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All Journal Journal of Technology Research in Information System and Engineering
VIN, Vinodkumar
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Computer Science & IT

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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 Patil, Manisha S. Vaidya, Archana S.