• ELUMALAI K
• HARISH V
• HARIHARAN K
• KANNAMMAL A

: Diabetic Eye Disease (DED) is a serious retinal illness that affects diabetics. The timely identification and precise categorizing of multi-class DED in retinal fundus images play a pivotal role in mitigating the risk of vision loss. The development of an effective diagnostic model using retinal fundus images relies significantly on both the quality and quantity of the images. This study proposes a comprehensive approach to enhance and segment retinal fundus images, followed by multi-class classification employing pre-trained and customized Deep Convolutional Neural Network (DCNN) models.The raw retinal fundus dataset was subjected to experimentation using four pre-trained models: ResNet50, VGG-16, Xception, and EfficientNetB7, and the optimal performing model EfficientNetB7 was acquired. Then, image enhancement approaches including the green channel extraction, applying Contrast-Limited Adaptive Histogram Equalization (CLAHE), and illumination correction, were employed on these raw images. Subsequently, image segmentation methods such as the Tyler Coye Algorithm, Otsu thresholding, and Circular Hough Transform are employed to extract essential Region of Interest (ROIs) like optic nerve, Blood Vessels (BV), and the macular region from the raw ocular fundus images.After preprocessing, the model is trained using these images that outperformed the four pre-trained models and the proposed customized DCNN model. The proposed DCNN methodology holds promising results for the Cataract (CA), Diabetic Retinopathy (DR), Glaucoma (GL), and NORMAL detection tasks, achieving accuracies of 96.43%, 98.33%, 97%, and 96%, respectively.

Deep convolutional neural network, diabetic eye diseases, image enhancement, image segmentation, retinal fundus images.

"Identifying eye disease and predict onset other health conditions using artificial intelligence and machine learning ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.o552-o558, April-2024, Available :http://www.jetir.org/papers/JETIR2404F77.pdf

"Identifying eye disease and predict onset other health conditions using artificial intelligence and machine learning ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppo552-o558, April-2024, Available at : http://www.jetir.org/papers/JETIR2404F77.pdf