Mobile Cloud Platform for Breast Cancer Diagnosis Using Deep Learning

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AreejRebat Abed, Dr.Karim Q. Hussein

Abstract

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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