An adaptive hybrid clustering framework for high-precision microarray image segmentation using GA and BEMD

Ch Ravikumar; Dasari Kavitha; Nimmala Satyanarayana; Sutraya Sukerthi; Sahith R.

doi:10.6977/IJoSI.202510_9(5).0004

International Journal of Systematic Innovation ›› 2025, Vol. 9 ›› Issue (5) :43 -55. DOI: 10.6977/IJoSI.202510_9(5).0004

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An adaptive hybrid clustering framework for high-precision microarray image segmentation using GA and BEMD

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¹ Department of Computer Science and Engineering, School of Engineering, Sreenidhi University, Hyderabad, Telangana, India

² Department of Computer Science and Engineering (Artificial Intelligence and Machine Learning), G. Narayanamma Institute of Technology and Sciences, Hyderabad, Telangana, India

³ Department of Computer Science and Engineering (Data Science), CVR College of Engineering, Hyderabad, Telangana, India

⁴ Department of Computer Science and Engineering (Data Science), G. Narayanamma Institute of Technology and Sciences, Hyderabad, Telangana, India

⁵ Department of Computer Science and Engineering, CVR College of Engineering, Hyderabad, Telangana, India

^* ravikumar.c@suh.edu.in

Dr. Ravikumar Ch is an accomplished professional in computer science and engineering. He obtained his B.Tech. from Jawaharlal Nehru Technological University in 2004 and completed his M.Tech in 2011. He completed a Ph.D. in Computer Science and Engineering at Lovely Professional University. He is an Assistant Professor at Sreenidhi University and Osmania University. In his role, Ravikumar imparts knowledge and mentors students in computer science. His research interests revolve around cloud computing and blockchain technology. For any inquiries or further communication, he can be contacted at chrk5814@gmail.com.

Dr. D. Kavitha is an accomplished academic in computer science and engineering with over 16 years of teaching experience. She earned her B.Tech from JNTU Anantapur in 2005, M.Tech from JNTU Hyderabad in 2007, and Ph.D. from JNTU Hyderabad. At present, an Assistant Professor at G. Narayanamma Institute of Technology and Sciences, she has received the Best Research Paper Presentation Award at ICFE-2022. She is a Faculty Partner with Pegasystems and a former SPOC for Infosys Campus Connect. Her expertise includes machine learning, deep learning, AI, IoT, and data privacy. She actively mentors final-year projects and serves as a reviewer for national and international conferences.

Dr. N. Satyanarayana completed his Ph.D. (CSE) at Osmania University, Hyderabad, India, in 2020. He obtained his B.Tech. from Kakatiya University, Kothagudem, India, in 2007. He completed his M.Tech. From JNTUH, Hyderabad, India, in 2010. At present, he is working as a Professor in the Department of CSE (Data Science), CVR College of Engineering, Hyderabad, India. His research interests are data mining, bioinformatics, and machine learning. He has authored 40 research papers, out of which 5 are SCI papers and 10 are Scopus-indexed papers, and has approximately 100 overall citations. He has credit for six patents and four book chapters. He has more than 18 years of teaching experience in a wide variety of subjects, such as advanced data structures through C++, core Java, Python, problem-solving through C, DS through C, DBMS, OS, and DWDM. He has guided several B.Tech. and M.Tech. projects during his teaching career. He can be contacted at satyauce234@gmail.com.

Sukerthi Sutraya is a dedicated professional in the field of CSE (Data Science). She earned her B.Tech degree from Jawaharlal Nehru Technological University in 2006, followed by an M.Tech in 2008. She is currently pursuing her Ph.D. in Computer Science and Engineering at KL University. Sukerthi serves as an Assistant Professor at G. Narayanamma Institute of Technology and Science, where she teaches and mentors students in various areas of computer science. Her research interests include artificial intelligence and deep learning technologies. For any inquiries or further communication, she can be reached at sukerthi@gnits.ac.in.

R. Sahith received his M.Tech. in Computer Science and Engineering from JNTUH, India, and is currently pursuing his Ph.D. in Informatics at the Department of Computer Science and Engineering, Osmania University, Hyderabad, India. He is currently working as a Senior Assistant Professor in the Department of Computer Science and Engineering at CVR College of Engineering, Hyderabad, India. His research interests are programming and machine learning. He can be contacted at r.sahith@cvr.ac.in.

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Abstract

The development of microarray technology has facilitated expression profiling analysis for various medical and agricultural research areas. Despite the increasing range of applications, precision in isolating microarray images remains a challenge due to noise and variances in spot morphology. This research proposes a hybrid and adaptive clustering solution that offers significant improvement in terms of accuracy, segmentation, noise reduction, processing time, and overall efficiency. The study used an adaptive K-means clustering approach enhanced with genetic algorithms and bi-dimensional empirical mode decomposition. This hybrid framework achieved an average segmentation accuracy of approximately 95%, compared to 85% with conventional K-means, showing its superiority. In addition, the enhanced method achieved unparalleled noise reduction by 80% and improved signal-to-noise ratio by 200%, while maintaining efficiency with an average image processing time of 1.2 s. These results uniquely address complex challenges in microarray image analysis, unlocking new solutions critical for gene profiling in medicine and agriculture, and driving transformative advancements in the sectors.

Keywords

Adaptive Clustering / Bi-Dimensional Empirical Mode Decomposition / Genetic Algorithms / Microarray Image Analysis / Noise Reduction / Segmentation

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Ch Ravikumar, Dasari Kavitha, Nimmala Satyanarayana, Sutraya Sukerthi, Sahith R.. An adaptive hybrid clustering framework for high-precision microarray image segmentation using GA and BEMD. International Journal of Systematic Innovation, 2025, 9(5): 43-55 DOI:10.6977/IJoSI.202510_9(5).0004

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