Searching for just a few words should be enough to get started. If you need to make more complex queries, use the tips below to guide you.
Article type: Research Article
Authors: Sainudeen, Jinu P.* | V, Ceronmani Sharmila | R, Parvathi
Affiliations: Department of Information Technology, Hindustan Institute of Technology & Science, Chennai, Rajiv Gandhi Salai (OMR), Padur, Kelambakkam, Tamil Nadu, India
Correspondence: [*] Corresponding author: Jinu P. Sainudeen, Department of Information Technology, Hindustan Institute of Technology & Science, Chennai, Rajiv Gandhi Salai (OMR), Padur, Kelambakkam, Tamil Nadu 603103, India. E-mail: jinups99@gmail.com.
Abstract: During the past few decades, melanoma has grown increasingly prevalent, and timely identification is crucial for lowering the mortality rates linked to this kind of skin cancer. Because of this, having access to an automated, trustworthy system that can identify the existence of melanoma may be very helpful in the field of medical diagnostics. Because of this, we have introduced a revolutionary, five-stage method for detecting skin cancer. The input images are processed utilizing histogram equalization as well as Gaussian filtering techniques during the initial pre-processing stage. An Improved Balanced Iterative Reducing as well as Clustering utilizing Hierarchies (I-BIRCH) is proposed to provide better image segmentation by efficiently allotting the labels to the pixels. From those segmented images, features such as Improved Local Vector Pattern, local ternary pattern, and Grey level co-occurrence matrix as well as the local gradient patterns will be retrieved in the third stage. We proposed an Arithmetic Operated Honey Badger Algorithm (AOHBA) to choose the best features from the retrieved characteristics, which lowered the computational expense as well as training time. In order to demonstrate the effectiveness of our proposed skin cancer detection strategy, the categorization is done using an improved Deep Belief Network (DBN) with respect to those chosen features. The performance assessment findings are then matched with existing methodologies.
Keywords: Gaussian filtering, histogram equalization, I-BIRCH, Improved Local Vector Pattern, Arithmetic Operated Honey Badger Algorithm (AOHBA), improved DBN
DOI: 10.3233/MGS-230040
Journal: Multiagent and Grid Systems, vol. 19, no. 2, pp. 187-210, 2023
IOS Press, Inc.
6751 Tepper Drive
Clifton, VA 20124
USA
Tel: +1 703 830 6300
Fax: +1 703 830 2300
sales@iospress.com
For editorial issues, like the status of your submitted paper or proposals, write to editorial@iospress.nl
IOS Press
Nieuwe Hemweg 6B
1013 BG Amsterdam
The Netherlands
Tel: +31 20 688 3355
Fax: +31 20 687 0091
info@iospress.nl
For editorial issues, permissions, book requests, submissions and proceedings, contact the Amsterdam office info@iospress.nl
Inspirees International (China Office)
Ciyunsi Beili 207(CapitaLand), Bld 1, 7-901
100025, Beijing
China
Free service line: 400 661 8717
Fax: +86 10 8446 7947
china@iospress.cn
For editorial issues, like the status of your submitted paper or proposals, write to editorial@iospress.nl
如果您在出版方面需要帮助或有任何建, 件至: editorial@iospress.nl