FuNet-40: fundus disease/abnormality classification using ensemble of fine-tuned pretrained convolution models
Age-related macular diseases (AMD) are common reason for visual impairment in humans. These anomalies can result from a variety of illnesses and disorders. Currently, skilled medical professionals make this diagnosis by visually inspecting the pictures. The study of ophthalmology is moving towards t...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2024-12-01
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| Series: | Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/21681163.2024.2422401 |
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| Summary: | Age-related macular diseases (AMD) are common reason for visual impairment in humans. These anomalies can result from a variety of illnesses and disorders. Currently, skilled medical professionals make this diagnosis by visually inspecting the pictures. The study of ophthalmology is moving towards the creation of a computer-aided diagnosis (CAD) system for the identification of ocular disorders and diseases. It is crucial to classify these illnesses as accurately and without any false-negatives as possible. However, a person’s retina could be impacted by a number of fundus problems. So, for the purpose of classifying these diseases and pathologies into multiple categories, we study a case with 40 possible class labels. Random Forest was found to be 72.57% accurate based on global features extracted from the dataset and an analysis of the performance of several machine learning models. The dataset was then trained using a few user-defined and pretrained models, and it was found that EfficientNet B1 outperformed all other deep learning models in terms of test accuracy (90.2%), precision (0.993), recall (0.992), F1 score (0.8737), and (=0.2) score. All the models were trained on a set of 1166 images, validated on 250 images, and tested on 250 images |
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| ISSN: | 2168-1163 2168-1171 |