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Title Detection And Classification Of Brain Tumor Using Deep Learning

Author(s) GHULAM HUSSAIN

Abstract A brain tumor is a potentially fatal condition caused by uncontrolled brain cell development that affects human brain cells and the neurological system. Brain tumors are among the leading causes of death worldwide. Therefore, for a patient to receive appropriate medication, a precise and early diagnosis of a brain tumor is essential. It also helps to avoid time-consuming and painful medical procedures. Manual brain tumor identification and treatment takes a long time, is complicated, and contains a human error. As a result, accurate automatic techniques are needed for the segmentation and classification of tumors. Machine learning techniques are employed for early detection and enhanced results. Precise tumor segmentation and classification are important in radio surgical planning and evaluating tumor treatment efficacy. The purpose of this research is to develop a deep learning-based system for segmenting and classifying brain tumors. In this study, a 3D U-Net model is used for the segmentation of MRI images, followed by 3D CNN for the classification of segmented images. BraTs 2019 datasets are used for training and testing the model. This model gets the accuracy of 96%.

Type Thesis/Dissertation MS

Faculty Engineering and Computer Science

Department Engineering

Language English

Publication Date 2023-06-06

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