Lightweight Convolutional Neural Networks for Retinal Disease Classification
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arXiv
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| Format: | Preprint |
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2025
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| author | Qasim, Duaa Kareem Jebur, Sabah Abdulazeez Ali, Lafta Raheem Khalaf, Abdul Jalil M. Hussain, Abir Jaafar |
| author_facet | Qasim, Duaa Kareem Jebur, Sabah Abdulazeez Ali, Lafta Raheem Khalaf, Abdul Jalil M. Hussain, Abir Jaafar |
| contents | Retinal diseases such as Diabetic Retinopathy (DR) and Macular Hole (MH) significantly impact vision and affect millions worldwide. Early detection is crucial, as DR, a complication of diabetes, damages retinal blood vessels, potentially leading to blindness, while MH disrupts central vision, affecting tasks like reading and facial recognition. This paper employed two lightweight and efficient Convolution Neural Network architectures, MobileNet and NASNetMobile, for the classification of Normal, DR, and MH retinal images. The models were trained on the RFMiD dataset, consisting of 3,200 fundus images, after undergoing preprocessing steps such as resizing, normalization, and augmentation. To address data scarcity, this study leveraged transfer learning and data augmentation techniques, enhancing model generalization and performance. The experimental results demonstrate that MobileNetV2 achieved the highest accuracy of 90.8%, outperforming NASNetMobile, which achieved 89.5% accuracy. These findings highlight the effectiveness of CNNs in retinal disease classification, providing a foundation for AI-assisted ophthalmic diagnosis and early intervention. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_03186 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Lightweight Convolutional Neural Networks for Retinal Disease Classification Qasim, Duaa Kareem Jebur, Sabah Abdulazeez Ali, Lafta Raheem Khalaf, Abdul Jalil M. Hussain, Abir Jaafar Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning Neural and Evolutionary Computing Retinal diseases such as Diabetic Retinopathy (DR) and Macular Hole (MH) significantly impact vision and affect millions worldwide. Early detection is crucial, as DR, a complication of diabetes, damages retinal blood vessels, potentially leading to blindness, while MH disrupts central vision, affecting tasks like reading and facial recognition. This paper employed two lightweight and efficient Convolution Neural Network architectures, MobileNet and NASNetMobile, for the classification of Normal, DR, and MH retinal images. The models were trained on the RFMiD dataset, consisting of 3,200 fundus images, after undergoing preprocessing steps such as resizing, normalization, and augmentation. To address data scarcity, this study leveraged transfer learning and data augmentation techniques, enhancing model generalization and performance. The experimental results demonstrate that MobileNetV2 achieved the highest accuracy of 90.8%, outperforming NASNetMobile, which achieved 89.5% accuracy. These findings highlight the effectiveness of CNNs in retinal disease classification, providing a foundation for AI-assisted ophthalmic diagnosis and early intervention. |
| title | Lightweight Convolutional Neural Networks for Retinal Disease Classification |
| topic | Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2506.03186 |