A Novel Momentum-Based Deep Learning Techniques for Medical Image Classification and Segmentation
<|reference_start|>A Novel Momentum-Based Deep Learning Techniques for Medical Image Classification and Segmentation: Accurately segmenting different organs from medical images is a critical prerequisite for computer-assisted diagnosis and intervention planning. This study proposes a deep learning-based approach for segmenting various organs from CT and MRI scans and classifying diseases. Our study introduces a novel technique integrating momentum within residual blocks for enhanced training dynamics in medical image analysis. We applied our method in two distinct tasks: segmenting liver, lung, & colon data and classifying abdominal pelvic CT and MRI scans. The proposed approach has shown promising results, outperforming state-of-the-art methods on publicly available benchmarking datasets. For instance, in the lung segmentation dataset, our approach yielded significant enhancements over the TransNetR model, including a 5.72% increase in dice score, a 5.04% improvement in mean Intersection over Union (mIoU), an 8.02% improvement in recall, and a 4.42% improvement in precision. Hence, incorporating momentum led to state-of-the-art performance in both segmentation and classification tasks, representing a significant advancement in the field of medical imaging.<|reference_end|>
@article{biswas2024a,
    title={A Novel Momentum-Based Deep Learning Techniques for Medical Image
  Classification and Segmentation},
    author={Koushik Biswas, Ridal Pal, Shaswat Patel, Debesh Jha, Meghana Karri,
  Amit Reza, Gorkem Durak, Alpay Medetalibeyoglu, Matthew Antalek, Yury
  Velichko, Daniela Ladner, Amir Borhani, Ulas Bagci},
    journal={arXiv preprint arXiv:2408.05692},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.05692},
    primaryClass={cs.CV cs.AI cs.LG eess.IV}
}