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All Journal International Journal of Power Electronics and Drive Systems (IJPEDS) IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Reconfigurable and Embedded Systems (IJRES) International Journal of Applied Power Engineering (IJAPE) International Journal of Advances in Applied Sciences Bulletin of Electrical Engineering and Informatics
Rajanna, Bodapati Venkata
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Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Environmental Science Materials Science & Nanotechnology Mathematics Physics

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Journal : Bulletin of Electrical Engineering and Informatics

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Brain tumor classification using PCA-NGIST features with an enhanced RELM classifier Babu, Bukkapatnam Rakesh; Rajesh, Vullanki; Rajanna, Bodapati Venkata; Ahammad, Shaik Hasane
Bulletin of Electrical Engineering and Informatics Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i6.10742

Abstract

Brain tumours may cause severe health risks because of abnormal cell growth, which may result in organ malfunctions and death in adulthood. As precise identification of the tumour type is required for effective treatment. Magnetic resonance imaging (MRI) has recently been provided as an effective method for brain tumour diagnosis by computer-based based systems. To categorize brain tumours from MRI images, the paper offered a fusion model integrating an enhanced regularized extreme learning machine (RELM) classifier with principal component analysis (PCA) and normalized GIST (NGIST) feature extraction. While NGIST extracts strong spatial and texture features essential for modelling the tumour, PCA reduces the dimension of the input features without sacrificing significant data patterns. The improved RELM efficiently categorizes brain tumours into three categories: pituitary, meningioma, and glioma. It is optimized to improve learning capacity and generalization. The novelty of this study lies in the integration of NGIST descriptors with PCA-driven dimensionality reduction and an enhanced RELM classifier in a single lightweight framework. Unlike conventional methods that trade accuracy for computational cost, the proposed model ensures high precision and recall while remaining computationally efficient. This unique fusion demonstrates significant improvements in both diagnostic accuracy of 96% and clinical applicability, offering a balanced solution for real-time brain tumor classification.
Co-Authors Abotula, Sireesha Ahammad, Shaik Hasane Ahammad, Sk. Hasane Annavarapu, Mahalakshmi Babu, Bukkapatnam Rakesh Bagadi, Ravi Kiran Balaswamy, Chinthaguntla Batakala, Jeevanrao Bhavana, Mukku Chaturvedi, Abhay Cheerla, Sreevardhan Daniel, Ravuri Eamani, Ramakrishna Reddy Eragamreddy, Gouthami Himabindu, D. Inthiyaz, Syed Kallakunta, Ravi Kumar Kallakuta, Ravi Kumar Kameswari, Yeluripati Lalitha Karthik, Nachagari Karuturi, Kavya Ramya Sree Kiranbabu, Movva Naga Venkata Kodali, Prakash Kolukula, Nitalaksheswara Rao Koraganji, Neelima Santoshi Krishnaiah, Kondragunta Rama Kumar, Cheeli Ashok Kumar, Mugachintala Dilip Kumar, Munuswamy Siva Kumar, Yarrem Narasimhulu Vijaya Kumari, Popuri Rajani Meka, James Stephen Mohan, Kaja Krishna Mohana, Thota Naidu, Madhireddi Bhaskara Najumunnisa, Mohammad Nandaprakash, Nelaturi Parvez, Muzammil Pasam, Prudhvi Kiran Perumal, Chitra Pinajala, Jayasree Prakash, Nelaturi Nanda Prasad, Bode Raja, Banda Srinivas Rajasri, Kasula Rajesh, Vullanki Ramu, Tirunagari Bhargava Rani, Prathipati Ratna Sudha Rao, Bitra Janardhana Rao, Seetamraju Venkata Bala Subrahmanyeswara Ravikanth, Sivangi Reddy, Ganta Raghotham Reddy, Govulla Ravi Kumar Reddy, Gujjula Ramana Reddy, Mula Sreenivasa Reddy, Tadi Diwakara Subba Sai, Cheepurupalli Krishna Chaitanya Shashank, Ramagiri Sudarsa, Dorababu Sudhakar, Ambarapu Surendher, Guntukala Talagadadeevi, Srinivasa Rao Venkata Seshukumari, Bandreddi Vinodhkumar, Nallathambi Yadagiri, Aerpula