An Integrated Deep Learning Methodology for the Automated Histopathological Differentiation of Lung throughout Colon Cancer

Lung along with colon cancer are recognized as across the most significant causes of long-term morbidity and mortality to the global health sector. The treatment process is significantly impacted and the patient's chances of sur-vival are significantly reduced when these two cancers spread rapidly to each other if they are not detected adequately at the early stage. The diagnosis of diseases through histopathological images has made significant contributions to medical science; however, this procedure is time-consuming, experience-dependent, and produces relatively inconsistent results. The potential for precise, automated, and scalable diagnostic technologies has been facilitated by the emergence of deep learning-based methods and artificial intelligence in this context. An effective deep learning system based on transfer learning is shown in this work, which can analyze the histological images of colon as well as lung cancer to classify them with high accuracy. Using three pre-trained convolutional neural networks—DenseNet169, DenseNet201, and ResNet152V2—the investigation trains and assesses various models. The DenseNet201 architecture demonstrates the highest performance, with a maximal classification accuracy of 99.74%, as indicated by the results. Deep learning's relevance and efficacy in digital pathology are underscored by the proposed method, which also demonstrates its potential as a dependable support technology for traditional clinical diagnosis systems. Our framework integrates Grad-CAM visualizations to provide interpretability by highlighting discriminative tissue regions influencing predictions. This transparency is essential for clinical adoption, as it aligns with pathologists’ reasoning and builds trust in AI-assisted diagnostics.