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Description / Abstract
Submitted to the Department of Mathematics and Computer Science in partial fulfillment of the requirements for the degree of Bachelor of Science
Prostate cancer is the second most common cancer in men. Its high five-year relative survival rate hinges on the identification of cancer, especially before it spreads. A negative misdiagnosis can be deadly, which creates a need for a consistently accurate method of identification. This research sought to develop a computer vision software tool that, given a digital image of a stained prostate biopsy, locates any malignant glands present in the image. A three-step process was devised for this: first, run supervised machine learning classifiers to mark the key cellular structures that point to adenocarcinoma of the prostate—nuclei, nucleoli, and lumina. Second, analyze those structures for key traits such as size and clustering. Third, use these derived traits in a second round of classification to locate cancerous regions. A support vector machine and decision tree were used for step one with reasonable success—nuclei and lumina were found with high accuracy, but nucleoli identification was troublesome. Better accuracy than this is desired. Future work includes determining the value of continuing with this three-step method, and if so refining step one and completing steps two and three. Otherwise, a new classification algorithm such as a convolutional neural network will be investigated.Download this Resource
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File Size Small - 3.4 MB
Copyright NotesNon-exclusive right to publish on file at NNU Library. Available on request.
Keywordscomputer vision software, machine learning, classification algorithms, cancer identification
Degree AwardedBachelor of Science
Awarding InstitutionNorthwest Nazarene University
Committee MembersStephen Riley
Supervisor or ChairBarry L. Myers
Peer Reviewed?No
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NNU Computer Science Theses
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