Region Convolutional Neural Network for Brain Tumor Segmentation

Autor: R. Pitchai, K. Praveena, P. Murugeswari, Ashok Kumar, M. K. Mariam Bee, Nouf M. Alyami, R. S. Sundaram, B. Srinivas, Lavanya Vadda, T. Prince
Rok vydání: 2022
Předmět:
Zdroj: Computational Intelligence and Neuroscience. 2022:1-9
ISSN: 1687-5273
1687-5265
DOI: 10.1155/2022/8335255
Popis: Gliomas are often difficult to find and distinguish using typical manual segmentation approaches because of their vast range of changes in size, shape, and appearance. Furthermore, the manual annotation of cancer tissue segmentation under the close supervision of a human professional is both time-consuming and exhausting to perform. It will be easier and faster in the future to get accurate and quick diagnoses and treatments thanks to automated segmentation and survival rate prediction models that can be used now. In this article, a segmentation model is designed using RCNN that enables automatic prognosis on brain tumors using MRI. The study adopts a U-Net encoder for capturing the features during the training of the model. The feature extraction extracts geometric features for the estimation of tumor size. It is seen that the shape, location, and size of a tumor are significant factors in the estimation of prognosis. The experimental methods are conducted to test the efficacy of the model, and the results of the simulation show that the proposed method achieves a reduced error rate with increased accuracy than other methods.
Databáze: OpenAIRE
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