VESSEL LOOP STRUCTURE-AWARE MULTI-GRADE DIABETIC RETINOPATHY CLASSIFICATION USING ENHANCED TCLLKEN

In order to prevent individuals from blindness and blurred vision, timely and accurate Diabetic Retinopathy (DR) diagnosis is essential in the context of the medical field. Therefore, numerous studies have been implemented by a variety of researchers to classify DR using Artificial Intelligence (AI) algorithms and Retinal Fundus Images (RFIs). The fundamental biomarker for recognizing the DR is the vessel loop. However, the conventional works failed to concentrate on investigating the Vessel Loop Structure (VLS) during DR classification, thereby causing poor DR grading. Thus, to upgrade the diagnosis reliability, a significant VLS-aware multi-grade DR classification framework is pivotal. Therefore, This work is driven by the need to develop a more accurate, VLS-aware multi-grade DR classification method using Transfer Complementary Log-Log Karplus-EfficientNet (TCLLKEN). Primarily, the eye-retinal images are gathered and further pre-processed, followed by gray-scale conversion. After that, regions in the gray-scale images are grouped. In the meantime, from the pre-processed images, the Green Channel (GC) is extracted. Subsequently, vessel structure segmentation, vessel graph construction, and VLS extraction are carried out. Lastly, the extracted features, extracted VLS, and pre-processed images are inputted into the proposed TCLLKEN that classifies the multi-grades of the DR efficiently. Overall, through the inclusion of VLS analysis, DR grading accuracy is significantly enhanced across all stages. Hence, highly accurate multi-grade DR classification is achieved by the proposed VLS-aware TCLLKEN model with an accuracy of 98.99%, thus performing better than existing methods.