Machine Learning in Healthcare
An approach for breast cancer detection on histopathological images
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Breast cancer is a disease characterized by rampant proliferation of cells that can lead to the appearance of tumors near the breast region. However, the diagnosis of breast cancer has faced several challenges to health professionals and specialists, since it is difficult to analyze biopsy samples, for example. With this in mind and considering the computational advance of computer vision techniques for image recognition, many researchers and experts consider that the use of CAD (Computer-Aided Diagnosis) systems to aid in the diagnosis of breast cancer through the use of image processing and ...
Breast cancer is a disease characterized by rampant proliferation of cells that can lead to the appearance of tumors near the breast region. However, the diagnosis of breast cancer has faced several challenges to health professionals and specialists, since it is difficult to analyze biopsy samples, for example. With this in mind and considering the computational advance of computer vision techniques for image recognition, many researchers and experts consider that the use of CAD (Computer-Aided Diagnosis) systems to aid in the diagnosis of breast cancer through the use of image processing and machine learning techniques can contribute positively in the diagnosis of breast cancer among experts. Thus, this work implements three CNN approaches of VGG-16 architecture using the techniques of knowledge transfer and color transfer, aiming to propose and evaluate a solution for the detection of breast cancer in histopathological cancer images using CNN with knowledge transfer and color transfer.
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