Deep CNN-based feature extraction with optimised LSTM for enhanced diabetic retinopathy detection

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ABSTRACT The early detection and treatment of DR have helped the ophthalmologist to treat the affected patients and to reduce vision loss. Computer-aided screening for automatic DR detection in the medical system has consistent detection of lesions in retinal fundus image. To overcome these challenges and to offer timely treatment, this paper aims to develop a novel deep learning-based DR detection. Here, the integration of ‘Optimized Iterative Thresholding (O-IT)’ is adopted for the accurate segmentation of blood vessels. The first novelty of this work is that the thresholding approach is improved by tuning the parameters in the proposed model by developing a hybrid meta-heuristic Shark Smell-Jaya Optimisation (SS-JO) algorithm to enhance the performance of both blood vessel segmentation and classification. CNN is replaced by a deep learning framework termed as optimised ‘Long Short-Term Memory (LSTM)’. The second novelty of this work is that the optimised LSTM is designed in the proposed model by optimising the parameters in LSTM using the implemented SS-JO to reduce the complexity of the network. The accuracy analysis of the implemented SS-JO-CN-LSTM is secured 44%, 29%, 19%, 6%, 4% and 15% improved than SVM, NN, CNN, LSTM, CN-LSTM and FR-CSA-NN+CNN.