Antialiasing Attention Spatial Convolution Model for Skin Lesion Segmentation with Applications in the Medical IoT

Abstract

This study presents a noninvasive visual sensing enhancing system for skin lesion segmentation. According to the Skin Cancer Foundation, skin cancer kills more than two people every hour in the United States, and one in every five Americans will develop the disease. Skin cancer is becoming more popular, so the need for skin cancer diagnosis is increasing, particularly for melanoma, which has a high metastasis rate. Many traditional algorithms, as well as a computer-aided diagnosis tool, have been implemented in dermoscopic images for skin lesion segmentation to meet this need. However, the accuracy of the model is low, and the prognosis time is lengthy. This paper presents antialiasing attention spatial convolution (AASC) to segment melanoma skin lesions in dermoscopic images. Such a system can enhance the existing Medical IoT (MIoT) applications and provide third-party clues for medical examiners. Empirical results show that the AASC performs well when it is able to overcome dermoscopic limitations such as thick hair, low contrast, or shape and color distortion. The model was evaluated strictly under many statistical evaluation metrics such as the Jaccard index, Recall, Precision, F1 score, and Dice coefficient. The performance of the AASC was trained and tested. Remarkably, the AASC model yielded the highest scores in both three databases compared with the state-of-the-art models across three datasets: ISIC 2016, ISIC 2017, and PH2.