A Multi-cancer Detection and Localization System Utilizing X-AI and Ensemble Technique using CNN

Multi-cancer early detection (MCED) tests aim to identify multiple cancers at an early stage through the analysis of a single specimen, typically utilizing blood or liquid biopsy. However, the manual examination of blood samples under a microscope is time-consuming, biased, and relies on expert availability. Additionally, the integration of AI-based diagnosis into cancer detection faces challenges due to the unreliability of medical experts. Despite lacking FDA approval, MCED tests hold the potential to enhance cancer screening efficiency, reduce costs, improve patient survival, and automate medical treatment planning. In this research, we explored the efficacy of eight distinct CNN architectures (EfficientNet, MobileNetV3, DenseNet201, DenseNet121, VGG16, ResNet50, ResNet152, and VGG19). Subsequently, combining the top-five models into a stacking ensemble yielded improved results, achieving 98.78 % accuracy, 99% precision, and 99% sensitivity in detecting various cancers from digital histopathology images. The study also integrated grad-CAM visualization to enhance explainability in the cancer detection system, offering valuable insights into CNN-based cancer detection. The proposed model demonstrates precise early multi-cancer detection and monitoring, offering potential cost savings, fostering trust in AI among medical professionals, and streamlining autonomous diagnosis procedures.