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@article{204344,
        author = {Shantanu Vedpathak and Anand Kejkar and Ajay Shinde and Ajay chopne and Santosh Tondare and Udhav Badgire and Sidharth Kamble and Vandana Agrawal},
        title = {Deep Learning-Driven Multimodal Medical Imaging for Early Cancer Detection: Advances, Challenges, and Future Directions},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {1},
        pages = {9390-9396},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=204344},
        abstract = {Early cancer detection is a major determinant of patient prognosis, and artificial intelligence (AI), particularly deep learning (DL), offers transformative capabilities for image-based screening, diagnosis, and prognostication. This review synthesizes the current state of DL-based approaches applied to medical imaging for the early detection of cancer across various organ systems, with an emphasis on mechanisms, modalities, radiomics/radiogenomics, multimodal fusion, and their translation to clinical practice. We integrate evidence from systematic reviews, methodological surveys, and modality- and organ-specific studies to provide a cohesive picture of what works, where gaps remain, and how future developments may converge toward robust, generalizable, and clinically actionable solutions. Throughout, we emphasize the diversity of imaging modalities (ultrasound, CT, MRI, PET/CT) and the spectrum of DL-enabled tasks (detection, segmentation, radiomics feature extraction, radiogenomics integration, and multimodal fusion). We also discuss key challenges—data heterogeneity, reproducibility, external validation, and real-world deployment—and outline avenues for reproducible, multicenter collaboration. This synthesis draws on evidence from major reviews and primary studies spanning hepatobiliary, breast, gynecologic, head/neck, thyroid, prostate, and gastrointestinal cancers, highlighting both shared principles and domain-specific nuances [1]},
        keywords = {Deep Learning, Medical Imaging, Cancer Detection, Artificial Intelligence in Healthcare, Precision Oncology},
        month = {June},
        }