Multimodal Machine Learning Approaches for Early Detection and Classification of Breast Cancer Using Imaging and Genomic Data

This research presents an innovative multimodal machine learning approach for breast cancer detection, integrating imaging, genomic, and clinical data to enhance diagnostic precision and reliability. Addressing the critical challenges of traditional single-modality diagnostic methods, the study develops a sophisticated neural network framework capable of comprehensive cancer assessment. The proposed system employs a pre-trained DenseNet121 convolutional neural network for processing mammogram images, complemented by specialized neural network branches dedicated to genomic and clinical data analysis. By strategically fusing features from these diverse data sources, the model generates a more nuanced and comprehensive diagnostic prediction. Rigorous preprocessing techniques were applied to standardize and optimize input data across imaging, genomic, and clinical domains. The neural network architecture incorporates advanced feature extraction and fusion mechanisms, enabling complex inter-modal relationship learning. A binary classification approach determines tumor malignancy based on the integrated multimodal input. Performance evaluation demonstrated exceptional results, with the model achieving 94.3% overall accuracy, 92.7% precision, and 95.1% recall. The Area Under the Receiver Operating Characteristic Curve (ROC-AUC) registered at 0.964, significantly outperforming traditional diagnostic approaches. Comparative analysis highlighted the substantial improvements offered by the multimodal methodology. The research contributes significantly to precision medicine, offering a promising framework for more accurate, personalized breast cancer diagnostics. By leveraging complementary data sources and advanced machine learning techniques, the study provides a transformative approach to early cancer detection and potentially improves patient outcomes.