An intelligent cancer image classification framework using region-vision transformer-based adaptive multiscale EfficientNetB7 with gated recurrent unit layer

Cancer is a harmful disorder, so detecting cancer in the beginning stages helps to save human life. But, detecting the type of cancer by manual methods is difficult. Conventional techniques perform separate procedures for feature learning and dimensionality reduction. However, these processes need more time. An adaptive deep learning approach is developed to resolve the limitations of the traditional cancer image classification process. The required images for performing the classification process are gathered. After collecting the images, they are directly passed to the Region-Vision Transformer-based Adaptive Multi-scale EfficientNetB7 with Gated Recurrent Unit Layer (RViT-AMEB7-GRU) for cancer image classification, in which the attributes of the RViT-AMEB7-GRU are tuned using the Updated Random Function-aware Good, the Bad, and the Ugly Optimiser (URF-GBUO). The developed model is adopted by clinicians for detecting cancer at an earlier stage. Finally, the experimentation is done on the proposed model to prove effectiveness.