eng Oriental Scientific Publishing Company Biomedical and Pharmacology Journal 0974-6242 2026-09-30 19 3 2343 2365 10.13005/bpj/3498 73464 article Ranjith Kumar Paulraj 1 Vimala Mannarsamy 2 Nishanthini Gopalakrishnan 3 Shanmugam Manikandan 4 Department of Electronics and Communication, P.S.R. Engineering College, Sivakasi, Tamilnadu, India Department of Biomedical Engineering, Mepco Schlenk Engineering College, Sivakasi, India Early and accurate classification of malignant and non-malignant tissues is essential for improving diagnostic accuracy and patient outcomes. With the rapid advancement of artificial intelligence, machine learning , deep learning methods have demonstrated significant potential in automating histopathological image analysis and assisting clinical diagnosis. This paper proposes an explainable deep learning framework for early cancer detection using histopathological images. A total of six deep learning architectures, which are Convolutional Neural Network CNN, VGG16, ResNet50, MobileNetV2, EfficientNetB0, and InceptionV3 were tested with LC25000. It has 25,000 histopathological images which are classified into five groups such as lung squamous cell carcinoma, lung adenocarcinoma, lung benign tissue, and colon adenocarcinoma. The data was separated into 15% test, 15% validation and 70 % training of the models considered, ResNet50 had the best classification accuracy of 99% with excellent ability to generalize to all tissue classes. CNN and VGG16 also achieved high accuracy of 98%., whereas EfficientNetB0 was 97%. Conversely, MobileNetV2 (86% accuracy) and InceptionV3 (69% accuracy) demonstrated relatively worse performance especially when it comes to differentiating lung cancer subtypes. Explainable artificial intelligence methods such as Grad CAM, LIME, and SHAP were implemented to the most successful model to increase the level of transparency in the model. These visual explanations identified clinically significant tissue locations that influenced model predictions, hence improving the diagnostic system's interpretability and reliability. The findings establish that high performance deep learning architectures can be used in conjunction with explainable artificial intelligence techniques to offer a strong and reliable system of computer aided diagnosis of cancer. https://biomedpharmajournal.org/vol19no3/explainable-deep-learning-framework-for-early-cancer-detection-from-histopathological-images-using-lime-and-shap/ Cancer classification Explainable artificial intelligence Histopathology images LC25000 dataset LIME ResNet50 SHAP