Scholarlink

This research investigates integrating profound learning strategies into therapeutic picture investigation and illness determination, pointing to up-grade demonstrative exactness and treatment arranging. Leveraging convolutional neural networks (CNNs), repetitive neural systems (RNNs), U-Net, and ResNet designs, we conducted broad tests over different restorative imaging modalities, counting MRI, CT, X-ray, ultrasound, and histopathology pictures. Our results illustrate the predominant execution of CNNs in picture classification errands, accomplishing correctnesses of up to 95%. Moreover, U-Net displayed extraordinary execution in picture division assignments, accomplishing Dice coefficients surpassing 0.90 over numerous modalities. Besides, object discovery assignments showcased the adequacy of CNNs, accomplishing cruel normal precisions (mAP) of up to 0.94. These discoveries emphasise the potential of profound learning calculations in moving forward with therapeutic picture examination and infection determination, advertising bits of knowledge for analysts and specialists within the biomedical imaging community.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Enhancing Medical Image Analysis and Disease Diagnosis with Deep Learning: Advances and Future Directions in Biomedical Imaging

  • 会议论文

  • 发表日期：

    2025年

查看全文
  • Suman Kumar Swarnkar,
  • Abhishek Guru,
  • Rohit R. Dixit,
  • Harshitha Raghavan Devarajan,
  • Sivasubramanian Balasubramanian,
  • Rupesh Kumar Jindal

摘要

This research investigates integrating profound learning strategies into therapeutic picture investigation and illness determination, pointing to up-grade demonstrative exactness and treatment arranging. Leveraging convolutional neural networks (CNNs), repetitive neural systems (RNNs), U-Net, and ResNet designs, we conducted broad tests over different restorative imaging modalities, counting MRI, CT, X-ray, ultrasound, and histopathology pictures. Our results illustrate the predominant execution of CNNs in picture classification errands, accomplishing correctnesses of up to 95%. Moreover, U-Net displayed extraordinary execution in picture division assignments, accomplishing Dice coefficients surpassing 0.90 over numerous modalities. Besides, object discovery assignments showcased the adequacy of CNNs, accomplishing cruel normal precisions (mAP) of up to 0.94. These discoveries emphasise the potential of profound learning calculations in moving forward with therapeutic picture examination and infection determination, advertising bits of knowledge for analysts and specialists within the biomedical imaging community.