A Robust Deep Learning-Based Approach for Detection of Breast Cancer from Histopathological Images

Abstract

Breast cancer is a frequently encountered and potentially lethal illness that can affect not only women but also men. It is the most common disease affecting women globally, and is the main cause of morbidity and death. Early and accurate detection of this risky disease is crucial. A timely and precise identification of breast cancer can decrease death rate and can also protect people from additional damage. The traditional methods used for breast cancer detection are very expensive in term of time and cost. The goal of this study is to develop a system which can detect the breast cancer accurately and at an early stage. The primary objective of this research study is to make use of histopathological images to identify breast cancer correctly and faster. In the proposed research work, we have developed a model with the name Breast Cancer Detection Network (BCDecNet), which comprises eleven learnable layers, i.e., eight convolution layers and three fully connected (FC) layers. The architecture has a total of twenty-nine layers, including one input layer, seven leaky ReLu (LR) layers, four ReLu layers, five maximum-pooling layers, six batch-normalization (BN) layers, one cross-channel normalization layer and three dropout layers, a softmax layer, and a classification layer. The proposed work uses image-based data taken from the Kaggle online repository. The suggested model achieved 97.33% accuracy, 96% precision, 96.5% recall and a 96.25% F1 score. Furthermore, the result of the proposed model was compared with other hybrid approaches used for diagnosis of breast cancer at early stages. Our model achieved a more satisfactory result than all other approaches used for breast cancer detection. Additionally, the proposed BCDecNet model can be generally applied to other medical-image datasets for diagnosis of various diseases.

Keywords

• detection
• breast cancer
• BCDecNet
• histopathological images