Deep Learning based Computer Aided Diagnosis for Diabetic Retinopathy

Proposed Method Stage 1

Diabetic Retinopathy (DR) is considered as a deadly disease which significantly affects a number of diabetic patients and end up in their vision loss. The proposed model incorporates different processes namely data collection, preprocessing, segmentation, feature extraction and classification. At first, the IoT-based data collection process takes place and the contrast level of the input DR image undergoes preprocessing by following Contrast Limited Adaptive Histogram Equalization (CLAHE) model. Next, the preprocessed image is segmented using Adaptive Spatial Kernel distance measure-based Fuzzy c-means clustering (ASKFCM) model. Afterwards, deep Convolution Neural Network (CNN) is applied as a feature extractor and the resulting feature vectors undergo classification in line with the Gaussian Naïve Bayes (GNB) model. The proposed model was tested using a benchmark DR MESSIDOR image dataset and the obtained results showcased superior performance of the proposed model over other such models compared in the study.

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