An Adaptable Model for Medical Image Classification Using the Streamlined Attention Mechanism

Abstract

The resurgence of deep learning has improved computer vision by increasing its applicability and scalability for challenges in the real world. Specifically, utilizing attention in computer vision tasks has improved the performance of models to a superior level. Today we need medical diagnostic tools to tackle the immediate needs of the population suffering from Cancer and COVID-19. Thus, an end-to-end screening is tedious and needs validation expertise. But, with the current deep learning methods, it is quite possible to provide a diagnostic tool that can assist doctors and patients with their immediate needs. So, to tackle these real-world problems, we have proposed a method that is implied on 3 diverse standard datasets in the field of medical imagery, which are Skin Lesions, Brain tumors, and COVID-19 classification. To justify the model’s performance, the authors have experimented on 5 diverse data sets ranging from binary class to multi-class. The experimentation has shown that the proposed “streamlined-attention module” is not only capable of producing superior performance in fine-grained visual recognition but also bio-medical imagery. To further justify, we have illustrated Grad-Cam heat map visualizations to the model and show that it can extract the detailed features with proportionate attention. Our results have proved that our methods excel in their performance compared to that of existing methods. It has achieved state-of-the-art accuracy scores on COVID-19 and HAM10000 datasets with a well-guided explainable result. This work represents a significant advancement in the field of medical image processing with clear results, and the authors anticipate that the suggested method will prove to be a useful tool for medical professionals in the detection and identification of diseases like Covid-19 and cancer. This paper provided the best accuracy for COVID-19 multiclass (94.75 ± 1.07) and HAM10000 (94.31 ± 0.91).