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.
The human brain is the most interesting and intricate mechanism in the human body which is comprised of hundreds of billions of neurons and that has prompted a considerable lot of research of the organ. Some of the primary activities of the human brain are to govern muscles, and coordinate bodily movement, sensory perceptions, memory, learning, speech, emotions, intelligences and consciousness. The abnormal proliferation of cells in brain leads to the establishment of the tumor in brain. In this study effort, an automated brain tumor detection and segmentation technology is suggested. The suggested technique comprises of feature extraction, classification and segmentation. In this study, Gray Level Co-occurrence Matrix (GLCM) based features, Discrete Wavelet Transform (DWT) co-efficient and Laws texture features are employed. These characteristics are learned and categorised into either normal or pathological using Adaptive Neuro Fuzzy Inference System (ANFIS) classifier. Morphological procedures are conducted on the categorized abnormal brain imaging in order to separate the tumor areas.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.