Integrative Deep Learning-Driven Multi-Modal Diagnostic Framework for Automated Cancer Detection and Histopathological Image Analysis

Abstract

Cancer diagnosis remains one of the most critical challenges in medical research due to the complexity and heterogeneity of tumor tissues. Conventional diagnostic procedures often rely on manual histopathological examination, which can be time-consuming and prone to human error. To address these limitations, this paper proposes an Integrative Deep Learning-Driven Multi-Modal Diagnostic Framework for automated cancer detection and histopathological image analysis. The proposed framework combines Convolutional Neural Networks (CNNs) for image feature extraction and Recurrent Neural Networks (RNNs) for sequential feature learning, enabling the model to capture both spatial and contextual information from multi-modal data sources such as MRI, CT, and histopathological images. By integrating deep learning with advanced image preprocessing and feature fusion techniques, the framework aims to enhance diagnostic accuracy, minimize false detection rates, and assist clinicians in real-time cancer screening. Extensive experiments conducted on publicly available benchmark datasets demonstrate the robustness and generalization capability of the proposed system. The hybrid architecture achieves superior performance in terms of accuracy, sensitivity, specificity, and F1-score, outperforming traditional machine learning and single-modality deep learning models. Moreover, visualization-based interpretability methods such as Grad-CAM are utilized to highlight discriminative regions in histopathological images, improving model transparency and clinical trustworthiness. The proposed integrative framework provides a scalable and intelligent diagnostic solution, paving the way for AI-assisted precision oncology and facilitating early detection, classification, and treatment planning for diverse cancer types.