Scholarlink

With the rapid advancements in machine learning and medical imaging technologies, there is a growing need for specialized diagnostic tools tailored to individual organs for more accurate cancer detection and staging. This paper presents an innovative machine learning-based graphical user interface (GUI) designed to enhance cancer diagnosis by first identifying the organ—lungs, breast, or brain—from MRI, CT, or ultrasound images, and then applying specialized algorithms like EfficientNetB0, ResNet101, VGG16, VGG19, Xception, MobileNetV2 for accurate cancer staging. The system utilizes advanced image recognition techniques to classify the organ type and deploys tailored diagnostic algorithms for each specific organ, thereby improving the precision of cancer detection and staging. This approach aims to streamline the diagnostic process and provide healthcare professionals with a robust tool for efficient and accurate cancer diagnosis.

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

Organ-Specific Cancer Diagnosis: A Machine Learning-Based GUI Approach

  • 会议论文

  • 发表日期：

    2026年

查看全文
  • B. Gayathri,
  • Saba Sultana,
  • M. Malini,
  • K. E. Ch. Vidyasagar

摘要

With the rapid advancements in machine learning and medical imaging technologies, there is a growing need for specialized diagnostic tools tailored to individual organs for more accurate cancer detection and staging. This paper presents an innovative machine learning-based graphical user interface (GUI) designed to enhance cancer diagnosis by first identifying the organ—lungs, breast, or brain—from MRI, CT, or ultrasound images, and then applying specialized algorithms like EfficientNetB0, ResNet101, VGG16, VGG19, Xception, MobileNetV2 for accurate cancer staging. The system utilizes advanced image recognition techniques to classify the organ type and deploys tailored diagnostic algorithms for each specific organ, thereby improving the precision of cancer detection and staging. This approach aims to streamline the diagnostic process and provide healthcare professionals with a robust tool for efficient and accurate cancer diagnosis.