Theses and Dissertations

Issuing Body

Mississippi State University


Wang, Haifeng

Committee Member

Duggar, William Neil

Committee Member

Bian, Linkan

Date of Degree


Document Type

Graduate Thesis - Open Access


Industrial and Systems Engineering

Degree Name

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.