Mobile Cloud Platform for Breast Cancer Diagnosis Using Deep Learning
Areej Rebat Abed, Karim Q. Hussein · Turkish Journal of Computer and Mathematics Education (TURCOMAT) · 2021
The development of mobile technology has led to great advances in providinghealth 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. Inthis method, the mammogram image is transferred from the x-ray machine to the cloud usingthe Android platform in client-side. The technique used to detect breast cancer is the use ofthe convolutional neural network of the X-ray system to classify a mammogram into benigncalcification, benign mass, malignant Calcification, malignant Mass, and normal. Becauseconvolutional neural networks (CNNs) accelerate the diagnostic process with the support of aspecialist in diagnosing tumors, they are therefore used to test for breast cancer. A set ofmammography images were reprocessed to transform a mammogram that is visible to humaninto an understandable image for the computer. The parameters assigned were appropriate tothe CNN classifier, and then trained a set of images as a source of training. Then produced aform to recognition the mammogram image. The results obtained show that the CNNclassifier achieved an accuracy reached 91,039 on the DDSM (Digital Database of ScreeningMammography) data.