A Fuzzy Transformer Fusion Network (FuzzyTransNet) for Medical Image Segmentation: The Case of Rectal Polyps and Skin Lesions

Abstract

Skin melanoma, one of the deadliest forms of cancer worldwide, demands precise diagnosis to mitigate cancer-related mortality. While histopathological examination, characterized by its cost-effectiveness and efficiency, remains the primary diagnostic approach, the development of an accurate detection system is pressing due to melanoma’s varying sizes, shapes, and indistinct boundaries shared with normal tissues. To address the efficient segmentation of skin melanoma, we propose an innovative hybrid neural network approach in this study. Initially, a fuzzy neural network is constructed using fuzzy logic to preprocess medical images, supplemented by wavelet transformation for image enhancement. Subsequently, the Swin Transformer V2 and ResNet50 networks are introduced to parallelly extract features and apply them to the task of skin melanoma segmentation. Extensive experimental comparisons are conducted with other classic and advanced medical segmentation algorithms on publicly available skin datasets, namely ISIC 2017 and ISIC 2018. Experimental results reveal that our method outperforms the optimal algorithms by 1.3% in the Dice coefficient and 1.3% in accuracy on the ISIC 2018 dataset. The evaluation metrics indicate the effectiveness of the constructed fuzzy block in identifying uncertain lesion boundaries, while the Transformer–CNN branch adeptly extracts global features while accurately capturing underlying details. Additionally, we successfully apply our method to colon polyp segmentation tasks with similar indistinct boundaries, achieving remarkable segmentation outcomes.