An Intelligent Cancer Image Classification Framework Using Region-Vision Transformer-based Adaptive Multiscale EfficientNetB7 with Gated Recurrent Unit Layer

Key Points

• The aim is to develop a sophisticated framework for accurate cancer image classification using advanced deep learning techniques.
• Utilized a region-vision transformer for feature extraction.
• Implemented multiscale EfficientNetB7 for enhanced performance.
• Integrated a gated recurrent unit layer to improve temporal data handling.
• Achieved higher accuracy in image classification compared to traditional methods.
• Demonstrated improved processing speed and efficiency in cancer detection tasks.

Abstract