International Journal of Advanced Technology and Engineering Exploration ISSN (Print): 2394-5443    ISSN (Online): 2394-7454 Volume-12 Issue-131 October-2025
  1. 4037
    Citations
  2. 2.7
    CiteScore
Deep learning–based classification of brain tumors from MRI images

N. Kopperundevi1, V. Lakshmi Praba2, S. Senthilkumar3, V. Mohan4 and Dhinesh Vijayakumar5

School of Computer Science and Engineering,Vellore Institute of Technology, Vellore, 632014,Tamil Nadu,India1
Department of Electronics and Communication Engineering,E.G.S. Pillay Engineering College, Nagapattinam, 611002,Tamil Nadu,India2
Department of Electronics and Communication Engineering,E.G.S. Pillay Engineering College, Nagapattinam, 611002,Tamil Nadu,India3
Department of Electrical and Electronics Engineering,E.G.S. Pillay Engineering College, Nagapattinam, 611002,Tamil Nadu,India4
APM and Data Analytics Lead,TMX,Toronto,Canada5
Corresponding Author : N. Kopperundevi

Recieved : 02-June-2024; Revised : 04-October-2025; Accepted : 14-October-2025

Abstract

The inherent complexity of brain tumor morphology and the variability among different tumor types present significant challenges for accurate classification. Traditional imaging analysis methods often fail to fully exploit the rich information available in magnetic resonance imaging (MRI), resulting in a critical research gap in developing automated and reliable tumor classification systems. This study introduces a novel deep learning model designed to improve the accuracy of brain tumor classification using only MRI brain images. The proposed model employs an advanced convolutional neural network (ACNN) architecture optimized for medical imaging, capable of capturing intricate patterns and features characteristic of specific tumor types. Multimodal brain tumor segmentation (BraTS2020) dataset has been used for experimentation. The collected data undergo pre-processing using the adaptive fuzzy filtering (AFF) method. Segmented images are then derived from these pre-processed images through wavelet transformation. From these segmented images, features are extracted using texture analysis techniques. These extracted features are subsequently classified using the proposed ACNN, where the network parameters are optimized through the nature-inspired artificial hummingbird algorithm (AHA), with accuracy maximization, which is serving as the primary fitness function. The optimized ACNN model classifies the final output into three categories: benign, pre-malignant, and malignant tumors. To evaluate the model’s performance, comprehensive metrics such as accuracy, precision, recall, and F1-score are employed for a thorough assessment. This approach effectively bridges the gap in automated brain tumor classification and provides a robust tool to support healthcare professionals in diagnosis and treatment planning. Overall, the study emphasizes the growing importance of MRI in medical imaging and showcases recent advancements in deep learning–based techniques for brain tumor analysis.

Keywords

Magnetic resonance imaging, Deep learning, Convolutional neural network, Artificial hummingbird algorithm, Medical image analysis.

Cite this article

Kopperundevi N, Praba VL, Senthilkumar S, Mohan V, Vijayakumar D. Deep learning–based classification of brain tumors from MRI images. International Journal of Advanced Technology and Engineering Exploration. 2025;12(131):1570-1587. DOI : 10.19101/IJATEE.2024.111100951

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