A METHOD TO IMPROVE THE ACCURACY OF DEEP LEARNING MODELS FOR BRAIN TUMOR DETECTION ON MRI IMAGES
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DOI:
https://doi.org/10.34238/tnu-jst.12185Abstract
This paper proposes a preprocessing method to enhance the accuracy of deep learning models in detecting and classifying brain tumors on magnetic resonance imaging scans. First, the input images are processed using a Gabor filter to highlight essential features, including edges, textures, and directional structures of the brain, thereby improving the ability to recognize the morphological characteristics of tumors. Next, since magnetic resonance images are often affected by noise during acquisition, the denoising autoencoder technique is applied to remove noise and enhance image quality. Finally, the deep learning model VGG16 is employed to classify four common types of brain tumors: no tumor, glioma, meningioma, and pituitary tumor. Experiments conducted on a large dataset with thousands of magnetic resonance images demonstrate that the proposed method improves model accuracy to 96.68%, outperforming traditional approaches. These results confirm the potential of deep learning in the early diagnosis and classification of brain diseases, contributing to advancements in modern medical support systems.Downloads
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