Rapid Assessment of Breast Cancer Malignancy Using Deep Neural Network

Breast cancer is one of the deadly diseases that have high morbidity and mortality rate. Traditionally, doctors or radiologists should delineate the malignancy suspicious in a manual procedure such as manual segmentation or making diagnosis decisions. This medical examination may occur some problems such as time-consuming, tedious, and possible to produce subjective results. An alternative way to overcome this problem is that implementing technology to support this examination process. Hence, we propose a deep neural network model to reach the development of a rapid classification system for breast cancer. Our model was performed in the Breast Cancer Wisconsin (Diagnostic) dataset consisting of 569 instances and 30 attributes. Our proposed model was started by balancing the data in the preparation step. After conducting the preparation step, we performed a deep neural network model with three hidden layers and two dropout layers. Our experiment achieved the best performance compared to the previous study with accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of 98.536, 98.466, 98.765, 99.689, 94.118, and 98.500 respectively which indicates that our proposed method has suitable performance for assessing cancerous of breast cancer. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.