Convolutional Deep Belief Network with Feature Encoding for Classification of Neuroblastoma Histological Images

• Published in: Journal of pathology informatics Vol. 9; no. 1; p. 17
• Main Authors: Gheisari, Soheila, Catchpoole, Daniel R., Charlton, Amanda, Kennedy, Paul J.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.01.2018; Wolters Kluwer India Pvt. Ltd; Medknow Publications & Media Pvt. Ltd; Medknow Publications & Media Pvt Ltd; Elsevier
• Subjects: Accuracy; Algorithms; Architecture; Belief networks; Breast cancer; Children; Classification; Coding; Convolutional deep belief network; Datasets; Digital imaging; feature encoding; Feature extraction; histological images; Histology; Image classification; Image resolution; Learning; Level (quantity); Machine learning; Methods; Neuroblastoma; Support vector machines; Tumors; neuroblastoma; Convolutional deep belief network; histological images; feature encoding
• ISSN: 2153-3539; 2229-5089; 2153-3539
• DOI: 10.4103/jpi.jpi_73_17

Background: Neuroblastoma is the most common extracranial solid tumor in children younger than 5 years old. Optimal management of neuroblastic tumors depends on many factors including histopathological classification. The gold standard for classification of neuroblastoma histological images is visual microscopic assessment. In this study, we propose and evaluate a deep learning approach to classify high-resolution digital images of neuroblastoma histology into five different classes determined by the Shimada classification. Subjects and Methods: We apply a combination of convolutional deep belief network (CDBN) with feature encoding algorithm that automatically classifies digital images of neuroblastoma histology into five different classes. We design a three-layer CDBN to extract high-level features from neuroblastoma histological images and combine with a feature encoding model to extract features that are highly discriminative in the classification task. The extracted features are classified into five different classes using a support vector machine classifier. Data: We constructed a dataset of 1043 neuroblastoma histological images derived from Aperio scanner from 125 patients representing different classes of neuroblastoma tumors. Results: The weighted average F-measure of 86.01% was obtained from the selected high-level features, outperforming state-of-the-art methods. Conclusion: The proposed computer-aided classification system, which uses the combination of deep architecture and feature encoding to learn high-level features, is highly effective in the classification of neuroblastoma histological images.