Özet:

The development of mobile technology has led to great advances in providing health services in many developed countries. In this research, cloud computing technology (MCC) was used through the use of mobile applications to employ a mobile health system. In this method, the mammogram image is transferred from the x-ray machine to the cloud using the Android platform in client-side. The technique used to detect breast cancer is the use of the convolutional neural network of the X-ray system to classify a mammogram into benign calcification, benign mass, malignant Calcification, malignant Mass, and normal. Because convolutional neural networks (CNNs) accelerate the diagnostic process with the support of a specialist in diagnosing tumors, they are therefore used to test for breast cancer. A set of mammography images were reprocessed to transform a mammogram that is visible to human into an understandable image for the computer. The parameters assigned were appropriate to the CNN classifier, and then trained a set of images as a source of training. Then produced a form to recognition the mammogram image. The results obtained show that the CNN classifier achieved an accuracy reached 91,039 on the DDSM (Digital Database of Screening Mammography) data.

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