Early detection of eye diseases is the only solution to receive timely treatment and prevent blindness. Colour fundus photography (CFP) is an effective fundus examination technique. Because of the similarity in the symptoms of eye diseases in the early stages and the difficulty in distinguishing between the type of disease, there is a need for computer-assisted automated diagnostic techniques. This study focuses on classifying an eye disease dataset using hybrid techniques based on feature extraction with fusion methods. Three strategies were designed to classify CFP images for the diagnosis of eye disease. The first method is to classify an eye disease dataset using an Artificial Neural Network (ANN) with features from the MobileNet and DenseNet121 models separately after reducing the high dimensionality and repetitive features using Principal Component Analysis (PCA). The second method is to classify the eye disease dataset using an ANN on the basis of fused features from the MobileNet and DenseNet121 models before and after reducing features. The third method is to classify the eye disease dataset using ANN based on the fused features from the MobileNet and DenseNet121 models separately with handcrafted features. Based on the fused MobileNet and handcrafted features, the ANN attained an AUC of 99.23%, an accuracy of 98.5%, a precision of 98.45%, a specificity of 99.4%, and a sensitivity of 98.75%.

The number of diabetic retinopathy (DR) patients is increasing every year, and this causes a public health problem. Therefore, regular diagnosis of diabetes patients is necessary to avoid the progression of DR stages to advanced stages that lead to blindness. Manual diagnosis requires effort and expertise and is prone to errors and differing expert diagnoses. Therefore, artificial intelligence techniques help doctors make a proper diagnosis and resolve different opinions. This study developed three approaches, each with two systems, for early diagnosis of DR disease progression. All colour fundus images have been subjected to image enhancement and increasing contrast ROI through filters. All features extracted by the DenseNet-121 and AlexNet (Dense-121 and Alex) were fed to the Principal Component Analysis (PCA) method to select important features and reduce their dimensions. The first approach is to DR image analysis for early prediction of DR disease progression by Artificial Neural Network (ANN) with selected, low-dimensional features of Dense-121 and Alex models. The second approach is to DR image analysis for early prediction of DR disease progression is by integrating important and low-dimensional features of Dense-121 and Alex models before and after PCA. The third approach is to DR image analysis for early prediction of DR disease progression by ANN with the radiomic features. The radiomic features are a combination of the features of the CNN models (Dense-121 and Alex) separately with the handcrafted features extracted by Discrete Wavelet Transform (DWT), Local Binary Pattern (LBP), Fuzzy colour histogram (FCH), and Gray Level Co-occurrence Matrix (GLCM) methods. With the radiomic features of the Alex model and the handcrafted features, ANN reached a sensitivity of 97.92%, an AUC of 99.56%, an accuracy of 99.1%, a specificity of 99.4% and a precision of 99.06%.