Scholarlink

Colorectal cancer (CRC) is the second most prevalent type of cancer among men and women in Brazil, accounting for 9.1% of cancers in men and 9.2% in women between 2020 and 2022. CRC diagnosis typically relies on histopathological images, which can be challenging to interpret and prone to errors. This paper proposes a method for diagnosing CRC using histopathological images. This method involves enhancing images with MIRNet and classifying them as benign or malignant using InceptionV3. The results surpass the state-of-the-art by introducing, for the first time, enhancement through MIRNet, achieving an accuracy of 99.67% and an AUC of 0.9999. We believe that this method, combined with medical expertise, holds great promise for early CRC 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

Improving Colorectal Cancer Diagnosis Using MIRNet and InceptionV3 on Histopathological Images

  • 会议论文

  • 发表日期：

    2025年

查看全文
  • Neilson P. Ribeiro,
  • Felipe R. S. Teles,
  • João Otávio Bandeira Diniz,
  • Luana B. da Cruz,
  • Domingos A. Dias Jr.,
  • Geraldo Braz Junior,
  • João D. S. de Almeida,
  • Anselmo C. de Paiva

摘要

Colorectal cancer (CRC) is the second most prevalent type of cancer among men and women in Brazil, accounting for 9.1% of cancers in men and 9.2% in women between 2020 and 2022. CRC diagnosis typically relies on histopathological images, which can be challenging to interpret and prone to errors. This paper proposes a method for diagnosing CRC using histopathological images. This method involves enhancing images with MIRNet and classifying them as benign or malignant using InceptionV3. The results surpass the state-of-the-art by introducing, for the first time, enhancement through MIRNet, achieving an accuracy of 99.67% and an AUC of 0.9999. We believe that this method, combined with medical expertise, holds great promise for early CRC diagnosis.