What is it about?
Detection and classification of various brain tumors from MR images using convolutional neural networks and multi-level thresholding concepts.
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Why is it important?
Our proposed approach incorporates advanced texture feature extraction methods, considering spatial correlations, and integrates data augmentation techniques to boost classification performance. Additionally, our framework introduces different strategies to optimize and streamline the training of deep learning models, ensuring efficient and accurate tumor detection in medical imaging.
Perspectives
Unlike many other works, this work is twofold. We first classified the normal and abnormal MR images. Later, tumors were detected in the abnormal images using advanced segmentation approaches.
Rajesh Kandala
VIT-AP University
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This page is a summary of: Brain MRI detection and classification: Harnessing convolutional neural networks and multi-level thresholding, PLoS ONE, August 2024, PLOS,
DOI: 10.1371/journal.pone.0306492.
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