SLDCNet: Skin lesion detection and classification using full resolution convolutional network‐based deep learning CNN with transfer learning. Issue 9 (25th January 2022)

• Record Type: Journal Article
• Authors: Varma, P. Bharat Siva; Paturu, Siddartha; Mishra, Suman; Rao, B. Srinivasa; Kumar, Pala Mahesh; Krishna, Namani Vamshi
• Other Names: Herrero Álvaro guestEditor.; Urda Daniel guestEditor.; Sedano Javier guestEditor.; Quintián Héctor guestEditor.; Corchado Emilio guestEditor.; Ahmed Syed Hassan guestEditor.; Khan Murad guestEditor.; Guibene Wael guestEditor.
• Is Part Of: Expert systems. Volume 39:Issue 9(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-01-25
• Subjects: fast march inpainting -- fully resolution convolutional neural networks -- gamma regularizer -- hybrid Gaussian filter -- skin lesion detection; Expert systems (Computer science); 006.33
• DOI: 10.1111/exsy.12944
• Languages: English
• ISSNs: 0266-4720

Abstract

Abstract: Background: Skin cancer is one of the life threating diseases in the world. So, millions of lives can be saved by early detection of skin cancer. In addition, automating the computer‐aided system of skin lesion detection and classification (SLDC) will assist the medical practitioners to ensure more efficacious treatment of skin lesion disease. Material and Method: In this article, a hybrid preprocessing‐based transfer learning model for SLDC is proposed, which is named as SLDCNet. Initially, the hybrid Gaussian filter (HGF) with connected component label (CCL) based fast march inpainting procedure is used for hair removal and denoising of skin lesions. Next, full resolution convolutional networks (FrCN) based segmentation method is adapted for detecting the cancer region. Then, feature extraction is performed using deep residual learning and finally, transfer learning mechanism is applied for classification of eight skin lesions. Results: The extensive simulation results shows that proposed SLDCNet resulted in a classification accuracy of 99.92%, sensitivity of 99%, and specificity of 99.36%, respectively. Conclusion: From the obtained results, it is proven that proposed SLDCNet provides better performance as compared to state‐of‐art SLDC approaches, and even the standard ISIC‐2019 public challenge.