Convolutional Neural Networks Grid Search Optimizer Based Brain Tumor Detection

Authors

  • Pushpak Kurella Department of Electrical and Electronics Engineering, Conestoga College, Doon campus, Ontario, Canada

Keywords:

Brain tumor detection, Deep learning, Convolution neural networks, Hidden markov random field

Abstract

The brain tissues segmented by MRI and CT provide a more accurate viewpoint on diagnosing various brain illnesses. Many different segmentation approaches may be used to brain MRI images. Some of the most successful include Histogram thresholding, area based segmentation (K-means, Expectation and Maximization (EM), Fuzzy connectivity, and Markov random fields (MRF). The Hidden Markov Random field (HMRF) approach is one of the most effective segmentation techniques available. It is capable of solving quickly distinct brain tissues for recognition purposes. Using the HMRF model allows for the reduction of energy consumption and the smoothing of images. In this work, the primary goal is to increase segmentation quality by implementing a unique Hidden Markov Random field model and employing MATLAB simulations to implement in Spatial Fuzzy, Iterative Conditional Mode (ICM) method, Fuzzy MRF technique, and Hidden Markov Random field model. The results will be compared to those obtained using Histogram thresholding, the Region Growing method (RGM), the k-means methodology, and the Expectation and Maximization methods to assess segmentation quality and noise reduction.

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Additional Files

Published

2023-12-30

How to Cite

Kurella, P. (2023). Convolutional Neural Networks Grid Search Optimizer Based Brain Tumor Detection. International Transactions on Electrical Engineering and Computer Science, 2(4), 183–190. Retrieved from http://iteecs.com/index.php/iteecs/article/view/68

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Articles