Real-Time Histopathological Cancer Diagnosis Using ResNet50: Transfer Learning for Automated Tumor Detection and Classification

Real-time and accurate histopathological diagnosis of cancer is essential in enhancing the timely detection and decreasing the workload associated with the standard digital pathology. In this study, a highly optimized, fine-tuned, and ResNet50 deep learning model that uses transfer learning to classify breast histopathology images into either benign or malignant will be proposed, with an exceptionally high level of precision. It trains the model on the Kaggle Breast Histopathology Images dataset and uses selective layer freezing and unfreezing as a way to improve domain-specific learning. Another custom classifier block further enhances the discriminative ability of the extracted features resulting in a impressive 98 % accuracy. Grade-CAM is incorporated to create interpretable heatmaps to reflect the parts of the tumor of interest and give clear visual interpretations of what the model is doing and why in order to achieve transparency and clinical trust. The superiority and the strength of the proposed system is confirmed by extensive experiments, such as loss-accuracy analysis, confusion-matrix analysis, ROC-AUC analysis, and comparative benchmarking with VGG16, EfficientNet, DenseNet121 and MobileNetV2. Generally, the research shows that ResNet50 in combination with explainable AI can greatly increase the accuracy of the diagnostic results, and it provides a highly efficient scalable method of automatic tumor detection in a real-time clinical setting.