TransSLC: Skin Lesion Classification in Dermatoscopic Images Using Transformers - Sarker, Md Mostafa Kamal; Moreno-García, Carlos Francisco; Ren, Jinchang; Elyan, Eyad (Lecture notes in computer science, 2022)
Early diagnosis and treatment of skin cancer can reduce patients’ fatality rates significantly. In the area of computer-aided diagnosis (CAD), the Convolutional Neural Network (CNN) has been widely used for image classification, segmentation, and recognition. However, the accurate classification of skin lesions using CNN-based models is still challenging, given the inconsistent shape of lesion areas (leading to intra-class variance) and inter-class similarities. In addition, CNN-based models with massive downsampling operations often result in loss of local feature attributes from the dermatoscopic images. Recently, transformer-based models have been able to tackle this problem by exploiting both local and global characteristics, employing self-attention processes, and learning expressive long-range representations. Motivated by the superior performance of these methods, in this paper we present a transformer-based model for skin lesion classification. We apply a transformers-based model using bidirectional encoder representation from the dermatoscopic image to perform the classification task. Extensive experiments were carried out using the public dataset HAM10000, and promising results of $$90.22%$$ , $$99.54%$$ , $$94.05%$$ , and $$96.28%$$ in accuracy, precision, recall, and F1 score respectively, were achieved. This opens new research directions towards further exploration of transformers-based methods to solve some of the key challenging problems in medical image classification, namely generalisation to samples from a different distribution.