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@article{168103,
        author = {USHA N and MANJULA K and MEGHA M},
        title = {Fusion-Based Deep Learning for Kidney Stone Detection Using Ultrasound, CT, and MRI},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {4},
        pages = {1251-1254},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=168103},
        abstract = {Kidney stone detection using medical imaging techniques such as ultrasound (US) and computed tomography (CT) is a critical diagnostic task. Ultrasound is safe but lacks the contrast needed for reliable stone detection, while CT provides higher accuracy but exposes patients to radiation. In this study, we propose a multimodal learning approach that fuses data from both ultrasound and CT/MRI to improve detection rates, reduce false positives/negatives, and leverage the strengths of both imaging techniques. Our approach employs a dual-branch deep learning architecture, combining U-Net for ultrasound image segmentation with a ResNet-based model for CT/MRI data. We demonstrate that multimodal fusion significantly enhances kidney stone detection accuracy},
        keywords = {},
        month = {September},
        }