Intelligent Deep Learning Based Multi-Retinal Disease Diagnosis and Classification Framework

In past decades, retinal diseases have become more common and affect people of all age grounds over the globe. For examining retinal eye disease, an artificial intelligence (AI) based multilabel classification model is needed for automated diagnosis. To analyze the retinal malady, the system proposes a multiclass and multi-label arrangement method. Therefore, the classification frameworks based on features are explicitly described by ophthalmologists under the application of domain knowledge, which tends to be time-consuming, vulnerable generalization ability, and unfeasible in massive datasets. Therefore, the automated diagnosis of multi-retinal diseases becomes essential, which can be solved by the deep learning (DL) models. With this motivation, this paper presents an intelligent deep learning-based multi-retinal disease diagnosis (IDL-MRDD) framework using fundus images. The proposed model aims to classify the color fundus images into different classes namely AMD, DR, Glaucoma, Hypertensive Retinopathy, Normal, Others, and Pathological Myopia. Besides, the artificial flora algorithm with Shannon's function (AFA-SF) based multi-level thresholding technique is employed for image segmentation and thereby the infected regions can be properly detected. In addition, SqueezeNet based feature extractor is employed to generate a collection of feature vectors. Finally, the stacked sparse Autoencoder (SSAE) model is applied as a classifier to distinguish the input images into distinct retinal diseases. The efficacy of the IDL-MRDD technique is carried out on a benchmark multi-retinal disease dataset, comprising data instances from different classes. The experimental values pointed out the superior outcome over the existing techniques with the maximum accuracy of 0.963.