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International Journal of Advanced Innovative Technology in Engineering (IJAITE)A Hybrid Genetic Algorithm and Optimized Vision Net Model for Accurate Brain Tumor Detection from MRI Images Ranjana Gawai Abstract : Accurate detection of brain tumors from Magnetic Resonance Imaging (MRI) is essential for effective diagnosis and treatment. This paper proposes a hybrid model, GA-VNet, integrating a Genetic Algorithm (GA) with a Vision Net architecture to improve classification performance. MRI images are first preprocessed through resizing, normalization, and noise reduction. Deep features are then extracted using Vision Net, to capture the critical spatial information. A Genetic Algorithm is applied for optimal feature selection, reducing redundancy and enhancing efficiency. The selected features are classified using a softmax layer. Experimental results show that the proposed model achieves 99.4% accuracy with high precision, recall, and F1-score, outperforming SVM, Random Forest, CNN, and ResNet50 models. Performance evaluation using confusion matrix, ROC, and PrecisionâRecall curves confirm the robustness of the approach. The proposed GA-VNet model provides an efficient and reliable solution for automated brain tumor detection. Keywords : Brain Tumor, Magnetic Resonance Imaging, Genetic Algorithm, Deep Learning Full Text : Download PDF DOI : 10.65809/IJAITE/26/v11i02/002 Cite this paper : - References : [1] MartĂnez-Del-RĂo-Ortega, R., Civit-Masot, J., Luna-PerejĂłn, F., & DomĂnguez-Morales, M. (2024). Brain Tumor Detection Using Magnetic Resonance Imaging and Convolutional Neural Networks. Big Data and Cognitive Computing, 8(9), 123. https://doi.org/10.3390/bdcc8090123 [2] Rahman, M. (2025). 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