DEEP HYBRID CNN-VIT MODEL FOR ACCURATE CANCER DIAGNOSIS FROM HISTOPATHOLOGICAL IMAGES

Abstract

Cancer diagnosis using histopathological images is One of the most important and difficult tasks in medical picture analysis. mechanisms, transformer-based architectures, and multi-scale learning, a thorough grasp of these  methods  and  their  efficacy  is  still  required .Conventional deep learning algorithms ignore the surrounding tissue context, which is crucial for a precise diagnosis, and instead concentrate mostly on tumour regions 41. Convolutional neural networks (CNNs), Vision Transformers (ViTs), and hybrid models are examples of recent developments in deep learning that have made it possible to extract both local and global characteristics from dataimages. This survey paper reviews various deep learning approaches for cancer detection, with a special focus on models that incorporate surrounding tissue information. We analyze different techniques, datasets, performance metrics, and limitations. Furthermore, we highlight current challenges and future research directions for developing efficient and interpretable diagnostic systems.