An Efficient Deep Learning Technique for Brain Abnormality Detection Using MRI

• Shilpa Mahajan ^[1] ; Anuradha Dhull ^[1] ; Aryan Dahiya ^[1]
  1. [1] The NorthCap University, India
• Localización: Inteligencia artificial: Revista Iberoamericana de Inteligencia Artificial, ISSN-e 1988-3064, ISSN 1137-3601, Vol. 28, Nº. 75, 2025, págs. 81-100
• Idioma: inglés
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• Resumen

  • This research proposes an effective and reliable deep learning method for detecting brain abnormalities via magnetic resonance imaging (MRI). The technique consists of two primary stages: first, a binary classifier that divides pictures into "Brain" and "Non-Brain" categories; second, multi-class classifiers that explicitly recognise categories such pituitary adenomas, gliomas, and meningiomas. The labelled and preprocessed data were taken from a collection of 7,753 pictures provided by Qhills Technologies Pvt. Ltd. Additional data from the Brain Tumour MRI collection was also incorporated to improve the model's generalisation skills. VGG-16 outperforms the other machine learning models, with an accuracy rate of 96.4%, when compared to ANN, CNN, VGG-16, and AlexNet. A thorough model evaluation and hyperparameter tweaking process was conducted using the accuracy, precision, recall F1-score. The findings of this study point to the potential of deep learning techniques in identifying brain disorders fast and precisely, opening the door to more precise diagnosis in clinical settings.