Eye diseases detection using deep learning with BAM attention module

With the changing lifestyle, a large population suffers from eye diseases such as glaucoma, cataract, and diabetic retinopathy. Therefore, timely detection and classification of the disease are necessary to minimize vision loss, however, it is time taking task and requires various tests and physicians' in-depth analysis. Thus, an accurate automated technique, timely detection, and classification are needed to cope with the aforementioned challenges. Therefore, this study proposes a technique based on an improved deep learning algorithm i.e., SqueezeNet that uses the eye image' features to detect various diseases such as cataract, glaucoma, and diabetic retinopathy simultaneously. In our proposed model, we employed Bottleneck Attention Module (BAM) with SqueezeNet having an additional layer. Our proposed attention module utilizes two different ways and effectively extracts the most representative features and drops the image's background features of eyes which don't take part in the detection of diseases. Moreover, the algorithm is a pre-trained network that doesn't require a huge training set, therefore, the existing dataset i.e., ODIR, cataract, ORIGA, and glaucoma datasets have been utilized for the training and testing. Additionally, cross-validation has been employed using the cataract dataset to assess the performance of the proposed model. The squeezed connections with regularization power help to minimize the overfitting during the training of eye samples training sets. The proposed algorithm is a novel and effective technique to report the successful implementation for the early detection and classification of eye disease images. The algorithm achieved 98.9% accuracy over the testing dataset and 98.1% accuracy over cross-validation. Various experiments have been performed to confirm that our proposed algorithm performs significantly to detect and classify eye diseases than existing state-of-the-art.