A Survey on Brain Tumour Segmentation Techniques in Deep Learning
Keywords:
classification, MV-KBC, inconsistencies, computed tomography (CT), algorithmsAbstract
Machine learning has recently been used in hospitals to speed up the diagnosis and analysis process. To speed up the beginning of the recovery process, doctors can now get help with diagnosis. The future of AI in health care may involve tasks ranging from simple to complex, including answering the phone, assessing medical records, primary care trending and analytics, therapeutic medicine and computer design, reading radiology images, constructing medical treatment and diagnosis plans, and even conversing with patients. Deep learning models can interpret medical images like X-rays, MRI scans, CT scans, etc. to establish a diagnosis. The algorithms can spot risks and detect inconsistencies in the medical images. Deep learning is often used for cancer detection. MRI scans are needed to properly segment brain tumours, which can help with clinical diagnosis and treatment planning. However, medical practice is particularly difficult because some testing methods are not included in MRI pictures. When comparing the quantitative and qualitative findings of medical image analysis as it is now practised, the suggested approach performs better. The Chest CT images are superior at precisely identifying malignant lung nodules in the case of lung cancer detection. For patients' prospects of survival, early identification of lung cancer is essential. To identify between malignant and benign nodules, develop a multi-view knowledge-based collaborative (MV-KBC) deep model using sparse chest computed tomography (CT) data from previous study work. But the MV-KBC model was more precise. The model, however, is only usable with supervised image data. In order to address the model's limitation, we provide a unique deep learning-based multi view model in our research. For semi-supervised medical picture applications, the proposed model's accuracy was greatly increased, and calculation and classification times were reduced.
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