Patch-based system for Classification of Breast Histology images using deep learning

• Published in: Computerized medical imaging and graphics 2019-01, Vol.71, p.90-103
• Main Authors: Roy, Kaushiki, Banik, Debapriya, Bhattacharjee, Debotosh, Nasipuri, Mita
• Format: Article
• Language: English
• Subjects: Accuracy; Artificial neural networks; Breast; Breast Neoplasms - diagnostic imaging; Breast Neoplasms - pathology; Classification; convolutional neural networks; Datasets; Deep Learning; Diagnosis, Differential; Diagnostic systems; Female; Histology; Histopathological breast images; Humans; Image classification; Image Processing, Computer-Assisted - methods; Invasiveness; majority voting; Neural networks; patch-based classifier; Patches (structures); Training
• ISSN: 0895-6111; 1879-0771
• DOI: 10.1016/j.compmedimag.2018.11.003

•In this work, we have developed a patch-based classifier (PBC) using the convolutional neural network (CNN) for automated classification of breast cancer histopathology images into 4-different histology class namely normal, benign, in situ and invasive carcinoma.•The developed patch-based classifier (PBC) uses an optimal architecture of a convolutional neural network (CNN), for automated classification of breast cancer histopathology images.•The proposed classification system works in two different modes: one patch in one decision (OPOD) and all patches in one decision (APOD). The patch labels are predicted by OPOD mode, and the result is obtained unanimously whereas in the APOD mode class label of the image is obtained by a majority voting scheme.•To verify the classification ability of the proposed system, the breast histopathological images are classified into 2 classes (non-malignant and malignant) as well as 4 classes (normal, benign, in situ and invasive carcinoma) while most of the existing methods classify the same broadly into 2 classes.•We have also explored the potentiality of our proposed model in classifying the images in the test dataset obtained by splitting the training set as well as the actual hidden test dataset of ICIAR-2018 breast cancer histology image dataset.•Our model achieves an accuracy of 87% in classifying the images of ICIAR-2018 hidden test dataset. In this work, we proposed a patch-based classifier (PBC) using Convolutional neural network (CNN) for automatic classification of histopathological breast images. Presence of limited images necessitated extraction of patches and augmentation to boost the number of training samples. Thus patches of suitable sizes carrying crucial diagnostic information were extracted from the original images. The proposed classification system works in two different modes: one patch in one decision (OPOD) and all patches in one decision (APOD). The proposed PBC first predicts the class label of each patch by OPOD mode. If that class label is the same for all the extracted patches and that is the class label of that image, then the output is considered as correct classification. In another mode that is APOD, the class label of each extracted patch is extracted as done in OPOD and a majority voting scheme takes the final decision about class label of the image. We have used ICIAR 2018 breast histology image dataset for this work which comprises of 4 different classes namely normal, benign, in situ a