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Miu Sakaida, Takaaki Yoshimura, Minghui Tang, Shota Ichikawa and Hiroyuki Sugimori
Convolutional neural networks (CNNs) in deep learning have input pixel limitations, which leads to lost information regarding microcalcification when mammography images are compressed. Segmenting images into patches retains the original resolution when i...
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Dilovan Asaad Zebari, Dheyaa Ahmed Ibrahim, Diyar Qader Zeebaree, Mazin Abed Mohammed, Habibollah Haron, Nechirvan Asaad Zebari, Robertas Dama?evicius and Rytis Maskeliunas
Breast cancer detection using mammogram images at an early stage is an important step in disease diagnostics. We propose a new method for the classification of benign or malignant breast cancer from mammogram images. Hybrid thresholding and the machine l...
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Mohamed Tahoun, Abdulwahab Ali Almazroi, Mohammed A. Alqarni, Tarek Gaber, Emad E. Mahmoud and Mohamed Meselhy Eltoukhy
Breast cancer is one of the most prevalent cancer types with a high mortality rate in women worldwide. This devastating cancer still represents a worldwide public health concern in terms of high morbidity and mortality rates. The diagnosis of breast abno...
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