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Brain tumor detection from MRI medical images based on machine learning algorithms

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dc.contributor.author Omy, Jannatul Faria
dc.date.accessioned 2024-05-18T05:14:27Z
dc.date.available 2024-05-18T05:14:27Z
dc.date.issued 2023-12-21
dc.identifier.uri http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/12414
dc.description.abstract This study aims to develop an efficient and accurate system for the early detection of brain tumors using machine learning algorithms applied to magnetic resonance imaging (MRI) medical images. Brain tumor occurs because of anomalous development of cells. It is one of the major causes of death in adults around the globe. Millions of deaths can be prevented through early detection of brain tumors. Earlier brain tumor detection using Magnetic Resonance Imaging (MRI) may increase a patient's survival rate.machine learning) has gained prominence in almost every field where decision-making is involved in recent years, spanning economics, health care, marketing, and sales. In the field of healthcare, machine learning & deep learning have shown promising results in a variety of fields, namely disease diagnosis with medical imaging, surgical robots, and boosting hospital performance. One such application of deep learning to detect brain tumors from MRI scan images. In MRI, tumor is shown more clearly that helps in the process of further treatment. This work aims to detect tumors at an early phase. A comprehensive dataset of MRI scans, encompassing both tumor and non-tumor cases, is utilized to train and validate the proposed machine learning models. Preprocessing techniques, including image enhancement and normalization, are applied to standardize the input data. Various machine learning algorithms, such as convolutional neural networks (CNNs), MobileNet model with ImageNet weights from keras and decision trees, are implemented and compared to identify the most effective approach for brain tumor detection.In this research of brain tumor classification, using machine learning, and built a binary classifier to detect brain tumors from MRI scan images. The classifier used transfer learning and obtained an accuracy of 96.5% and visualized the model’s overall performance.The presents a model which is based on machine learning algorithms to detect brain tumors from magnetic resonance images with high accuracy. A Convolutional Neural Network (CNN) has been used as the algorithm for feature extraction, and segmentation. The dataset used has been acquired from kaggle. en_US
dc.publisher Daffodil International University en_US
dc.subject Image segmentation en_US
dc.subject CNN en_US
dc.subject Augmentation en_US
dc.subject Image classification, en_US
dc.subject Prediction en_US
dc.subject Machine learning en_US
dc.subject Deep Learning en_US
dc.subject Algorithms en_US
dc.title Brain tumor detection from MRI medical images based on machine learning algorithms en_US
dc.type Thesis en_US


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