An Effective Skin Disease Segmentation Model Based on Deep Convolutional Neural Network

Ginni Arora, Ashwani Kumar Dubey and Zainul Abdin Jaffery
Additional contact information
Ginni Arora: Amity Institute of Information Technology, Amity University Uttar Pradesh, Noida, India
Ashwani Kumar Dubey: Amity School of Engineering and Technology, Amity University Uttar Pradesh, Noida, India
Zainul Abdin Jaffery: Faculty of Engineering and Technology, Jamia Millia Islamia, New Delhi, India

International Journal of Intelligent Information Technologies (IJIIT), 2022, vol. 18, issue 1, 1-18

Abstract: Automated segmentation of skin lesions as of digitally recorded images is a crucial procedure to diagnose skin diseases accurately. This paper proposes a segmentation model for skin lesions centered on deep convolutional neural network (DCNN) for melanoma, squamous, basal, keratosis, dermatofibroma, and vascular types of skin diseases. The DCNN is trained from scratch instead of pre-trained networks with different layers among variations in pooling and activation functions. The comparison of the proposed model is made with the winner of the ISIC 2018 challenge task (skin lesion segmentation) and other methods. The experiments are performed on challenge datasets and shown better segmentation results. The main contribution is developing an automated segmentation model, evaluating performance, and comparing it with other state-of-the-art methods. The essence of the proposed work is the simple network architecture and its excellent results. It outperforms by obtaining a Jaccard index of 87%, dice similarity coefficient of 91%, the accuracy of 94%, recall of 94%, and precision of 89%.

Date: 2022
References: Add references at CitEc
Citations:

Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJIIT.298695 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:igg:jiit00:v:18:y:2022:i:1:p:1-18

Access Statistics for this article

International Journal of Intelligent Information Technologies (IJIIT) is currently edited by Vijayan Sugumaran

More articles in International Journal of Intelligent Information Technologies (IJIIT) from IGI Global
Bibliographic data for series maintained by Journal Editor ().