Deep Learning Based Identification and Categorization of Various Phases of Diabetic Retinopathy
Journal Title: International Journal of Innovations in Science and Technology - Year 2024, Vol 6, Issue 2
Abstract
Diabetic Retinopathy is a growing disease that affects the human retina of diabetic patients, leading to loss of vision if left untreated. Early diagnosis and accurate classification of various stages of DR are crucial for immediate intervention and efficient control. Therefore, this study utilizes a Deep Learning (DL) model named Densenet121 to classify different stages of DR. The dataset used in this research contains collections of color fundus images obtained from diabetic patients, labelled with corresponding disease stages. The dataset used was taken from Kaggle; APTOS 2019. Standard metrics such as accuracy, recall, F1-score, and precision are used to measure the effectiveness of the proposed model. The proposed DL based classification model shows encouraging results and has achieved a high level of accuracy across various severity levels. This model offers an automated method for detection and classification of the disease facilitating early diagnosis. Overall, this study advances automated diagnosis to lessen the burden of diabetic retinopathy.
Authors and Affiliations
Reem Jawed, MuhammadAhsan Ansari, Syed Muhammad Shehram Shah, Jawed AhmadUnar, Khair Muhammad Unar, Sammer Zai
Keywords
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