GastroNet: a robust attention-based deep learning and cosine similarity feature selection framework for gastrointestinal disease classification from endoscopic images

Diseases of the Gastrointestinal (GI) tract significantly affect the quality of human life and have a high fatality rate. Accurate diagnosis of GI diseases plays a pivotal role in healthcare systems. However, processing large amounts of medical image data can be challenging for radiologists and other medical professionals, increasing the risk of inaccurate medical assessments. Computer-aided Diagnosis systems provide help to doctors for rapid and accurate diagnosis, thus resulting in saving lives. Recently, many techniques are found in the literature that uses deep Convolutional Neural Network (CNN) models for accurate disease classification. However, they have limitations in their ability to detect deformation-invariant features and lack robustness. The diseased region is highlighted, using attention-based image generation and superimposition with original images. A lightweight deep CNN model is employed to get significant features. These features are further reduced using a Cosine similarity-based technique. The proposed framework is assessed using the Kvasir dataset. To verify the effectiveness of the proposed framework, vast experiments are conducted. The overall accuracy of 97.68%, 99.02% precision, 96.37% recall, and an F-measure of 97.68% are achieved using the 810 significant features. This reduction in features resulted in a significant reduction in classification time. The robustness of the framework can be observed not only in terms of considerable improvement in accuracy, but also in terms of precision as well as recall, and F-measure.