Karim, Dewan ZiaulZiaul, DewanAlam, Golam RabiulHaque, Hossain MD. HasibulApon, MD. Sayeed ArefinChowdhury, Dhrubo RashidImtiaz, Shahriar IslamMahi, Nishat Tasnim2024-09-232024-09-23©20242024ID 18101656ID 18201010ID 20101278ID 20101279ID 18201044http://hdl.handle.net/10361/24166Cataloged from PDF version of thesis.Includes bibliographical references (pages no. 45-47).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2024.One of the deadliest and most difficult tumors to cure is a brain tumor. Patients diagnosed with brain tumors tend to have a comparatively shorter lifespan. This tumor can affect any individual of any age. To mitigate the damages of brain tumors, early prognosis, and diagnosis are mandatory for a comparatively longer lifespan. Our primary goal is to develop a functional convolutional neural network (CNN) model that can reliably identify brain tumor cells in a patient’s magnetic resonance imaging (MRI). Unfortunately, this is a hard task as there are not many resources available as around 2 to 3 cases occur each year in 100,000 individuals in Bangladesh. For this purpose, a dataset was collected and augmented into a larger dataset by splitting, rotating, changing orientation, etc. Three categories were added to the dataset: training, validation, and testing where 70% of the data was for training, 15% for validation, and 15% for testing. Finally, we trained our dataset for 50 epochs to get the accuracy rate and then tested the same data sets with other pre-trained models like MobileNetV2, DenseNet121, and ResNet50. In this course of training our custom CNN model, we gained the highest accuracy rate, which is 97.07% in training, 95.99% in validation, and 96.51% for testing.58 pagesenBrac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.Brain tumorCNNMRIDiagnosisDeep learning.Magnetic resonance imaging.Brain tumors--Diagnosis.Thesis