An efficient Berkeley’s wavelet convolutional transfer learning and local binary Gabor fuzzy C-means clustering for brain tumour detection

ABSTRACT

Manual diagnosis of brain tumour tissues is particularly labourintensive as well as operator-dependent due to the intricacy of brain tissue. Traditional approaches are ineffective in the presence of these effects, necessitating the assessment of the photographs by professionals who can identify them. This research proposes a novel technique in brain tumour detection based on segmentation with classification utilizing DL architectures. Here, input has been collected as various brain slice image datasets. Initially, this image has been processed for resizing and smoothening and this image has been segmented. The segmentation has been carried out using local binary Gabor fuzzy C-means clustering. Then the segmented image has been classified for spotting the tumour using Berkeley’s wavelet convolutional transfer learning. Based on the accuracy, sensitivity, specificity, Jaccard’s coefficient, spatial overlap, AVME, and FoM, the creative outcome of the approach used was evaluated.

KEYWORDS:

• Brain tumours
• segmentation
• classification
• DL
• brain slice image datasets
• fuzzy C-means clustering
• Berkeley’s wavelet
• convolutional transfer learning

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No potential conflict of interest was reported by the author(s).

Human and animal rights

This article does not contain any studies with human or animal subjects performed by any of the authors.

Informed consent

Informed consent was obtained from all individual participants included in the study.

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Data sharing is not applicable to this article as no new data were created or analyzed in this study.

Additional information

Notes on contributors

M. Padma Usha

M. Padma Usha, Pursuing PhD from B.S.Abdur Rahman Crescent Institute of Science and Technology Chennai, India, she received M.E Communication Systems, degree from Anna University Chennai, India, and B.Tech Electronic Communication Engineering degree from Anna University Chennai, India, in the year 2008 and 2005 respectively. At present she is working as Assistant Professor (Sr.Gr.) in the Department of Electronics and Communication Engineering of B.S.Abdur Rahman Crescent Institute of Science and Technology Chennai, India. She has 14 years of teaching experience and doing areas of research include Medical Image processing, AI & Machine Learning. She has published 6 papers in well reputed international journals, 5 papers in international conferences and more than 7 papers in national conferences.

G. Kannan

G. Kannan received Ph.D degree from Anna University Chennai, India, an M.Tech Embedded Systems from SASTRA University Thanjavur and B.E Electronics and Instrumentation Engineering from Bharadhidasan University Tiruchirappalli in the year 2014, 2005 and 2000 respectively. At present he is working as Associate Professor in the Department of Electronics and Communication Engineering of B.S.Abdur Rahman Crescent Institute of Science and Technology Chennai, India. He has 15 years of teaching & research experience and his areas of research include Wireless Sensor Network, System level power management in Embedded systems, Real Time Operating Systems and Artificial Intelligence. He has published 14 papers in well reputed international journals, 16 papers in international conferences and more than 25 papers in national conferences.

M. Ramamoorthy

M. Ramamoorthy, B.Tech., M.E., Ph.D., Head of Department & Professor, Department of the Artificial Intelligence and Machine Learning, Saveetha School of Engineering, Saveetha Institute of Medical And Technical Sciences (SIMATS), Chennai. He has Completed his Ph.D. Degree in Information Technology from B.S Abdur Rahman Crescent University, Tamilnadu, India in 2021, He did his M.E. Degree in Computer Science and Engineering from V.M.K.V Engineering College, Vinayaka Mission University, Salem, India in 2009, He has received the B.Tech. Degree in Information Technology from Anna University, Chennai, India, in the year 2005. He is a Member of CSI chapter and IAENG. He has 16+ years of teaching experience in UG & PG Engineering courses. He has published 18 papers in well reputed international journals, 10 papers in international conferences, more than 20 papers in national conferences, he has 5 Patents Published and 3 Books Published. His current Research interests are Medical Video Analytics, Bio Medical Image Processing, and AI & Machine Learning.