Abnormality classification and localization using dual-branch whole-region-based CNN model with histopathological images. (October 2022)

• Record Type: Journal Article
• Authors: Oyelade, Olaide N.; Ezugwu, Absalom E.; Venter, Hein S.; Mirjalili, Seyedali; Gandomi, Amir H.
• Is Part Of: Computers in biology and medicine. Volume 149(2022)
• Publication Date: 2022-10
• Subjects: Classification -- Localization -- Breast cancer -- Deep learning -- Convolutional neural network -- CNN; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2022.105943
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: The task of classification and localization with detecting abnormalities in medical images is considered very challenging. Computer-aided systems have been widely employed to address this issue, and the proliferation of deep learning network architectures is proof of the outstanding performance reported in the literature. However, localizing abnormalities in regions of images that can support the confidence of classification continues to attract research interest. The difficulty of using digital histopathology images for this task is another drawback, which needs high-level deep learning models to address the situation. Successful pathology localization automation will support automatic acquisition planning and post-imaging analysis. In this paper, we address issues related to the combination of classification with image localization and detection through a dual branch deep learning framework that uses two different configurations of convolutional neural networks (CNN) architectures. Whole-image based CNN (WCNN) and region-based CNN (RCNN) architectures are systematically combined to classify and localize abnormalities in samples. A multi-class classification and localization of abnormalities are achieved using the method with no annotation-dependent images. In addition, seamless confidence and explanation mechanism is provided so that outcomes from WCNN and RCNN are mapped together for further analysis. Using images from both BACH and BreakHis databases, an exhaustive set of experiments was carried out to validate the performance of the proposed method in achieving classification and localization simultaneously. Obtained results showed that the system achieved a classification accuracy of 97.08%, a localization accuracy of 94%, and an area under the curve (AUC) of 0.10 for classification. Further findings from this study revealed that a multi-neural network approach could provide a suitable method for addressing the combinatorial problem of classification and localization anomalies in digital medical images. Lastly, the study's outcome offers means for automating the annotation of histopathology images and the support for human pathologists in locating abnormalities. Highlights: A novel dual-branch deep learning model for image-level and region-level classification and localization was developed. Application of combinatorial problem of abnormality classification and localization to histopathology images samples. A probability map comparator mechanism capable of verifying coloration of two CNN models in the dual-branch model was employed. Accurate localization performance using heatmap was achieved with the dual-branch deep learning model.