i-manager Publications

Enhanced Approach for Brain Tumor Detection

Mar Kumar*, Vinuta Patil**, Sushitha Rachamalla***, Hepseeba Gajulavarthi****, Martha Bhavana*****

*-***** Sreyas Institute of Engineering and Technology, Hyderabad, India.

Periodicity:April - June'2023
DOI : https://doi.org/10.26634/jip.10.2.19818

Abstract

Automated defect detection in medical imaging has become an emerging field in several medical diagnostic applications. Automated detection of tumors in MRI is crucial as it provides information about abnormal tissues that are necessary for treatment. The conventional method for defect detection in magnetic resonance brain images is human inspection. This method is impractical due to the large amount of data. Hence, trusted and automatic classification schemes are essential to preventing the human death rate. So, automated tumor detection methods are being developed to save radiologist time and obtain tested accuracy. MRI brain tumor detection is a complicated task due to the complexity and variability of tumors. In this work, machine learning algorithms are proposed to overcome the drawbacks of traditional classifiers when tumors are detected in brain MRIs using machine learning algorithms. The outcome of the model is to predict whether a tumor is present or not in the image.

Keywords

Defect Dection, Classifiers, MRI, Brain Tumor, Algorithm, Magnetic Resonance, Machine Learning, Feature Extraction.

How to Cite this Article?

Kumar, M., Patil, V., Rachamalla, S., Gajulavarthi, H., and Bhavana, M. (2023). Enhanced Approach for Brain Tumor Detection. i-manager’s Journal on Image Processing, 10(2), 1-13. https://doi.org/10.26634/jip.10.2.19818

References

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Enhanced Approach for Brain Tumor Detection

Abstract

Keywords

How to Cite this Article?

References

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