Deep Learning Automates Brain Tumor Detection in MRI Scans

A new deep learning methodology has been developed to automate the detection of brain tumors from MRI images, addressing the challenges of manual interpretation and the complexity of brain structures. This approach leverages Convolutional Neural Networks (CNNs) and specifically employs transfer learning with pre-trained Residual Network (ResNet) architectures. Experiments conducted on a dataset of 3,929 brain MRI images compared ResNet18 and ResNet50. ResNet18 demonstrated superior performance, achieving a 97% accuracy rate for classifying MRI scans into tumor and non-tumor categories, slightly surpassing ResNet50's 96%. This indicates that a less deep model can generalize better on limited medical data. The proposed framework promises faster, more accurate, and cost-effective brain tumor detection, significantly aiding early diagnosis and clinical decision-making.