Optimized CNN-based Brain Tumor Segmentation and Classification using Artificial Bee Colony and Thresholding
DOI:
https://doi.org/10.15837/ijccc.2023.1.4577Keywords:
Artificial Bee Colony , Brain tumor, Magnetic Resonance Image, Medical Image Analysis, Thresholding methodAbstract
One of the most important tasks used by the medical profession for disease identification and recovery preparation is automatic medical image processing. Statistical approaches are the most commonly used algorithms, and they consist several important step. Brain tumors are the foremost causes of death of cancerous diseases all over the world. The hippocampus is the human body’s primary control structure. Since a tumor attacks the brain, it can kill the patient if it is not detected early. Among the various imaging modalities available, Magnetic Resonance Image (MRI) is a better implement for calculating area and classifying tumors based on their grade. MRI does not emit any toxic radiation. There is currently no automated method for detecting and identifying the grade of a tumor. This study mainly focusses on classifying and segmenting brain tumors from MRI scan data. It aids physicians in the planning of future care or surgery. This procedure consists of four steps: image de-noising, tumor extraction, attribute extraction, and hybrid classification. In the first step of image de-noising, the curvelet transformation (CT) is used. Then, in the next stage, Artificial Bee Colony (ABC) Optimization is used in conjunction with the thresholding process to remove tumors from brain MRI scans. Another optimization approach is used to recover the learning rate of the Convolutional Neural Network for the final hybrid classification. The experiment model is assessed by using the multimodal brain tumor (BRATS) 2013 and 2015 challenge datasets from medical image computing. The outcomes of the experiment presented that the method achieved the segmentation 95.23% and 94% of accuracy, where the proposed optimized CNN achieved classification accuracy of 98.5% and 99% for both datasets.
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