Khondaker, ArnishaKhan, Mushfiqur Rahman2024-09-242024-09-2420222022ID 19241006http://hdl.handle.net/10361/24177Cataloged from PDF version of thesis.Includes bibliographical references (pages 29-33).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022.Brain tumors result from the accumulation of abnormal cells inside the brain. The process of brain tumor segmentation plays a vital role in detecting early-stage brain tumors. There are several challenges in the tumor segmentation process due to the variations in size, location, and intensity of brain tissues. Traditional brain tumor segmentation methods are very time-consuming as segmentation is carried out manually. Automated segmentation methods are necessary for rapid diagnosis and treatment. In our paper, we highlight the complications surrounding a brain tumor and use an encoder-decoder-based approach of CNN algorithms to train segmentation models that will help identify and localize brain tumors with the utmost accuracy. We have used four CNN architectures to train our models, namely UNet, ResNet50, ResNext50, and EfficientNetB7. For our encoder-decoder models, we used ResNet50, ResNext50, and EfficientNetB7 as the encoder blocks of U-Net in three different models. Hence, we performed three experiments using these four architectures and compared their performance. We obtained the best results using the U-Net + EfficientNetB7 model.33 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.Bain tumorConvolutional Neural NetworkU-NetResNet50ResNext50EfficientNetB7SegmentationMRI imageNeural networks (Computer science)Image processing--Digital techniques.Thesis