Mississippi State University
Duggar, William Neil
Date of Degree
Graduate Thesis - Open Access
Industrial and Systems Engineering
Master of Science (M.S.)
James Worth Bagley College of Engineering
Department of Industrial and Systems Engineering
Medical imaging is a key tool used in healthcare to diagnose and prognose patients by aiding the detection of a variety of diseases and conditions. In practice, medical image screening must be performed by clinical practitioners who rely primarily on their expertise and experience for disease diagnosis. The ability of convolutional neural networks (CNNs) to extract hierarchical features and determine classifications directly from raw image data makes CNNs a potentially useful adjunct to the medical image analysis process. A common challenge in successfully implementing CNNs is optimizing hyperparameters for training. In this study, we propose a method which utilizes scheduled hyperparameters and Bayesian optimization to classify cancerous and noncancerous tissues (i.e., segmentation) from head and neck computed tomography (CT) and positron emission tomography (PET) scans. The results of this method are compared using CT imaging with and without PET imaging for 2D and 3D image segmentation models.
Douglas, Zachariah, "Improving deep neural network training with batch size and learning rate optimization for head and neck tumor segmentation on 2D and 3D medical images" (2022). Theses and Dissertations. 5422.