Explainable Cancer Segmentation Through Classification

Andrei Dugaesescu, Catalin-Mihail Chiru, Mihai Nan, Mihai Trascau, Adina Magda Florea

Abstract: Lung cancer detection remains a challenging task due to the complex nature of medical imaging and the shortage of specialized radiologists. This paper addresses the challenges of early detection of lung cancer using advanced machine learning techniques for CT image analysis. Given the limitations of current semantic segmentation methods and the lack of interpretability, we propose an innovative approach combining classification techniques with explanatory methods to enhance both precision and transparency. We developed a novel data preprocessing pipeline, a multi-stage CNN classifier training model, and a segmentation technique using concatenated Grad-CAM heatmaps. Our evaluations demonstrate significant improvements in tumour detection and model explainability, providing more effective support for radiologists. These advancements are reflected in increased segmentation accuracy, as evidenced by comparative analyses and qualitative assessments.