CancerNet: A comprehensive deep learning framework for precise and intelligible cancer identification

Abstract

The medical community continually seeks innovative solutions to address healthcare challenges, particularly in cancer detection. A promising approach involves the use of Artificial Intelligence (AI) techniques, specifically Deep Learning (DL) models. This research introduces CancerNet, incorporating convolutional, involutional, and transformer components to extract hierarchical features and capture long-range dependencies from medical imaging data across the channel and spatial domains. CancerNet was trained and evaluated on an extensive dataset of histopathological images (HI) of tumor tissues and validated on the DeepHisto dataset, which comprises whole slide images (WSI) of various subtypes of glioma. CancerNet surpasses other comparative models and, achieves a higher accuracy on both datasets. CancerNet exhibits robustness across various imaging conditions, thereby ensuring reliable performance in various clinical scenarios. By integrating Explainable AI (XAI) techniques, CancerNet enhances transparency in its decision-making process, improves understanding and fosters trust in clinical adoption. CancerNet achieved an accuracy of 98.77% on the Histopathological Image dataset and 97.83% on the DeepHisto validation dataset, proving to be more effective than previous. Furthermore, transparency in AI models is crucial as it enhances healthcare professionals ability to understand and trust the model’s decision-making process, facilitating their adoption in clinical settings.