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Intelligent computer aided diagnosis system to enhance mass lesions in digitized mammogram images
by
AbuBaker, Ayman
, Yasin Ghadi, Yazeed
, Santarisi, Nader
in
Artificial neural networks
/ Breast cancer
/ Cancer
/ Diagnosis
/ Digitization
/ False positive results
/ Fuzzy logic
/ Image analysis
/ Image enhancement
/ Image restoration
/ Imagery
/ Inference
/ Intelligence
/ Labeling
/ Labels
/ Lesions
/ Linguistics
/ Mammography
/ Mass media images
/ Medical diagnosis
/ Medical imaging
/ Neural networks
/ Radiologists
2022
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Intelligent computer aided diagnosis system to enhance mass lesions in digitized mammogram images
by
AbuBaker, Ayman
, Yasin Ghadi, Yazeed
, Santarisi, Nader
in
Artificial neural networks
/ Breast cancer
/ Cancer
/ Diagnosis
/ Digitization
/ False positive results
/ Fuzzy logic
/ Image analysis
/ Image enhancement
/ Image restoration
/ Imagery
/ Inference
/ Intelligence
/ Labeling
/ Labels
/ Lesions
/ Linguistics
/ Mammography
/ Mass media images
/ Medical diagnosis
/ Medical imaging
/ Neural networks
/ Radiologists
2022
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Intelligent computer aided diagnosis system to enhance mass lesions in digitized mammogram images
by
AbuBaker, Ayman
, Yasin Ghadi, Yazeed
, Santarisi, Nader
in
Artificial neural networks
/ Breast cancer
/ Cancer
/ Diagnosis
/ Digitization
/ False positive results
/ Fuzzy logic
/ Image analysis
/ Image enhancement
/ Image restoration
/ Imagery
/ Inference
/ Intelligence
/ Labeling
/ Labels
/ Lesions
/ Linguistics
/ Mammography
/ Mass media images
/ Medical diagnosis
/ Medical imaging
/ Neural networks
/ Radiologists
2022
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Intelligent computer aided diagnosis system to enhance mass lesions in digitized mammogram images
Journal Article
Intelligent computer aided diagnosis system to enhance mass lesions in digitized mammogram images
2022
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Overview
The paper presents an intelligent system to enhance mass lesions in digitized mammogram images. This system can assist radiologists in detecting mass lesions in mammogram images as an early diagnosis of breast cancer. In this paper, the early detection of mass lesion is visually detected by enhancing mass lesions in mammogram images using hybrid neuro-fuzzy technique. Fuzzified engine is proposed as a first step to convert all pixels in mammogram image to a fuzzy value using three linguistic labels. After that, artificial neural networks are used instead of the inference engine to accurately detect the mass lesions in the mammogram images in a short time. Finally, five linguistic labels are used as a defuzzifier engine to restore the mammogram image. Processed mammogram images are extensively evaluated using two different types of mammogram resources, mammographic image analysis society (MIAS) and University of South Florida (USF) databases. The results show that the proposed intelligent computer aided diagnosis system can successfully enhance the mass lesions in mammogram images with minimum number of false positive regions.
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