DermoExpert: Skin lesion classification using a hybrid convolutional neural network through segmentation, transfer learning, and augmentation. (2022)

• Record Type: Journal Article
• Authors: Hasan, Md. Kamrul; Elahi, Md. Toufick E.; Alam, Md. Ashraful; Jawad, Md. Tasnim; Martí, Robert
• Is Part Of: Informatics in medicine unlocked. Volume 28(2022)
• Publication Date: 2022
• Subjects: ISIC skin lesion datasets -- Skin lesion classification and segmentation -- Convolutional neural networks -- Transfer learning -- Image augmentation; Medical informatics -- Periodicals; 610.285
• DOI: 10.1016/j.imu.2021.100819
• Languages: English
• ISSNs: 2352-9148

Abstract

Abstract: Background and Objective: Although automated Skin Lesion Classification (SLC) is a crucial integral step in computer-aided diagnosis, it remains challenging due to variability in textures, colors, indistinguishable boundaries, and shapes. Methods: This article proposes an automated dermoscopic SLC framework named Dermoscopic Expert (DermoExpert). It combines the pre-processing and hybrid Convolutional Neural Network (hybrid-CNN). The proposed hybrid-CNN has three distinct feature extractor modules, which are fused to achieve better-depth feature maps of the lesion. Those single and fused feature maps are classified using different fully connected layers, then ensembled to predict a lesion class. In the proposed pre-processing, we apply lesion segmentation, augmentation (geometry- and intensity-based), and class rebalancing (penalizing the majority class's loss and merging additional images to the minority classes). Moreover, we leverage transfer learning from the pre-trained models. Finally, we deploy the weights of our DermoExpert to a possible web application. Results: We evaluate our DermoExpert on the ISIC-2016, ISIC-2017, and ISIC-2018 datasets, where the DermoExpert has achieved the area under the receiver operating characteristic curve (AUC) of 0.96, 0.95, and 0.97, respectively. The experimental results improve the state-of-the-art by the margins of 10.0% and 2.0%, respectively, for the ISIC-2016 and ISIC-2017 datasets in terms of AUC. The DermoExpert also outperforms by 3.0% for the ISIC-2018 dataset concerning a balanced accuracy. Conclusion: Since DermoExpert provides better classification outcomes on three different datasets, leading to a better recognition tool to assist dermatologists. Our source code and segmented masks for the ISIC-2018 dataset will be available as a public benchmark for future improvements. Graphical abstract: Highlights: Proposing a hybrid-CNN classifier for multiple skin diseases recognition. Precisely segmenting skin lesion although the presence of hair fibers and other artifacts. Class-rebalancing, transfer learning, and augmentation for a generic model, as tiny datasets are being used. State of the art results on ISIC-16 (2-class), ISIC-17 (3-class), and ISIC-18 (7-class). Development of a possible web application, deploying our trained model's weights.