Pigmented skin disease classification via deep learning with an attention mechanism

Abstract

Pigmented skin disease is common; doctors need to observe and analyze pigmented skin disease images for diagnostic purposes. However, due to regional differences, diagnoses are subjective, resulting in high misdiagnosis rates. Therefore, this paper proposes a deep learning-based method for classifying pigmented skin disease images named the skin-global attention block (Skin-GAB). This method automatically classifies pigmented skin disease images through a system architecture that includes image augmentation, image segmentation, cluster analysis, segmented and original image classification, and network fusion. Additionally, this paper utilizes the GAB attention mechanism to encode the height, width, and channel of the feature maps, allowing the model to automatically learn crucial features from pigmented skin disease images and focus its attention on task-relevant information, thereby capturing disparities in input feature maps and further enhancing the model’s performance. The experimental results show that the proposed method performs well in terms of accuracy and practicality. Compared to using Xception as the classification network and the convolutional block attention module (CBAM) as the attention mechanism on the HAM10000 dataset, the system architecture proposed in this paper provides an improvement in accuracy of 2.89%. Therefore, this method will provide more accurate and efficient technical support for medical fields such as pigmented skin disease diagnosis and treatment.