Brain Tumour Prediction Using MRI Images with MobileNet and Transfer Learning: A Deep Learning Approach

Brain tumour detection and classification are crucial for early diagnosis, improving patient survival rates. Magnetic Resonance Imaging (MRI) offers non-invasive, high-resolution brain images, but manual interpretation is time-consuming and error prone. This research proposes a deep learning approach for brain tumour prediction using MRI images, leveraging the MobileNet architecture with transfer learning, incorporating Flatten, Dense, and Dropout layers to enhance model generalization. MobileNet, a lightweight Convolutional Neural Network (CNN), efficiently processes large-scale MRI datasets. The pre-trained MobileNet is integrated with a Flatten layer to reduce feature map dimensions, followed by Dense layers for learning complex patterns, and a Dropout layer to prevent overfitting. Transfer learning allows leveraging pre-trained weights, reducing the need for large datasets. The model was finetuned on an MRI brain tumour dataset, achieving an accuracy of 0.9845 with a loss of 0.0015, outperforming traditional deep learning methods in tumour classification. This architecture is computationally efficient, making it suitable for real-time clinical applications, providing medical professionals with a reliable, automated tool for brain tumour diagnosis.