Optimal deep neural network-driven computer aided diagnosis model for skin cancer. (October 2022)

• Record Type: Journal Article
• Authors: Malibari, Areej A.; Alzahrani, Jaber S.; Eltahir, Majdy M.; Malik, Vinita; Obayya, Marwa; Duhayyim, Mesfer Al; Lira Neto, Aloísio V.; de Albuquerque, Victor Hugo C.
• Is Part Of: Computers & electrical engineering. Volume 103(2022)
• Publication Date: 2022-10
• Subjects: Dermoscopic images -- Image-guided intervention -- Medical imaging -- Computer aided diagnosis -- Skin cancer -- Image classification -- Deep neural network -- Decision making -- Transfer learning; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2022.108318
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: Present a computer aided diagnosis model for skin cancer. Propose an ODNNCADSCC model for skin cancer detection and classification. Employ U-Net segmentation with Squeezenet feature extraction. Introduce IWOA with deep neural network for skin cancer classification. Validate the performance on benchmark ISIC 2019 dataset. Abstract: Image-guided intervention is a medical procedure that leverages computerized systems to deliver virtual image overlays to help physicians in visualization and targeting the surgical site in an accurate manner. Computer Aided Diagnosis (CAD) models that use Deep Learning (DL) techniques are useful in achieving accurate skin cancer classification. In this background, the current research paper concentrates on the design of Optimal Deep Neural Network Driven Computer Aided Diagnosis Model for Skin Cancer Detection and Classification (ODNNCADSCC) model. The presented ODNNCADSCC model primarily applies Wiener Filtering (WF)-based pre-processing step followed by U-Net segmentation approach. In addition, SqueezeNet model is also exploited to generate a collection of feature vectors. Finally, Improved Whale Optimization Algorithm (IWOA) with DNN model is utilized for effectual skin cancer detection and classification. In this procedure, IWOA is applied to select the DNN parameters in a proficient manner. The comparative analysis results established the promising performance of the proposed ODNNCADSCC model over recent approaches with a maximum accuracy of 99.90%. Graphical abstract: Image, graphical abstract