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All Journal International Journal of Electrical and Computer Engineering Journal of Technology Research in Information System and Engineering
Vaidya, Archana S.
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Computer Science & IT Electrical & Electronics Engineering

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Performance analysis of hybrid bio-inspired algorithms for classifying brain tumors in imbalanced magnetic resonance imaging datasets Chakre, Rahul Ramesh; Vaidya, Archana S.; Patil, Dipak V.
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp6339-6350

Abstract

Magnetic resonance imaging (MRI) is a substantial imaging procedure for diagnosing brain tumors. However, brain tumor classification continues challenging due to the unequal distribution of classes within datasets, complicating precise diagnosis and classification. This research focuses on the class imbalance in medical image datasets by proposing a hybrid bio-inspired algorithm for brain tumor classification. A rider optimization and particle rider mutual information-based dendritic-squirrel search algorithm combined with an artificial immune classifier is developed and tested on imbalanced datasets generated from BRATS and SimBRATS. Experimental outcomes are encouraging, For the imbalanced BRATS dataset, the rider optimization- based classifier achieved an accuracy of 94.84%, sensitivity of 92.96%, and specificity of 94.95%. The particle rider mutual information-based classifier outperformed others with 96.25% accuracy, 94.33% sensitivity, and 94.85% specificity. For the imbalanced SimBRATS dataset, the rider optimization-based classifier achieved 94.95% accuracy, 92.05% sensitivity, and 94.04% specificity. The particle rider mutual information-based classifier excelled with 96.35% accuracy, 94.42% sensitivity, and 95.44% specificity. These findings suggest that the proposed algorithm effectively addresses class imbalance in medical image datasets, offering a robust solution for brain tumor classification. The particle rider mutual information-based classifier shows promise for enhancing diagnostic accuracy in clinical settings, demonstrating the efficacy of hybridized bio-inspired algorithms in managing imbalanced datasets.
A Automated Brain Tumor Classification using Deep Convolutional and Transfer Learning VIN, Vinodkumar; Vaidya, Archana S.; Patil, Manisha S.
JTRISTE Vol 13 No 1 (2026): JTRISTE
Publisher : STMIK KHARISMA Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55645/jtriste.v13i1.628

Abstract

Brain cancers are some of the fastest growing and most deadly types of neurological disease. Early detection with accuracy is very important to improve survival of patients. Manually reading MRI scans is a slow process. It requires special skills and can differ from one observer to another. It is in this context that the automatic computer-aided diagnosis has emerged as a vital research area. In this work we use deep learning based methods for classified various types of brain tumors using MRI. We developed a baseline convolutional neural network and compared it with four transfer-learning models: MobileNetV2, VGG16, VGG19, and ResNet50V2. To ensure data diversity and robustness, we merged two publicly available MRI tumor datasets and normalized, balanced, and pre-processed the data to a constant 224 × 224 pixel size for each image of the four categories: glioma, meningioma, pituitary tumor, and no tumor. The experimental results show that transfer learning performs significantly superior to the CNN baseline. ResNet50V2 became highly effective provided 97.2% accuracy, high precision, and excellent recall. These findings demonstrate that combining pre-trained neural networks with integrated datasets can provide better result, scalable framework for automated brain tumor identification.
Co-Authors Chakre, Rahul Ramesh Patil, Dipak V. Patil, Manisha S. VIN, Vinodkumar