Enhancing Breast Cancer Diagnosis with Integrated Histopathological and Thermal Image Analysis

Breast cancer is one of the most common and life-threatening diseases affecting women globally. Early and accurate diagnosis plays a crucial role in improving patient survival rates and guiding effective treatment strategies. Traditional diagnostic methods, while effective, are often time-consuming, resource-intensive, and subject to inter-observer variability. This project proposes an advanced computer-aided diagnostic system that integrates histopathological image analysis with thermal imaging using deep learning techniques to improve the accuracy and reliability of breast cancer detection.The proposed system leverages the complementary nature of the two imaging modalities: histopathological images offer cellular-level insights into tissue structure, while thermal images provide non-invasive functional information related to metabolic activity and vascular changes associated with malignancies.Using a multimodal deep learning approach, the system employs Convolutional Neural Networks (CNNs) to extract features from both image types. These features are then combined through a feature fusion strategy and passed through a fully connected neural network for classification into benign or malignant categories.