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Develop an automated framework for detecting and classifying brain tumors from MRI scans using advanced image processing and machine learning techniques.

January 8, 2018SHILAP Revista de lepidopterologíaOpen Access

Identification and classification of brain tumor MRI images with feature extraction using DWT and probabilistic neural network

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Develop an automated framework for detecting and classifying brain tumors from MRI scans using advanced image processing and machine learning techniques.
Applied noise reduction and discrete wavelet transform (DWT)-based region growing segmentation followed by morphological filtering.
Extracted texture characteristics using gray-level co-occurrence matrix (GLCM) feature extraction.
Trained and evaluated a probabilistic neural network (PNN) classifier to identify tumor locations and distinguish normal from abnormal brain tissue.
Achieved nearly 100% accuracy in classifying normal versus abnormal tissues in brain MRI images.
Demonstrated effective artifact reduction and boundary identification through post-segmentation morphological filtering.

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Implication

Diagnostic model evaluation reveals nearly 100% accuracy classifying brain tumor MRI scans, highlighting a robust automated framework for distinguishing abnormal neural tissue.

Cite This Study

Shree et al. (2018) studied this question.

synapsesocial.com/papers/69dd68470644c7b49d40d689 https://doi.org/10.1007/s40708-017-0075-5

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Implication

Diagnostic model evaluation reveals nearly 100% accuracy classifying brain tumor MRI scans, highlighting a robust automated framework for distinguishing abnormal neural tissue.

Identification and classification of brain tumor MRI images with feature extraction using DWT and probabilistic neural network

Key Points

Discussion

Implication

Cite This Study

Identification and classification of brain tumor MRI images with feature extraction using DWT and probabilistic neural network

Key Points

Discussion

Implication

Cite This Study

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