BREAST CANCER CAD SYSTEM BY USING TRANSFER LEARNING AND ENHANCED ROI
Muayed S AL-HUSEINY
aalhuseiny@uowasity.edu.iqWasit University, Department of Electrical Engineering (Iraq)
Ahmed S SAJIT
Wasit University, College of Engineering (Iraq)
Abstract
Computer systems are being employed in specialized professions such as medical diagnosis to alleviate some of the costs and to improve dependability and scalability. This paper implements a computer aided breast cancer diagnosis system. It utilizes the publicly available mini MIAS mammography image dataset. Images are preprocessed to clean isolate breast tissue region. Extracted regions are used to adjust and verify a pretrained convolutional deep neural network, the GoogLeNet. The implemented model shows good performance results compared to other published works with accuracy of 86.6%, sensitivity of 75% and specificity of 88.9%.
Keywords:
mammography, transfer learning, computer vision, image processingReferences
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Authors
Muayed S AL-HUSEINYaalhuseiny@uowasity.edu.iq
Wasit University, Department of Electrical Engineering Iraq
Authors
Ahmed S SAJITWasit University, College of Engineering Iraq
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