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An Efficient Lightweight CNN and Ensemble Machine Learning Classification of Prostate Tissue Using Multilevel Feature Analysis

Authors
 Subrata Bhattacharjee  ;  Cho-Hee Kim  ;  Deekshitha Prakash  ;  Hyeon-Gyun Park  ;  Nam-Hoon Cho  ;  Heung-Kook Choi 
Citation
 APPLIED SCIENCES-BASEL, Vol.10(22) : 8013, 2020-11 
Journal Title
 APPLIED SCIENCES-BASEL 
Issue Date
2020-11
Keywords
prostate carcinoma ; microscopic ; convolutional neural network ; machine learning ; deep learning ; handcrafted
Abstract
Prostate carcinoma is caused when cells and glands in the prostate change their shape and size from normal to abnormal. Typically, the pathologist’s goal is to classify the staining slides and differentiate normal from abnormal tissue. In the present study, we used a computational approach to classify images and features of benign and malignant tissues using artificial intelligence (AI) techniques. Here, we introduce two lightweight convolutional neural network (CNN) architectures and an ensemble machine learning (EML) method for image and feature classification, respectively. Moreover, the classification using pre-trained models and handcrafted features was carried out for comparative analysis. The binary classification was performed to classify between the two grade groups (benign vs. malignant) and quantile-quantile plots were used to show their predicted outcomes. Our proposed models for deep learning (DL) and machine learning (ML) classification achieved promising accuracies of 94.0% and 92.0%, respectively, based on non-handcrafted features extracted from CNN layers. Therefore, these models were able to predict nearly perfectly accurately using few trainable parameters or CNN layers, highlighting the importance of DL and ML techniques and suggesting that the computational analysis of microscopic anatomy will be essential to the future practice of pathology.
Files in This Item:
T202006013.pdf Download
DOI
10.3390/app10228013
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Pathology (병리학교실) > 1. Journal Papers
Yonsei Authors
Cho, Nam Hoon(조남훈) ORCID logo https://orcid.org/0000-0002-0045-6441
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/181532
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