MultiNet with Transformers: A Model for Cancer Diagnosis Using Images
This work addresses the high cost and impracticality of large-scale cancer image classification for clinical institutions and researchers, though it appears incremental as it builds on existing transformer and multiclass methods.
The paper tackled the problem of early cancer diagnosis from biomedical images by proposing a deep neural network that incorporates transformers into a multiclass framework, achieving more accurate classifications as evaluated on publicly accessible datasets.
Cancer is a leading cause of death in many countries. An early diagnosis of cancer based on biomedical imaging ensures effective treatment and a better prognosis. However, biomedical imaging presents challenges to both clinical institutions and researchers. 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 cost. The cost makes large-scale biological image classification impractical. In this study, we provide unique deep neural network designs for multiclass classification of medical images, in particular cancer images. We incorporated transformers into a multiclass framework to take advantage of data-gathering capability and perform more accurate classifications. We evaluated models on publicly accessible datasets using various measures to ensure the reliability of the models. Extensive assessment metrics suggest this method can be used for a multitude of classification tasks.