Authors: Dang Ngoc Hoang Thanh; Le Thi Thanh; Ugur Erkan; Aditya Khamparia; V.B. Surya Prasath
Addresses: Department of Information Technology, School of Business Information Technology, University of Economics Ho Chi Minh City, Ho Chi Minh City, Vietnam ' Department of Mathematics, Faculty of Applied Sciences, Ho Chi Minh City University of Technology and Education, Ho Chi Minh City, Vietnam ' Department of Computer Engineering, Karamanoglu Mehmetbey University, Karaman, Turkey ' Lovely Professional University, Phagwara, Punjab, India ' Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Centre, Department of Paediatrics, University of Cincinnati, Cincinnati, Ohio, USA; Department of Electrical Engineering and Computer Science, University of Cincinnati, Cincinnati, Ohio, USA
Abstract: In this paper, we present an efficient dermoscopic image segmentation method based on the linearisation of gamma-correction, and convolutional neural networks. Linearisation of gamma-correction is helpful to enhance low-intensity regions of skin lesion areas. Therefore, postprocessing tasks can work more effectively. The proposed convolutional neural network architecture for the segmentation method is based on the VGG-19 network. The acquired training results are convenient to apply the semantic segmentation method. Experimental results are conducted on the public ISIC-2017 dataset. To assess the quality of obtained segmentations, we make use of standard error metrics such as the Jaccard and Dice which are based on the overlap with ground truth, along with other measures such as the accuracy, sensitivity, and specificity. Moreover, we provide a comparison of our segmentation results with other similar methods. From experimental results, we infer that our method obtains excellent results in all the metrics and obtains competitive performance over other current and state of the art models for dermoscopic image segmentation.
Keywords: dermoscopic images; deep CNNs; machine learning; skin lesions; image segmentation; skin cancer.
International Journal of Computer Applications in Technology, 2021 Vol.66 No.2, pp.89 - 99
Received: 26 May 2020
Accepted: 02 Jul 2020
Published online: 20 Dec 2021 *