HierAttn: Effectively Learn Representations from Stage Attention and Branch Attention for Skin Lesions Diagnosis

Accurate and unbiased examinations of skin lesions are critical for the early diagnosis and treatment of skin conditions and disorders. Visual features of skin lesions vary significantly because the images are collected from patients with different lesion colours and morphologies by using dissimilar imaging equipment. Recent studies have reported ensembled convolutional neural networks (CNNs) to classify the images for early diagnosis of skin disorders. However, the practical use of these ensembled CNNs is limited because they are heavyweight and inadequate for using contextual information. Although lightweight networks (e.g., MobileNetV3 and EfficientNet) were developed to achieve parameters reduction for implementing deep neural networks on mobile devices, insufficient depth of feature representation restricts the performance. To address the existing limitations, we introduce a new lite and effective neural network, namely HierAttn. The HierAttn applies a novel strategy to learn the local and global features by using multi-stage and multi-branch attention mechanisms. The efficacy of HierAttn was evaluated by using the dermoscopy images dataset ISIC2019 and smartphone photos dataset PAD-UFES-20 (PAD20). The experimental results show that HierAttn achieves the best accuracy and AUC among the state-of-the-art lightweight networks. The code is available at https://github.com/anthonyweidai/HierAttn.