Multi-Class Skin Lesion Detection and Classification via Teledermatology

• Published in: IEEE journal of biomedical and health informatics 2021-12, Vol.25 (12), p.4267-4275
• Main Authors: Khan, Muhammad Attique, Muhammad, Khan, Sharif, Muhammad, Akram, Tallha, Albuquerque, Victor Hugo C. de
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Canonical Correlation Analysis; Classification; Datasets; Dermatology; Dermoscopy; Dermoscopy Imaging; Embedding; Feature extraction; features fusion; features reduction; Humans; Image classification; Image Processing, Computer-Assisted; Image segmentation; Information processing; lesion localization; Lesions; Localization; Medical imaging; Melanoma; Microscopy; Modules; Neural networks; Neural Networks, Computer; Rural areas; Skin; Skin cancer; Skin diseases; Skin Diseases - diagnostic imaging; Skin lesions; Skin Neoplasms; Stochasticity; teledermatology; Telemedicine; Transfer learning
• ISSN: 2168-2194; 2168-2208
• DOI: 10.1109/JBHI.2021.3067789

Teledermatology is one of the most illustrious applications of telemedicine and e-health. In this field, telecommunication technologies are utilized to transfer medical information to the experts. Due to the skin's visual nature, teledermatology is an effective tool for the diagnosis of skin lesions especially in rural areas. Furthermore, it can also be useful to limit gratuitous clinical referrals and triage dermatology cases. The objective of this research is to classify the skin lesion image samples, received from different servers. The proposed framework is comprised of two module, which include the skin lesion localization/segmentation and the classification. In the localization module, we propose a hybrid strategy that fuses the binary images generated from the designed 16-layered convolutional neural network model and an improved high dimension contrast transform (HDCT) based saliency segmentation. To utilize maximum information extracted from the binary images, a maximal mutual information method is proposed, which returns the segmented RGB lesion image. In the classification module, a pre-trained DenseNet201 model is re-trained on the segmented lesion images using transfer learning. Afterward, the extracted features from the two fully connected layers are down-sampled using the t-distribution stochastic neighbor embedding (t-SNE) method. These resultant features are finally fused using a multi canonical correlation (MCCA) approach and are passed to a multi-class ELM classifier. Four datasets (i.e., ISBI2016, ISIC2017, PH2, and ISBI2018) are employed for the evaluation of the segmentation task, while HAM10000, the most challenging dataset, is used for the classification task. The experimental results in comparison with the state-of-the-art methods affirm the strength of our proposed framework.