Diabetic retinopathy (DR) is a major reason for the increased visual loss globally, and it became an important cause of visual impairment among people in 25-74 years of age. The DR significantly affects the economic status in society, particularly in healthcare systems. When timely treatment is provided to the DR patients, approximately 90% of patients can be saved from visual loss. Therefore, it becomes highly essential to classify the stages and severity of DR for the recommendation of required treatments. In this view, this paper introduces a new automated Hyperparameter Tuning Inception-v4 (HPTI-v4) model for the detection and classification of DR from color fundus images. At the preprocessing stage, the contrast level of the fundus image will be improved by the use of contrast limited adaptive histogram equalization (CLAHE) model. Then, the segmentation of the preprocessed image takes place utilizing a histogram-based segmentation model. Afterward, the HPTI-v4 model is applied to extract the required features from the segmented image and it subsequently undergoes classification by the use of a multilayer perceptron (MLP). A series of experiments take place on MESSIDOR (Methods to Evaluate Segmentation and Indexing Techniques in the field of Retinal Ophthalmology) DR dataset to guarantee the goodness of the HPTI-v4 approach and the obtained results clearly exhibited the supremacy of the HPTI-v4 model over the compared methods in a significant way.

Hyperparameter Tuning Deep Learning for Diabetic Retinopathy Fundus Image Classification