Image-Based Cancer Diagnosis Using Novel Deep Neural Networks

Abstract

Cancer is the major cause of death in many nations. This serious illness can only be effectivelytreated if it is diagnosed early. In contrast, biomedical imaging presents challenges to both clinical institutions and researchers. Physiological anomalies are often characterized by modest modifications in individual cells or tissues, making them difficult to detect visually. Physiological anomalies are often characterized by slight abnormalities in individual cells or tissues, making them difficult to detect visually. Traditionally, anomalies are diagnosed by radiologists and pathologists with extensive training. This procedure, however, demands the participation of professionals and incurs a substantial expense, making the classification of a large number of biological images impracticable. In this study, we offer a number of unique deep neural network designs for binary and multiclass classification of medical images, in particular cancer images. We incorporated transformers into our multiclass framework so that we could take use of its data-gathering capability and perform more accurate classifications. The models are evaluated on publicly accessible datasets. Various evaluation measures are used to examine the reliability of the models. Extensive assessment metrics suggest this method can be used for a multitude of classification tasks.