![Sample our Engineering & Technology journals, sign in here to start your access, latest two full volumes FREE to you for 14 days]({"type":"image" , "src" : "/sda/5933/TOC-Subject-Sample-2021-Engineering-Tech.jpg"})
ABSTRACT
The Computer Aided Diagnosis (CAD) system has evolved as a useful tool for radiologists to classify breast cancer images into various categories, enabling early diagnosis and treatment. In CAD model construction, feature selection is essential for determining a subset of appropriate features to diagnose breast cancer. Salp Swarm Algorithm (SSA) is an evolutionary algorithm that simulates the swarming behaviour of salps. SSA has some advantages such as simplicity, speed in searching and ease of hybridization with other optimization algorithms. However, it suffers from being stuck in local optima and having slow convergence. To address these issues, this work proposes a novel hybridization algorithm called SSACS by combining the SSA with Cuckoo Search (CS) to improve convergence and exploitation capabilities. Further, the Deep Belief Network (DBN) classifier is applied to classify the mammogram images and improve the diagnosis rates. The proposed system’s efficacy is validated with the benchmark database of the Mammographic Image Analysis Society (mini-MIAS) dataset. The experimental findings indicate that the proposed SSACS with DBN classifier outperforms the state-of-the-art methods.
Acknowledgments
The experimental analysis performed in Data Analytics and Solutions Lab (Catalyzed & Supported by SEED Division, DST, New Delhi), Sona College of Technology, Salem, Tamilnadu, India.
Disclosure statement
No potential conflict of interest was reported by the authors.
Additional information
Notes on contributors
R. Reenadevi
R. Reenadevi received her Master’s degree in Computer Science and Engineering from Sona College of Technology, Salem, India, in 2010. She is pursuing her Ph.D under Anna University, Chennai. She is currently Assistant Professor at Sona College of Technology, Salem. Her research interests lie in the areas of Data Mining, Big Data Analytics, and Healthcare Informatics.
B. Sathiyabhama
Dr. B. Sathiyabhama received her Ph.D. degree in Computer Science and Engineering from the National Institute of Technology, Tiruchirappalli, India. She is currently Professor and Head, Department of Computer Science and Engineering at Sona College of Technology, Salem. Her research interests lie in the areas of Data Mining and Bioinformatics, Big Data Analytics, Health Care Informatics, Algorithm Analysis, and Compiler Design and Optimization. She has been a Programme and Technical Committee member of several conferences. She was the Chair and invited speaker of several workshops on Data mining and Bioinformatics and technical symposia and International conferences. She is a reviewer of several journals and conferences and Associate Editor in Frontiers of Digital Health. She is co-authored a book titled Professional Ethics and Human Values. She has published widely in international journals and conferences. She has professional membership in IEEE, ACM, ISTE, CSI, and ISRD (Senior Member). She has received Best Women Engineer Award (R&D) by IEI Salem, India, and awards for excellence in teaching and research and development contributions, and the best outgoing student award (PG level). She is also selected for 2010 Who’s who in the world, conducted by Marquis USA, and completed 10 research and consultancy projects including DST, AICTE, and various other states/central Govt. agencies. She has guided 11 Ph.D. scholars and 18 Post Graduate Engineering research scholars.
S. Sankar
Dr. S. Sankar received an M.E degree from Anna University and a Ph.D. degree from VIT University, Vellore, India in 2019. He is currently working as an Assistant Professor at Sona College of Technology, Salem. Research interest includes the Internet of Things, Wireless Sensor Networks, and Machine Learning. He has published various papers in international journals and conferences.