A Convolutional Neural Network Based on Soft Attention Mechanism and Multi-Scale Fusion for Skin Cancer Classification

The seven most common skin diseases are melanocytic nevus, melanoma, benign keratosis, basal cell carcinoma, actinic keratosis, vascular lesions, and dermatofibroma. Among them, melanoma has been identified as one of the deadliest cancers based on medical studies and research. The current trend in disease detection revolves around the use of machine learning and deep learning models. Regardless of the model used, the crucial aspect is achieving accurate classification for these diseases. With the emergence of powerful convolutional neural networks (CNNs), significant progress has been made in classification of skin cancer lesions in recent years. However, various challenges hinder the development of practical and effective solutions. First, due to the specific nature of skin cancer lesion images, the current deep neural network architectures and training strategies have poor adaptability to medical images. They are also prone to gradient vanishing issues during network iteration, which hinders the construction of high-performance deep learning models that leverage distinctive characteristics of skin lesion images. Second, there exists a discordance between skin lesion images and deep learning network structures. To address these issues, this study introduces a soft attention mechanism to enhance adaptability to skin cancer lesion images and improve the extraction of informative features from medical images. Additionally, a novel multi-scale fusion convolutional neural network model is proposed to overcome the mismatch between deep learning CNN architectures and skin lesion images. This model autonomously extracts appearance features from raw dermatological medical images. Comparisons with other popular techniques demonstrate the effectiveness of the proposed model, which can achieve an accuracy of 93.9% on HAM10000 dataset. There is ongoing research to overcome the remaining challenges and further enhance the performance of skin cancer classification algorithms.