Optimizing colorectal cancer segmentation with MobileViT-UNet and multi-criteria decision analysis

Colorectal cancer represents a significant health challenge as one of the deadliest forms of malignancy. Manual examination methods are subjective, leading to inconsistent interpretations among different examiners and compromising reliability. Additionally, process is time-consuming and labor-intensive, necessitating the development of computer-aided diagnostic systems. This study investigates the segmentation of colorectal cancer regions of normal tissue, polyps, high-grade intraepithelial neoplasia, low-grade intraepithelial neoplasia, adenocarcinoma, and serrated Adenoma, using proposed segmentation models: VGG16-UNet, ResNet50-UNet, MobileNet-UNet, and MobileViT-UNet. This is the first study to integrate MobileViT as a UNet encoder. Each model was trained with two distinct loss functions, binary cross-entropy and dice loss, and evaluated using metrics including Dice ratio, Jaccard index, precision, and recall. The MobileViT-UNet+Dice loss emerged as the leading model in colorectal histopathology segmentation, consistently achieving high scores across all evaluation metrics. Specifically, it achieved a Dice ratio of 0.944 ± 0.030 and a Jaccard index of 0.897 ± 0.049, with precision at 0.955 ± 0.046 and Recall at 0.939 ± 0.038 across all classes. To further obtain the best performing model, we employed multi-criteria decision analysis (MCDA) using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). This analysis revealed that the MobileViT-UNet+Dice model achieved the highest TOPSIS scores of 1, thereby attaining the highest ranking among all models. Our comparative analysis includes benchmarking with existing works, the results highlight that our best-performing model (MobileViT-UNet+Dice) significantly outperforms existing models, showcasing its potential to enhance the accuracy and efficiency of colorectal cancer segmentation.