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Fu Y, Wei Y, Chen S, Chen C, Zhou R, Li H, Qiu M, Xie J, Huang D. UC-stack: a deep learning computer automatic detection system for diabetic retinopathy classification. Phys Med Biol 2024; 69:045021. [PMID: 38271723 DOI: 10.1088/1361-6560/ad22a1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2023] [Accepted: 01/25/2024] [Indexed: 01/27/2024]
Abstract
Object. The existing diagnostic paradigm for diabetic retinopathy (DR) greatly relies on subjective assessments by medical practitioners utilizing optical imaging, introducing susceptibility to individual interpretation. This work presents a novel system for the early detection and grading of DR, providing an automated alternative to the manual examination.Approach. First, we use advanced image preprocessing techniques, specifically contrast-limited adaptive histogram equalization and Gaussian filtering, with the goal of enhancing image quality and module learning capabilities. Second, a deep learning-based automatic detection system is developed. The system consists of a feature segmentation module, a deep learning feature extraction module, and an ensemble classification module. The feature segmentation module accomplishes vascular segmentation, the deep learning feature extraction module realizes the global feature and local feature extraction of retinopathy images, and the ensemble module performs the diagnosis and classification of DR for the extracted features. Lastly, nine performance evaluation metrics are applied to assess the quality of the model's performance.Main results. Extensive experiments are conducted on four retinal image databases (APTOS 2019, Messidor, DDR, and EyePACS). The proposed method demonstrates promising performance in the binary and multi-classification tasks for DR, evaluated through nine indicators, including AUC and quadratic weighted Kappa score. The system shows the best performance in the comparison of three segmentation methods, two convolutional neural network architecture models, four Swin Transformer structures, and the latest literature methods.Significance. In contrast to existing methods, our system demonstrates superior performance across multiple indicators, enabling accurate screening of DR and providing valuable support to clinicians in the diagnostic process. Our automated approach minimizes the reliance on subjective assessments, contributing to more consistent and reliable DR evaluations.
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Affiliation(s)
- Yong Fu
- The Life Sciences Research Institute of Guangxi Medical University, Nanning, 530021, People's Republic of China
| | - Yuekun Wei
- School of Information and Management, Guangxi Medical University, Nanning, 530021, People's Republic of China
| | - Siying Chen
- School of Basic Medical Sciences, Guangxi Medical University, Nanning, 530021, People's Republic of China
| | - Caihong Chen
- The Life Sciences Research Institute of Guangxi Medical University, Nanning, 530021, People's Republic of China
| | - Rong Zhou
- The Life Sciences Research Institute of Guangxi Medical University, Nanning, 530021, People's Republic of China
| | - Hongjun Li
- The Life Sciences Research Institute of Guangxi Medical University, Nanning, 530021, People's Republic of China
| | - Mochan Qiu
- School of Basic Medical Sciences, Guangxi Medical University, Nanning, 530021, People's Republic of China
| | - Jin Xie
- The Life Sciences Research Institute of Guangxi Medical University, Nanning, 530021, People's Republic of China
| | - Daizheng Huang
- The Life Sciences Research Institute of Guangxi Medical University, Nanning, 530021, People's Republic of 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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Mohammad F, Al Ahmadi S, Al Muhtadi J. Blockchain-Based Deep CNN for Brain Tumor Prediction Using MRI Scans. Diagnostics (Basel) 2023; 13:diagnostics13071229. [PMID: 37046446 PMCID: PMC10093074 DOI: 10.3390/diagnostics13071229] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 03/19/2023] [Accepted: 03/20/2023] [Indexed: 03/30/2023] Open
Abstract
Brain tumors are nonlinear and present with variations in their size, form, and textural variation; this might make it difficult to diagnose them and perform surgical excision using magnetic resonance imaging (MRI) scans. The procedures that are currently available are conducted by radiologists, brain surgeons, and clinical specialists. Studying brain MRIs is laborious, error-prone, and time-consuming, but they nonetheless show high positional accuracy in the case of brain cells. The proposed convolutional neural network model, an existing blockchain-based method, is used to secure the network for the precise prediction of brain tumors, such as pituitary tumors, meningioma tumors, and glioma tumors. MRI scans of the brain are first put into pre-trained deep models after being normalized in a fixed dimension. These structures are altered at each layer, increasing their security and safety. To guard against potential layer deletions, modification attacks, and tempering, each layer has an additional block that stores specific information. Multiple blocks are used to store information, including blocks related to each layer, cloud ledger blocks kept in cloud storage, and ledger blocks connected to the network. Later, the features are retrieved, merged, and optimized utilizing a Genetic Algorithm and have attained a competitive performance compared with the state-of-the-art (SOTA) methods using different ML classifiers.
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