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)
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.