extraction of the images and discovers the most similar patients. The input queries patients from the hospital database with similar chest X-ray images. It uses a similarity metric for the similarity computation of the images. Results: This methodology recommends the doctors, medicines, and resources associated with similar patients to a COVID-19 patients being admitted to the hospital. The performance of the proposed RS is verified with five different feature extraction CNN models and four similarity measures. The proposed RS with a ResNet-50 CNN feature extraction model and Maxwell-Boltzmann similarity is found to be a proper framework for treatment recommendation with a mean average precision of more than 0.90 for threshold similarities in the range of 0.7 to 0.9 and an average highest cosine similarity of more than 0.95. Conclusions: Overall, an RS with a CNN model and image similarity is proven as an efficient tool for the proper management of resources during the peak period of pandemics and can be adopted in clinical settings.
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Affiliation(s)
- Madhusree Kuanr
- Department of Computer Science and Engineering, IIIT, Bhubaneswar 751003, India
| | | | - Sanchi Mittal
- Department of Computer Science and Engineering, IIIT, Bhubaneswar 751003, India
| | - Mahesh Maindarkar
- Stroke Monitoring and Diagnostic Division, AtheroPointTM, Roseville, CA 95661, USA
| | - Mostafa M. Fouda
- Department of Electrical and Computer Engineering, Idaho State University, Pocatello, ID 83209, USA
| | - Luca Saba
- Department of Radiology, University of Cagliari, 09123 Cagliari, Italy
| | - Sanjay Saxena
- Department of Computer Science and Engineering, IIIT, Bhubaneswar 751003, India
| | - Jasjit S. Suri
- Stroke Monitoring and Diagnostic Division, AtheroPointTM, Roseville, CA 95661, USA
- Knowledge Engineering Center, Global Biomedical Technologies, Inc., Roseville, CA 95661, USA
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28
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Dubey S, Dixit M. Recent developments on computer aided systems for diagnosis of diabetic retinopathy: a review. MULTIMEDIA TOOLS AND APPLICATIONS 2022; 82:14471-14525. [PMID: 36185322 PMCID: PMC9510498 DOI: 10.1007/s11042-022-13841-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 04/27/2022] [Accepted: 09/06/2022] [Indexed: 06/16/2023]
Abstract
Diabetes is a long-term condition in which the pancreas quits producing insulin or the body's insulin isn't utilised properly. One of the signs of diabetes is Diabetic Retinopathy. Diabetic retinopathy is the most prevalent type of diabetes, if remains unaddressed, diabetic retinopathy can affect all diabetics and become very serious, raising the chances of blindness. It is a chronic systemic condition that affects up to 80% of patients for more than ten years. Many researchers believe that if diabetes individuals are diagnosed early enough, they can be rescued from the condition in 90% of cases. Diabetes damages the capillaries, which are microscopic blood vessels in the retina. On images, blood vessel damage is usually noticeable. Therefore, in this study, several traditional, as well as deep learning-based approaches, are reviewed for the classification and detection of this particular diabetic-based eye disease known as diabetic retinopathy, and also the advantage of one approach over the other is also described. Along with the approaches, the dataset and the evaluation metrics useful for DR detection and classification are also discussed. The main finding of this study is to aware researchers about the different challenges occurs while detecting diabetic retinopathy using computer vision, deep learning techniques. Therefore, a purpose of this review paper is to sum up all the major aspects while detecting DR like lesion identification, classification and segmentation, security attacks on the deep learning models, proper categorization of datasets and evaluation metrics. As deep learning models are quite expensive and more prone to security attacks thus, in future it is advisable to develop a refined, reliable and robust model which overcomes all these aspects which are commonly found while designing deep learning models.
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Affiliation(s)
- Shradha Dubey
- Madhav Institute of Technology & Science (Department of Computer Science and Engineering), Gwalior, M.P. India
| | - Manish Dixit
- Madhav Institute of Technology & Science (Department of Computer Science and Engineering), Gwalior, M.P. India
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Zhao H, Tao B, Huang L, Chen B. A siamese network-based approach for vehicle pose estimation. Front Bioeng Biotechnol 2022; 10:948726. [PMID: 36118568 PMCID: PMC9478513 DOI: 10.3389/fbioe.2022.948726] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Accepted: 08/08/2022] [Indexed: 11/13/2022] Open
Abstract
We propose a deep learning-based vehicle pose estimation method based on a monocular camera called FPN PoseEstimateNet. The FPN PoseEstimateNet consists of a feature extractor and a pose calculate network. The feature extractor is based on Siamese network and a feature pyramid network (FPN) is adopted to deal with feature scales. Through the feature extractor, a correlation matrix between the input images is obtained for feature matching. With the time interval as the label, the feature extractor can be trained independently of the pose calculate network. On the basis of the correlation matrix and the standard matrix, the vehicle pose changes can be predicted by the pose calculate network. Results show that the network runs at a speed of 6 FPS, and the parameter size is 101.6 M. In different sequences, the angle error is within 8.26° and the maximum translation error is within 31.55 m.
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Affiliation(s)
- Haoyi Zhao
- Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan, China
- Wisdri Utility Tunnel Designing Institute, Wuhan, China
| | - Bo Tao
- Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan, China
- Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan, China
- *Correspondence: Bo Tao, ; Baojia Chen,
| | - Licheng Huang
- Precision Manufacturing Institute, Wuhan University of Science and Technology, Wuhan, China
- Research Center for Biomimetic Robot and Intelligent Measurement and Control, Wuhan University of Science and Technology, Wuhan, China
| | - Baojia Chen
- Hubei Key Laboratory of Hydroelectric Machinery Design & Maintenance, China Three Gorges University, Yichang, China
- *Correspondence: Bo Tao, ; Baojia Chen,
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30
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Girdhar N, Sinha A, Gupta S. DenseNet-II: an improved deep convolutional neural network for melanoma cancer detection. Soft comput 2022; 27:1-20. [PMID: 36034768 PMCID: PMC9400005 DOI: 10.1007/s00500-022-07406-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/16/2022] [Indexed: 10/28/2022]
Abstract
Research in the field of medicine and relevant studies evince that melanoma is one of the deadliest cancers. It defines precisely that the condition develops due to uncontrolled growth of melanocytic cells. The current trends in any disease detection revolve around the usage of two main categories of models; these are general machine learning models and deep learning models. Further, the experimental analysis of melanoma has an additional requirement of visual records like dermatological scans or normal camera lens images. This further accentuates the need for a more accurate model for melanoma detection. In this work, we aim to achieve the same, primarily by the extensive usage of neural networks. Our objective is to propose a deep learning CNN framework-based model to improve the accuracy of melanoma detection by customizing the number of layers in the network architecture, activation functions applied, and the dimension of the input array. Models like Resnet, DenseNet, Inception, and VGG have proved to yield appreciable accuracy in melanoma detection. However, in most cases, the dataset was classified into malignant or benign classes only. The dataset used in our research provides seven lesions; these are melanocytic nevi, melanoma, benign keratosis, basal cell carcinoma, actinic keratoses, vascular lesions, and dermatofibroma. Thus, through the HAM10000 dataset and various deep learning models, we diversified the precision factors as well as input qualities. The obtained results are highly propitious and establish its credibility.
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Affiliation(s)
- Nancy Girdhar
- School of Computer Science Engineering and Technology, Bennett University, Greater Noida, UP India
| | - Aparna Sinha
- Amity School of Engineering and Technology, Amity University, Noida, UP India
| | - Shivang Gupta
- Amity School of Engineering and Technology, Amity University, Noida, UP India
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31
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Application of Pre-Trained Deep Convolutional Neural Networks for Coffee Beans Species Detection. FOOD ANAL METHOD 2022. [DOI: 10.1007/s12161-022-02362-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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32
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Jena M, Mishra D, Mishra SP, Mallick PK. A Tailored Complex Medical Decision Analysis Model for Diabetic Retinopathy Classification Based on Optimized Un-Supervised Feature Learning Approach. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2022. [DOI: 10.1007/s13369-022-07057-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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33
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Skandha SS, Agarwal M, Utkarsh K, Gupta SK, Koppula VK, Suri JS. A novel genetic algorithm-based approach for compression and acceleration of deep learning convolution neural network: an application in computer tomography lung cancer data. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07567-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Ahsan MA, Qayyum A, Razi A, Qadir J. An active learning method for diabetic retinopathy classification with uncertainty quantification. Med Biol Eng Comput 2022; 60:2797-2811. [PMID: 35859243 DOI: 10.1007/s11517-022-02633-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Accepted: 06/24/2022] [Indexed: 02/04/2023]
Abstract
In recent years, deep learning (DL) techniques have provided state-of-the-art performance in medical imaging. However, good quality (annotated) medical data is in general hard to find due to the usually high cost of medical images, limited availability of expert annotators (e.g., radiologists), and the amount of time required for annotation. In addition, DL is data-hungry and its training requires extensive computational resources. Furthermore, DL being a black-box method lacks transparency on its inner working and lacks fundamental understanding behind decisions made by the model and consequently, this notion enhances the uncertainty on its predictions. To this end, we address these challenges by proposing a hybrid model, which uses a Bayesian convolutional neural network (BCNN) for uncertainty quantification, and an active learning approach for annotating the unlabeled data. The BCNN is used as a feature descriptor and these features are then used for training a model, in an active learning setting. We evaluate the proposed framework for diabetic retinopathy classification problem and demonstrate state-of-the-art performance in terms of different metrics.
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Affiliation(s)
| | - Adnan Qayyum
- Information Technology University, Lahore, Pakistan
| | - Adeel Razi
- Turner Institute for Brain and Mental Health, Monash University, Clayton, Australia.,Wellcome Centre for Human Neuroimaging, London, UK.,CIFAR Azrieli Global Scholars Program, CIFAR, Toronto, Canada
| | - Junaid Qadir
- Department of Computer Science and Engineering, Faculty of Engineering, Qatar University, Doha, Qatar
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35
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Guanglong B, Qian G. Correlation Analysis between the Emotion and Aesthetics for Chinese Classical Garden Design Based on Deep Transfer Learning. JOURNAL OF ENVIRONMENTAL AND PUBLIC HEALTH 2022; 2022:1828782. [PMID: 35855813 PMCID: PMC9288283 DOI: 10.1155/2022/1828782] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 06/10/2022] [Accepted: 06/14/2022] [Indexed: 11/18/2022]
Abstract
Garden design with healthy psychological characteristics is a design method that mines positive psychological expressions and converts them into garden design elements. Chinese classical gardens are cultural heritage of China. Studying the beauty of space in classical gardens is of great significance to inheriting traditional culture, traditional art, and traditional aesthetics. At present, the research hotspots of garden design with healthy psychological characteristics mainly focus on the construction of relevant research theories and methods with the help of various intelligent tools. In this study, we propose a deep learning-based end-to-end model to recognize the positive psychological design of a Chinese classical garden. The model is designed based on Inception V3 that is proposed by Google. The innovation lies in that transfer learning which is integrated into Inception V3 to improve the generalization ability. Also, it is not necessary to encode the characteristics of the garden design style due to the end-to-end structure used in our proposed model. We design a positive psychological characteristics classification task to recognize high aesthetic feeling and low aesthetic feeling of rockery design. Experimental results indicate that our proposed model wins the best performance compared with other comparison models.
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Affiliation(s)
- Bao Guanglong
- College of Fine Arts and Design, YangZhou University, Yangzhou 225000, Jiangsu, China
| | - Gao Qian
- College of Fine Arts and Design, YangZhou University, Yangzhou 225000, Jiangsu, China
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PCA-Based Incremental Extreme Learning Machine (PCA-IELM) for COVID-19 Patient Diagnosis Using Chest X-Ray Images. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:9107430. [PMID: 35800685 PMCID: PMC9253873 DOI: 10.1155/2022/9107430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Accepted: 04/29/2022] [Indexed: 11/24/2022]
Abstract
Novel coronavirus 2019 has created a pandemic and was first reported in December 2019. It has had very adverse consequences on people's daily life, healthcare, and the world's economy as well. According to the World Health Organization's most recent statistics, COVID-19 has become a worldwide pandemic, and the number of infected persons and fatalities growing at an alarming rate. It is highly required to have an effective system to early detect the COVID-19 patients to curb the further spreading of the virus from the affected person. Therefore, to early identify positive cases in patients and to support radiologists in the automatic diagnosis of COVID-19 from X-ray images, a novel method PCA-IELM is proposed based on principal component analysis (PCA) and incremental extreme learning machine. The suggested method's key addition is that it considers the benefits of PCA and the incremental extreme learning machine. Further, our strategy PCA-IELM reduces the input dimension by extracting the most important information from an image. Consequently, the technique can effectively increase the COVID-19 patient prediction performance. In addition to these, PCA-IELM has a faster training speed than a multi-layer neural network. The proposed approach was tested on a COVID-19 patient's chest X-ray image dataset. The experimental results indicate that the proposed approach PCA-IELM outperforms PCA-SVM and PCA-ELM in terms of accuracy (98.11%), precision (96.11%), recall (97.50%), F1-score (98.50%), etc., and training speed.
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37
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Bethell EJ, Khan W, Hussain A. A deep transfer learning model for head pose estimation in rhesus macaques during cognitive tasks: towards a nonrestraint noninvasive 3Rs approach. Appl Anim Behav Sci 2022. [DOI: 10.1016/j.applanim.2022.105708] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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38
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Mehmood M, Alshammari N, Alanazi SA, Basharat A, Ahmad F, Sajjad M, Junaid K. Improved colorization and classification of intracranial tumor expanse in MRI images via hybrid scheme of Pix2Pix-cGANs and NASNet-large. JOURNAL OF KING SAUD UNIVERSITY - COMPUTER AND INFORMATION SCIENCES 2022. [DOI: 10.1016/j.jksuci.2022.05.015] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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COVLIAS 2.0-cXAI: Cloud-Based Explainable Deep Learning System for COVID-19 Lesion Localization in Computed Tomography Scans. Diagnostics (Basel) 2022; 12:diagnostics12061482. [PMID: 35741292 PMCID: PMC9221733 DOI: 10.3390/diagnostics12061482] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 06/07/2022] [Accepted: 06/13/2022] [Indexed: 02/07/2023] Open
Abstract
Background: The previous COVID-19 lung diagnosis system lacks both scientific validation and the role of explainable artificial intelligence (AI) for understanding lesion localization. This study presents a cloud-based explainable AI, the “COVLIAS 2.0-cXAI” system using four kinds of class activation maps (CAM) models. Methodology: Our cohort consisted of ~6000 CT slices from two sources (Croatia, 80 COVID-19 patients and Italy, 15 control patients). COVLIAS 2.0-cXAI design consisted of three stages: (i) automated lung segmentation using hybrid deep learning ResNet-UNet model by automatic adjustment of Hounsfield units, hyperparameter optimization, and parallel and distributed training, (ii) classification using three kinds of DenseNet (DN) models (DN-121, DN-169, DN-201), and (iii) validation using four kinds of CAM visualization techniques: gradient-weighted class activation mapping (Grad-CAM), Grad-CAM++, score-weighted CAM (Score-CAM), and FasterScore-CAM. The COVLIAS 2.0-cXAI was validated by three trained senior radiologists for its stability and reliability. The Friedman test was also performed on the scores of the three radiologists. Results: The ResNet-UNet segmentation model resulted in dice similarity of 0.96, Jaccard index of 0.93, a correlation coefficient of 0.99, with a figure-of-merit of 95.99%, while the classifier accuracies for the three DN nets (DN-121, DN-169, and DN-201) were 98%, 98%, and 99% with a loss of ~0.003, ~0.0025, and ~0.002 using 50 epochs, respectively. The mean AUC for all three DN models was 0.99 (p < 0.0001). The COVLIAS 2.0-cXAI showed 80% scans for mean alignment index (MAI) between heatmaps and gold standard, a score of four out of five, establishing the system for clinical settings. Conclusions: The COVLIAS 2.0-cXAI successfully showed a cloud-based explainable AI system for lesion localization in lung CT scans.
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Abstract
With the continuous miniaturization of conventional integrated circuits, obstacles such as excessive cost, increased resistance to electronic motion, and increased energy consumption are gradually slowing down the development of electrical computing and constraining the application of deep learning. Optical neuromorphic computing presents various opportunities and challenges compared with the realm of electronics. Algorithms running on optical hardware have the potential to meet the growing computational demands of deep learning and artificial intelligence. Here, we review the development of optical neural networks and compare various research proposals. We focus on fiber-based neural networks. Finally, we describe some new research directions and challenges.
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41
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Neto CMV, Honorio LG, de Aguiar EP. Semi-supervised deep rule-based approach for the classification of Wagon Bogie springs condition. EVOLVING SYSTEMS 2022. [DOI: 10.1007/s12530-022-09440-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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42
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Suri JS, Agarwal S, Chabert GL, Carriero A, Paschè A, Danna PSC, Saba L, Mehmedović A, Faa G, Singh IM, Turk M, Chadha PS, Johri AM, Khanna NN, Mavrogeni S, Laird JR, Pareek G, Miner M, Sobel DW, Balestrieri A, Sfikakis PP, Tsoulfas G, Protogerou AD, Misra DP, Agarwal V, Kitas GD, Teji JS, Al-Maini M, Dhanjil SK, Nicolaides A, Sharma A, Rathore V, Fatemi M, Alizad A, Krishnan PR, Nagy F, Ruzsa Z, Fouda MM, Naidu S, Viskovic K, Kalra MK. COVLIAS 1.0 Lesion vs. MedSeg: An Artificial Intelligence Framework for Automated Lesion Segmentation in COVID-19 Lung Computed Tomography Scans. Diagnostics (Basel) 2022; 12:diagnostics12051283. [PMID: 35626438 PMCID: PMC9141749 DOI: 10.3390/diagnostics12051283] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2022] [Revised: 05/18/2022] [Accepted: 05/19/2022] [Indexed: 02/01/2023] Open
Abstract
Background: COVID-19 is a disease with multiple variants, and is quickly spreading throughout the world. It is crucial to identify patients who are suspected of having COVID-19 early, because the vaccine is not readily available in certain parts of the world. Methodology: Lung computed tomography (CT) imaging can be used to diagnose COVID-19 as an alternative to the RT-PCR test in some cases. The occurrence of ground-glass opacities in the lung region is a characteristic of COVID-19 in chest CT scans, and these are daunting to locate and segment manually. The proposed study consists of a combination of solo deep learning (DL) and hybrid DL (HDL) models to tackle the lesion location and segmentation more quickly. One DL and four HDL models—namely, PSPNet, VGG-SegNet, ResNet-SegNet, VGG-UNet, and ResNet-UNet—were trained by an expert radiologist. The training scheme adopted a fivefold cross-validation strategy on a cohort of 3000 images selected from a set of 40 COVID-19-positive individuals. Results: The proposed variability study uses tracings from two trained radiologists as part of the validation. Five artificial intelligence (AI) models were benchmarked against MedSeg. The best AI model, ResNet-UNet, was superior to MedSeg by 9% and 15% for Dice and Jaccard, respectively, when compared against MD 1, and by 4% and 8%, respectively, when compared against MD 2. Statistical tests—namely, the Mann−Whitney test, paired t-test, and Wilcoxon test—demonstrated its stability and reliability, with p < 0.0001. The online system for each slice was <1 s. Conclusions: The AI models reliably located and segmented COVID-19 lesions in CT scans. The COVLIAS 1.0Lesion lesion locator passed the intervariability test.
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Affiliation(s)
- Jasjit S. Suri
- Stroke Diagnostic and Monitoring Division, AtheroPoint™, Roseville, CA 95661, USA; (I.M.S.); (P.S.C.)
- Advanced Knowledge Engineering Centre, GBTI, Roseville, CA 95661, USA;
- Correspondence: ; Tel.: +1-(916)-749-5628
| | - Sushant Agarwal
- Advanced Knowledge Engineering Centre, GBTI, Roseville, CA 95661, USA;
- Department of Computer Science Engineering, PSIT, Kanpur 209305, India
| | - Gian Luca Chabert
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 09124 Cagliari, Italy; (G.L.C.); (A.P.); (P.S.C.D.); (L.S.); (A.B.)
| | - Alessandro Carriero
- Department of Radiology, “Maggiore della Carità” Hospital, University of Piemonte Orientale (UPO), Via Solaroli 17, 28100 Novara, Italy;
| | - Alessio Paschè
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 09124 Cagliari, Italy; (G.L.C.); (A.P.); (P.S.C.D.); (L.S.); (A.B.)
| | - Pietro S. C. Danna
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 09124 Cagliari, Italy; (G.L.C.); (A.P.); (P.S.C.D.); (L.S.); (A.B.)
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 09124 Cagliari, Italy; (G.L.C.); (A.P.); (P.S.C.D.); (L.S.); (A.B.)
| | - Armin Mehmedović
- University Hospital for Infectious Diseases, 10000 Zagreb, Croatia; (A.M.); (K.V.)
| | - Gavino Faa
- Department of Pathology, Azienda Ospedaliero Universitaria (A.O.U.), 09124 Cagliari, Italy;
| | - Inder M. Singh
- Stroke Diagnostic and Monitoring Division, AtheroPoint™, Roseville, CA 95661, USA; (I.M.S.); (P.S.C.)
| | - Monika Turk
- The Hanse-Wissenschaftskolleg Institute for Advanced Study, 27753 Delmenhorst, Germany;
| | - Paramjit S. Chadha
- Stroke Diagnostic and Monitoring Division, AtheroPoint™, Roseville, CA 95661, USA; (I.M.S.); (P.S.C.)
| | - Amer M. Johri
- Department of Medicine, Division of Cardiology, Queen’s University, Kingston, ON K7L 3N6, Canada;
| | - Narendra N. Khanna
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi 110076, India;
| | - Sophie Mavrogeni
- Cardiology Clinic, Onassis Cardiac Surgery Center, 17674 Athens, Greece;
| | - John R. Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St Helena, CA 94574, USA;
| | - Gyan Pareek
- Minimally Invasive Urology Institute, Brown University, Providence, RI 02912, USA; (G.P.); (D.W.S.)
| | - Martin Miner
- Men’s Health Center, Miriam Hospital, Providence, RI 02906, USA;
| | - David W. Sobel
- Minimally Invasive Urology Institute, Brown University, Providence, RI 02912, USA; (G.P.); (D.W.S.)
| | - Antonella Balestrieri
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 09124 Cagliari, Italy; (G.L.C.); (A.P.); (P.S.C.D.); (L.S.); (A.B.)
| | - Petros P. Sfikakis
- Rheumatology Unit, National Kapodistrian University of Athens, 15772 Athens, Greece;
| | - George Tsoulfas
- Department of Surgery, Aristoteleion University of Thessaloniki, 54124 Thessaloniki, Greece;
| | - Athanasios D. Protogerou
- Cardiovascular Prevention and Research Unit, Department of Pathophysiology, National & Kapodistrian University of Athens, 15772 Athens, Greece;
| | - Durga Prasanna Misra
- Department of Immunology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow 226014, India; (D.P.M.); (V.A.)
| | - Vikas Agarwal
- Department of Immunology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow 226014, India; (D.P.M.); (V.A.)
| | - George D. Kitas
- Academic Affairs, Dudley Group NHS Foundation Trust, Dudley DY1 2HQ, UK;
- Arthritis Research UK Epidemiology Unit, Manchester University, Manchester M13 9PL, UK
| | - Jagjit S. Teji
- Ann and Robert H. Lurie Children’s Hospital of Chicago, Chicago, IL 60611, USA;
| | - Mustafa Al-Maini
- Allergy, Clinical Immunology and Rheumatology Institute, Toronto, ON L4Z 4C4, Canada;
| | | | - Andrew Nicolaides
- Vascular Screening and Diagnostic Centre, University of Nicosia Medical School, Nicosia 2408, Cyprus;
| | - Aditya Sharma
- Division of Cardiovascular Medicine, University of Virginia, Charlottesville, VA 22908, USA;
| | - Vijay Rathore
- AtheroPoint LLC, Roseville, CA 95661, USA; (S.K.D.); (V.R.)
| | - Mostafa Fatemi
- Department of Physiology and Biomedical Engineering, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA;
| | - Azra Alizad
- Department of Radiology, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA;
| | | | - Ferenc Nagy
- Internal Medicine Department, University of Szeged, 6725 Szeged, Hungary;
| | - Zoltan Ruzsa
- Invasive Cardiology Division, University of Szeged, 6725 Szeged, Hungary;
| | - Mostafa M. Fouda
- Department of Electrical and Computer Engineering, Idaho State University, Pocatello, ID 83209, USA;
| | - Subbaram Naidu
- Electrical Engineering Department, University of Minnesota, Duluth, MN 55812, USA;
| | - Klaudija Viskovic
- University Hospital for Infectious Diseases, 10000 Zagreb, Croatia; (A.M.); (K.V.)
| | - Manudeep K. Kalra
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA;
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Raj R, Mathew J, Kannath SK, Rajan J. Crossover based technique for data augmentation. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 218:106716. [PMID: 35290901 DOI: 10.1016/j.cmpb.2022.106716] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 02/19/2022] [Accepted: 02/26/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND AND OBJECTIVE Medical image classification problems are frequently constrained by the availability of datasets. "Data augmentation" has come as a data enhancement and data enrichment solution to the challenge of limited data. Traditionally data augmentation techniques are based on linear and label preserving transformations; however, recent works have demonstrated that even non-linear, non-label preserving techniques can be unexpectedly effective. This paper proposes a non-linear data augmentation technique for the medical domain and explores its results. METHODS This paper introduces "Crossover technique", a new data augmentation technique for Convolutional Neural Networks in Medical Image Classification problems. Our technique synthesizes a pair of samples by applying two-point crossover on the already available training dataset. By this technique, we create N new samples from N training samples. The proposed crossover based data augmentation technique, although non-label preserving, has performed significantly better in terms of increased accuracy and reduced loss for all the tested datasets over varied architectures. RESULTS The proposed method was tested on three publicly available medical datasets with various network architectures. For the mini-MIAS database of mammograms, our method improved the accuracy by 1.47%, achieving 80.15% using VGG-16 architecture. Our method works fine for both gray-scale as well as RGB images, as on the PH2 database for Skin Cancer, it improved the accuracy by 3.57%, achieving 85.71% using VGG-19 architecture. In addition, our technique improved accuracy on the brain tumor dataset by 0.40%, achieving 97.97% using VGG-16 architecture. CONCLUSION The proposed novel crossover technique for training the Convolutional Neural Network (CNN) is painless to implement by applying two-point crossover on two images to form new images. The method would go a long way in tackling the challenges of limited datasets and problems of class imbalances in medical image analysis. Our code is available at https://github.com/rishiraj-cs/Crossover-augmentation.
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Affiliation(s)
- Rishi Raj
- Department of Computer Science and Engineering, Indian Institute of Technology Patna, India.
| | - Jimson Mathew
- Department of Computer Science and Engineering, Indian Institute of Technology Patna, India
| | - Santhosh Kumar Kannath
- Department of Imaging Sciences and Interventional Radiology, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Kerala, India
| | - Jeny Rajan
- Department of Computer Science and Engineering, National Institute of Technology Karnataka, India
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Ibrahim DA, Zebari DA, Mohammed HJ, Mohammed MA. Effective hybrid deep learning model for COVID-19 patterns identification using CT images. EXPERT SYSTEMS 2022; 39:e13010. [PMID: 35942177 PMCID: PMC9348188 DOI: 10.1111/exsy.13010] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/12/2021] [Revised: 03/08/2022] [Accepted: 03/23/2022] [Indexed: 05/31/2023]
Abstract
Coronavirus disease 2019 (COVID-19) has attracted significant attention of researchers from various disciplines since the end of 2019. Although the global epidemic situation is stabilizing due to vaccination, new COVID-19 cases are constantly being discovered around the world. As a result, lung computed tomography (CT) examination, an aggregated identification technique, has been used to ameliorate diagnosis. It helps reveal missed diagnoses due to the ambiguity of nucleic acid polymerase chain reaction. Therefore, this study investigated how quickly and accurately hybrid deep learning (DL) methods can identify infected individuals with COVID-19 on the basis of their lung CT images. In addition, this study proposed a developed system to create a reliable COVID-19 prediction network using various layers starting with the segmentation of the lung CT scan image and ending with disease prediction. The initial step of the system starts with a proposed technique for lung segmentation that relies on a no-threshold histogram-based image segmentation method. Afterward, the GrabCut method was used as a post-segmentation method to enhance segmentation outcomes and avoid over-and under-segmentation problems. Then, three pre-trained models of standard DL methods, including Visual Geometry Group Network, convolutional deep belief network, and high-resolution network, were utilized to extract the most affective features from the segmented images that can help to identify COVID-19. These three described pre-trained models were combined as a new mechanism to increase the system's overall prediction capabilities. A publicly available dataset, namely, COVID-19 CT, was used to test the performance of the proposed model, which obtained a 95% accuracy rate. On the basis of comparison, the proposed model outperformed several state-of-the-art studies. Because of its effectiveness in accurately screening COVID-19 CT images, the developed model will potentially be valuable as an additional diagnostic tool for leading clinical professionals.
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Affiliation(s)
- Dheyaa Ahmed Ibrahim
- Communications Engineering Techniques Department, Information Technology CollageImam Ja'afar Al‐Sadiq UniversityBaghdadIraq
| | - Dilovan Asaad Zebari
- Department of Computer Science, College of ScienceNawroz UniversityDuhok Kurdistan RegionIraq
| | | | - Mazin Abed Mohammed
- Information systems Department, College of Computer Science and Information TechnologyUniversity of AnbarAl AnbarIraq
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Case Study: Development of the CNN Model Considering Teleconnection for Spatial Downscaling of Precipitation in a Climate Change Scenario. SUSTAINABILITY 2022. [DOI: 10.3390/su14084719] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Global climate models (GCMs) are used to analyze future climate change. However, the observed data of a specified region may differ significantly from the model since the GCM data are simulated on a global scale. To solve this problem, previous studies have used downscaling methods such as quantile mapping (QM) to correct bias in GCM precipitation. However, this method cannot be considered when certain variables affect the observation data. Therefore, the aim of this study is to propose a novel method that uses a convolution neural network (CNN) considering teleconnection. This new method considers how the global climate phenomena affect the precipitation data of a target area. In addition, various meteorological variables related to precipitation were used as explanatory variables for the CNN model. In this study, QM and the CNN models were applied to calibrate the spatial bias of GCM data for three precipitation stations in Korea (Incheon, Seoul, and Suwon), and the results were compared. According to the results, the QM method effectively corrected the range of precipitation, but the pattern of precipitation was the same at the three stations. Meanwhile, for the CNN model, the range and pattern of precipitation were corrected better than the QM method. The quantitative evaluation selected the optimal downscaling model, and the CNN model had the best performance (correlation coefficient (CC): 69% on average, root mean squared error (RMSE): 117 mm on average). Therefore, the new method suggested in this study is expected to have high utility in forecasting climate change. Finally, as a result of forecasting for future precipitation in 2100 via the CNN model, the average annual rainfall increased by 17% on average compared to the reference data.
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A Deep Learning Ensemble Method to Visual Acuity Measurement Using Fundus Images. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12063190] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Visual acuity (VA) is a measure of the ability to distinguish shapes and details of objects at a given distance and is a measure of the spatial resolution of the visual system. Vision is one of the basic health indicators closely related to a person’s quality of life. It is one of the first basic tests done when an eye disease develops. VA is usually measured by using a Snellen chart or E-chart from a specific distance. However, in some cases, such as the unconsciousness of patients or diseases, i.e., dementia, it can be impossible to measure the VA using such traditional chart-based methodologies. This paper provides a machine learning-based VA measurement methodology that determines VA only based on fundus images. In particular, the levels of VA, conventionally divided into 11 levels, are grouped into four classes and three machine learning algorithms, one SVM model and two CNN models, are combined into an ensemble method in order to predict the corresponding VA level from a fundus image. Based on a performance evaluation conducted using randomly selected 4000 fundus images, we confirm that our ensemble method can estimate with 82.4% of the average accuracy for four classes of VA levels, in which each class of Class 1 to Class 4 identifies the level of VA with 88.5%, 58.8%, 88%, and 94.3%, respectively. To the best of our knowledge, this is the first paper on VA measurements based on fundus images using deep machine learning.
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Shaik NS, Cherukuri TK. Hinge attention network: A joint model for diabetic retinopathy severity grading. APPL INTELL 2022. [DOI: 10.1007/s10489-021-03043-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/03/2023]
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Cubical Homology-Based Machine Learning: An Application in Image Classification. AXIOMS 2022. [DOI: 10.3390/axioms11030112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Persistent homology is a powerful tool in topological data analysis (TDA) to compute, study, and encode efficiently multi-scale topological features and is being increasingly used in digital image classification. The topological features represent a number of connected components, cycles, and voids that describe the shape of data. Persistent homology extracts the birth and death of these topological features through a filtration process. The lifespan of these features can be represented using persistent diagrams (topological signatures). Cubical homology is a more efficient method for extracting topological features from a 2D image and uses a collection of cubes to compute the homology, which fits the digital image structure of grids. In this research, we propose a cubical homology-based algorithm for extracting topological features from 2D images to generate their topological signatures. Additionally, we propose a novel score measure, which measures the significance of each of the sub-simplices in terms of persistence. In addition, gray-level co-occurrence matrix (GLCM) and contrast limited adapting histogram equalization (CLAHE) are used as supplementary methods for extracting features. Supervised machine learning models are trained on selected image datasets to study the efficacy of the extracted topological features. Among the eight tested models with six published image datasets of varying pixel sizes, classes, and distributions, our experiments demonstrate that cubical homology-based machine learning with the deep residual network (ResNet 1D) and Light Gradient Boosting Machine (lightGBM) shows promise with the extracted topological features.
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Nisha J, P. Gopi V, Palanisamy P. Automated colorectal polyp detection based on image enhancement and dual-path CNN architecture. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103465] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
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Segmentation and Classification of Glaucoma Using U-Net with Deep Learning Model. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:1601354. [PMID: 35222876 PMCID: PMC8866016 DOI: 10.1155/2022/1601354] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Revised: 01/07/2022] [Accepted: 01/12/2022] [Indexed: 11/17/2022]
Abstract
Glaucoma is the second most common cause for blindness around the world and the third most common in Europe and the USA. Around 78 million people are presently living with glaucoma (2020). It is expected that 111.8 million people will have glaucoma by the year 2040. 90% of glaucoma is undetected in developing nations. It is essential to develop a glaucoma detection system for early diagnosis. In this research, early prediction of glaucoma using deep learning technique is proposed. In this proposed deep learning model, the ORIGA dataset is used for the evaluation of glaucoma images. The U-Net architecture based on deep learning algorithm is implemented for optic cup segmentation and a pretrained transfer learning model; DenseNet-201 is used for feature extraction along with deep convolution neural network (DCNN). The DCNN approach is used for the classification, where the final results will be representing whether the glaucoma infected or not. The primary objective of this research is to detect the glaucoma using the retinal fundus images, which can be useful to determine if the patient was affected by glaucoma or not. The result of this model can be positive or negative based on the outcome detected as infected by glaucoma or not. The model is evaluated using parameters such as accuracy, precision, recall, specificity, and F-measure. Also, a comparative analysis is conducted for the validation of the model proposed. The output is compared to other current deep learning models used for CNN classification, such as VGG-19, Inception ResNet, ResNet 152v2, and DenseNet-169. The proposed model achieved 98.82% accuracy in training and 96.90% in testing. Overall, the performance of the proposed model is better in all the analysis.
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