Authors:
Arushi Raghuvanshi
1
and
Marek Perkowski
2
Affiliations:
1
Jesuit High School, United States
;
2
Portland State University, United States
Keyword(s):
Melanoma, Skin Cancer, Image processing, Machine Learning, Medical Diagnosis.
Related
Ontology
Subjects/Areas/Topics:
Biomedical Engineering
;
Biomedical Instruments and Devices
;
Computer-Aided Detection and Diagnosis
;
Devices
;
Health Monitoring Devices
;
Human-Computer Interaction
;
Imaging and Visualization Devices
;
Physiological Computing Systems
;
Technologies Evaluation
Abstract:
Melanoma cancer is one of the most dangerous and potentially deadly types of skin cancer; however, if diagnosed early, it is nearly one-hundred percent curable (UnderstMel09). Here we propose an efficient system which helps with the early diagnosis of melanoma cancer. Different image processing techniques and machine learning algorithms are evaluated to distinguish between cancerous and non-cancerous moles. Two image feature databases were created: one compiled from a dermatologist-training tool for melanoma from Hosei University and the other created by extracting features from digital pictures of lesions using a software called Skinseg. We then applied various machine learning techniques on the image feature database using a Python-based tool called Orange. The experiments suggest that among the methods tested, the combination of Bayes machine learning with Hosei image feature extraction is the best method for detecting cancerous moles. Then, using this method, a computer tool was
developed to return the probability that an image is cancerous. This is a very practical application as it allows for at-home findings of the probability that a mole is cancerous. This does not replace visits to a doctor, but provides early information that allows people to be proactive in the diagnosis of melanoma cancer.
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