AI Medical Diagnosis with ResNet50 for Brain Tumor Detection
Brain tumors such as glioma, meningioma, and pituitary tumors pose significant health risks and can be life-threatening if not detected early. Using deep learning techniques, particularly ResNet50, for classification of brain MRI scans can enhance diagnostic accuracy. This approach facilitates the identification of various tumor types, aiding in timely treatment.
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Implementation Highlights
- ResNet50 model is utilized for automatic feature extraction and classification of brain tumor types.
- MRI images are preprocessed to 200×200 pixel resolution to ensure optimal model performance.
- Transfer learning is applied by using pre-trained weights on large image datasets to improve model generalization.
- Data augmentation methods, including rotations and flipping, are used to diversify the training dataset and reduce overfitting.
Outcome
The model successfully classifies MRI scans into categories: glioma, meningioma, pituitary tumor, and no tumor. This approach offers a reliable tool for early diagnosis and supports clinical decision-making. The system demonstrates high accuracy in tumor detection and provides timely results for medical practitioners.