Authors : Sharmistha Paul; Shilpita Saha; Pritikona Maji

Volume/Issue : Volume 10 - 2025, Issue 5 - May

Google Scholar : https://tinyurl.com/yc63p9s6

DOI : https://doi.org/10.38124/ijisrt/25may855

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Brain tumours pose a critical healthcare challenge globally due to their potential for rapid progression and diagnostic complexity. In this research, we present a custom-built convolutional neural network (CNN) designed from scratch for the automatic detection and classification of brain tumours from magnetic resonance imaging (MRI). The model classifies images into four categories: glioma, meningioma, pituitary tumour, and no tumour. A total of 7024 MRI images were utilized, with a 90:10 train-test split. Performance was evaluated using metrics including accuracy, loss, precision, recall, and F1-score. Our model achieved a test accuracy of 96%, outperforming popular pretrained models including VGG16, ResNet50, and MobileNetV2. Notably, our CNN model uses smaller image dimensions (150×150) and does not rely on data augmentation, leading to reduced memory consumption. The study includes a comparative analysis and highlights the model's potential in supporting early and reliable diagnosis, particularly in resource-limited clinical settings.

Keywords : Brain Tumour Detection; MRI Classification; Deep Learning; Custom CNN; Medical Imaging; Transfer Learning; VGG16; Mobilenetv2; Resnet50; India.

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