ABSTRACT
Skin cancer is the irregular growth of skin cells, which is most often termed as cancer, developed by exposure of ultraviolet rays from sun. In this research paper, deep learning enabled hybrid optimization is followed for skin cancer detection and lesion segmentation. Two optimization algorithms are followed for skin lesion segmentation and cancer detection. Here, pre-processing is done by anisotropic diffusion followed by skin lesion segmentation. Here, Multi-Scale Residual Fusion Network (MSRFNet) is utilized for skin lesion segmentation, which is trained by proposed Average Subtraction Student Psychology Based Optimization (ASSPBO). After skin lesion segmentation, necessary features are extracted, followed by skin cancer detection. Skin cancer is detected by Deep Residual Network (DRN) trained by proposed Fractional ASSPBO (FrASSPBO). Moreover, performance of proposed FrASSPBO-DRN is analysed by three performance metrics like testing accuracy, True Positive Rate (TPR), and False Positive Rate (FPR) with values of 93.4%, 94%, and 8.2%.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Additional information
Notes on contributors
Diwan Baskaran
Dr. Diwan Baskaran, Professor, Department of Computer Science and Engineering, St.Joseph's College of Engineering, OMR, Chennai. He received the B.E degree in Electronics and Communication Engineering from Noorul Islam College of Engineering, India in 2003, M.E degree in Computer Science & Engineering from Manomaniam Sundaranar University, India in 2005 and Ph.D degree in Information and Communication Engineering at Anna University, Chennai, India in 2016. He works currently as an Professor for the Department of Computer Science and Engineering at St.Joseph's College of Engineering, OMR, Chennai; and he has 17 Years of teaching experience. He has participated and presented many Research Papers in International and National Conferences and also published many papers in International and National Journals. His area of interests includes Cloud Computing, Software Engineering, Data mining, Internet of Things and Big data.
Yanda Nagamani
Ms. Yanda Nagamani is an Assistant Professor in the Department of Computer Science and Engineering at GMR Institute of Technology in Rajam, Andhra Pradesh, India. She holds a Master's degree in Computer Science and Systems Engineering with a specialization in Artificial Intelligence and Robotics from Andhra University in Visakhapatnam, India. Her research interests primarily lie in the areas of Deep Learning, Artificial Intelligence, image processing, Robotics, and machine learning. She has been working on publishing research papers in reputed journals and conferences and actively participates in conferences and workshops related to her field of research.
Suneetha Merugula
Dr. Suneetha Merugula is currently working as an Assistant Professor in Department of Computer Science and Engineering, GITAM School of Technology, AP, India. She received her Ph.D in Computer Science and Engineering from Acharya Nagarjuna University, Guntur, India. She has over 14 years of experience in academia and research. Her research interests are in the area of Data Mining and machine learning. Her interest as a researcher reflects in her wide range of publications in various national and international journals and Conferences.
S P Premnath
Dr. Premnath S P, Completed his doctorate degree in 2022 from Anna University in Information and Communication Engineering and at present he is working as an Assistant Professor in the department of Electronics and Communication Engineering in Sri Krishna College of Engineering and Technology, Coimbatore, Tamilnadu, India. He has obtained his Master's Degree in Communication System from S.A college of Engineering, Chennai, (Anna University). He has done extensive research experience in Wireless Communication and Image Processing. He has also participated in many conferences and published several papers in National and International Journals.