A Multi-Modal Deep Learning Framework for Prostate Cancer Detection Using MRI

Prostate cancer is one of the most common cancers affecting men worldwide, and earlydetection is crucial for improving patient outcomes. However, accurate diagnosis often presentschallenges due to the variability in tumor presentation and the limitations of relying on single-modalitydata. In this study, we propose a novel multi-modal deep learning framework designed to improve thedetection and classification of prostate cancer by integrating various data sources, including magneticresonance imaging (MRI), histopathological images, and clinical data. Our method employsconvolutional neural networks (CNNs) and transformers to effectively extract and fuse features fromeach modality, capturing both spatial and contextual information relevant to tumor identification.The proposed multi-modal architecture consists of separate feature extraction pathways for each datatype, followed by a fusion mechanism that combines these features into a unified representation. Thisholistic approach enables the model to leverage complementary information from different modalities,resulting in more comprehensive and reliable predictions. In addition, we incorporate advancedtechniques such as attention mechanisms to highlight critical regions in imaging data, further enhancingdiagnostic accuracy. We evaluate the performance of our multi-modal approach using a large dataset ofprostate cancer cases and compare it with various state-of-the-art single-modality and multi-modalmodels. The results show that our framework outperforms existing methods in terms of key metrics,including accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve(AUC). Specifically, our model demonstrates superior capability in distinguishing between benign andmalignant tumors, as well as in identifying clinically significant cancer cases. The experimental findingsindicate that our multi-modal deep learning approach not only improves diagnostic precision but alsooffers a scalable solution that can be integrated into clinical workflows for automated prostate cancerdetection.

کلیدواژه ها:

Prostate Cancer Detection ، Multi-Modal ، Deep Learning ، Transformers