Date of Award
Fall 7-2022
Degree Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
School
Computing Sciences and Computer Engineering
Committee Chair
Dr. Beddhu Murali, Committee Chair
Committee Chair School
Computing Sciences and Computer Engineering
Committee Member 2
Dr. Andrew Sung
Committee Member 2 School
Computing Sciences and Computer Engineering
Committee Member 3
Dr. Sarah B. Lee
Committee Member 3 School
Computing Sciences and Computer Engineering
Committee Member 4
Dr. Amer Dawoud
Committee Member 4 School
Computing Sciences and Computer Engineering
Committee Member 5
Dr. Sungwook Lee
Committee Member 5 School
Mathematics and Natural Sciences
Abstract
Machine Learning and Artificial Intelligence have made significant progress concurrent with new advancements in hardware and software technologies. Deep learning methods heavily utilize parallel computing and Graphical Processing Units(GPU). It is already used in many applications ranging from image classification, object detection, segmentation, cyber security problems and others. Deep Learning is emerging as a viable choice in dealing with today’s real-time medical problems. We need new methods and technologies in the field of Medical Science and Epidemiology for detecting and diagnosing emerging threats from new viruses such as COVID-19. The use of Artificial Intelligence in these domains is becoming more accepted, not as a replacement for current medical practices but rather as an enhancing and augmenting tool to current practices. This dissertation is about the Application of Deep Learning for Medical Sciences and Epidemiology Data analysis and Diagnosis Modeling. The work started with an analysis of existing data, then focused on developing diagnosis models using both supervised and unsupervised approaches. This resulted in a renal transplantation recommender system, breast cancer diagnosis model and COVID-19 diagnosis models. The recent completed work for COVID-19 diagnosis modeling leverage the potential of more advanced deep neural network model and vision transformer. The results are very promising and outperformed all existing models. The final diagnosis model has three kinds of dataset. For Chest X-Ray(CXR) image dataset training accuracy is 95.9376 %, validation accuracy is 96.1667 % and testing accuracy is 95.1250 %. For Chest CT image dataset training accuracy is 96.5474 %, validation accuracy is 95.7302 % and testing accuracy is 97.0588 %. For the Combined CXR and Chest CT image dataset training accuracy is 95.6665 %, validation accuracy is 96.6961% and testing accuracy is 97.8859 %.
ORCID ID
https://orcid.org/ 0000-0002-3880-0308
Copyright
SOMENATH CHAKRABORTY
Recommended Citation
Chakraborty, Somenath, "APPLICATION OF DEEP LEARNING FOR MEDICAL SCIENCES AND EPIDEMIOLOGY DATA ANALYSIS AND DIAGNOSTIC MODELING" (2022). Dissertations. 2035.
https://aquila.usm.edu/dissertations/2035
Included in
Biomedical Engineering and Bioengineering Commons, Computational Engineering Commons, Computer Engineering Commons