Comparative Analysis of Convolutional Neural Network (CNN) and Transfer Learning in Breast Cancer Detection

E. Effah, S. Mensah, J. K. Aidoo

Abstract


Breast cancer is a type of cancer that is prevalent among women. Every year many women succumb to the fatality of breast cancer in Sub-Saharan Africa (SSA). The cancerous cells can metastasize to other parts of the body. Although preventive measures remain elusive to medical professionals, when detected in its early stages, measures can be taken to prevent fatality. However, health professionals make false positive and false negative diagnoses given that lumps are found within the breasts. In SSA, which is the focus of this research, most people mainly resort to physical examination predictive technique only, and often, during breast cancer awareness month. Additionally, individuals are not able to check on their own mammographs they have taken for the absence or presence of cancerous lumps. This research seeks to develop and integrate a machine learning model in a web application for detecting breast cancer when a mammogram is uploaded. To do this a comparison analysis is performed between two notable deep learning techniques; Convolution Neural Network (CNN) and Transfer Learning (TL) (MobileNetV2). Findings reveal the MobileNetV2 obtained a training and validation of 90.4% and 90.7% respectively. Greater than that of the CNN model which obtained training and validation of 88% and 88.7% respectively. Hence the MobileNetV2 model was integrated into the web application for easy accessibility to both health professionals and individuals. Furthermore, current result can be scaled to integrate more efficient techniques in the future scope.


Keywords


Breast Cancer, Deep Learning, Machine Learning, Transfer Learning, Web Application.

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References


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