Assessment of different U-Net backbones in segmenting colorectal adenocarcinoma from H&E histopathology.

Abstract

Adenocarcinoma, the most prevalent type of colorectal cancer, makes up roughly 95 % of all cases and is associated with a notably high mortality rate. Owing to the various risk factors which might include personal choices and habits or genetic factors, the risk of developing the cancer for every individual might vary. However, given the statistics, the rate of acquiring the disease is pretty high. Therefore, based on the need for early detection and diagnosis of the disease, there is a pressing demand for an automated system to accurately identify adenocarcinoma in the colorectal region by utilizing the concept of binary segmentation wherein two classes are employed to indicate the presence as well as the absence of the condition. To address this, the project explored several deep learning-based segmentation methods-such as U-Net, Attention U-Net, U-Net with ResNet50 backbone, U-Net with MobileNet-v2 backbone, U-Net with EfficientNetB0 backbone, and U-Net with DenseNet121 backbone-to segment adenocarcinoma regions in histopathological images of the colon and rectum, which are essentially the various U-Net backbones. The performance of each method was then compared to identify the most effective approach, and subsequently, it was found that the U-Net with DenseNet121 backbone and U-Net with ResNet50 backbone performed better than the rest of the models in terms of accuracy with its respective training accuracy scores being 93.81 % and 93.39 % while the testing accuracy scores were 90.21 % and 89.81 %, respectively.