Publication: Brain tumor segmentation with mri images
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Date
2023-08
Authors
Khoo, Rong Quan
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Abstract
This thesis addresses the critical issue of brain tumor segmentation in 3D magnetic resonance imaging (MRI) scans, aiming to enhance early detection and diagnosis for improved patient outcomes. Brain tumors, among the most serious and life-threatening types, necessitate accurate identification and treatment planning. However, manual human-assisted classification from a vast number of MRI scans is time-consuming and error-prone, underscoring the need for
automated segmentation methods. The study adopts deep learning techniques, with a focus on the U-Net architecture, which effectively captures both low-level details and high-level semantic information through CNN-based skip connections, facilitating precise segmentation. During the training process, the model benefits from a combination of Dice loss and categorical focal loss functions, enabling a comprehensive optimization approach for segmentation tasks. To evaluate the model's effectiveness, essential metrics such as sensitivity, specificity, accuracy, Intersection over Union (IoU), and F1 score are utilized. These metrics offer a holistic assessment, capturing segmentation accuracy, class imbalance, and overall correctness in identifying positive and negative instances. The optimal development of an efficient and accurate brain tumor segmentation model, as demonstrated by the evaluation metrics Sensitivity: 0.9784, Specificity: 0.9930, Accuracy: 0.9342, IoU, and F1 score has promising implications for medical image processing. Automating the segmentation process through this research contributes to the advancement of early detection and diagnosis, potentially improving patient recovery rates and treatment planning for individuals affected by brain tumors in the future medical field. This research paves the way for transformative advancements in the medical field, harnessing the power of deep learning techniques for accurate brain tumor segmentation. As technology continues to evolve, such automated approaches hold immense potential in revolutionizing medical imaging practices, ultimately contributing to better healthcare and a positive impact on patients' lives