Enhanced Invasive Ductal Carcinoma Prediction Using Densely Connected Convolutional Networks

Breast cancer is a heterogeneous disease that occurs when malignant cells form in the breast. It is the most common type of cancer in women but, it can also affect men. Due to its invasiveness and frequency of occurrence, breast cancer can be hard to diagnose. Although several approaches utilizing digital pathology and deep learning methods have successfully addressed the issue, these methods fail to capture some intrinsic and extrinsic cellular structural features required for precise automatic detection of Invasive Ductal Carcinoma (IDC) of the breast. Our proposed DenseBreast methodology involves the diagnosis of invasive ductal carcinoma with a densely connected convolutional network (DenseNet) to classify the IDC-affected histopathology images from the normal images. The benchmark dataset thus used to perform this task is the Breast Histopathology Images. The RGB microscopic images are first enhanced through our hybrid pre-processing technique based on color normalization, denoising, adaptive gamma correction (AGC), and contrast limited adaptive histogram equalization (CLAHE) with a 9% image quality improvement compared to the commonly used color normalization by Macenko. These images are then fed to the network which achieves an accuracy of 90%, a balanced accuracy of 87.2%, an improved f-score of 88.0%, and sensitivity/specificity of 80/95 % on a reduced dataset. Classification aptitude of the model is tested using standard performance metrics.