Improving Colorectal Cancer Diagnosis Using MIRNet and InceptionV3 on Histopathological Images - Ribeiro, Neilson P.; Teles, Felipe R. S.; Diniz, João Otávio Beira; Cruz, Luana B. da; Dias Jr., Domingos A.; Braz Junior, Geraldo; Almeida, João D. S. de; Paiva, Anselmo C. de (Anais da Brazilian Conference on Intelligent Systems (BRACIS); 2024: Anais da XXXIV Brazilian Conference on Intelligent Systems, 2024)
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.