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Machine learning methods for automated classification of tumors with papillary thyroid carcinoma-like nuclei: A quantitative analysis
- Source :
- PLoS ONE, Vol 16, Iss 9, p e0257635 (2021), PLOS ONE, 16 (9), e0257635, PLoS ONE
- Publication Year :
- 2021
- Publisher :
- Public Library of Science (PLoS), 2021.
-
Abstract
- When approaching thyroid gland tumor classification, the differentiation between samples with and without “papillary thyroid carcinoma-like” nuclei is a daunting task with high inter-observer variability among pathologists. Thus, there is increasing interest in the use of machine learning approaches to provide pathologists real-time decision support. In this paper, we optimize and quantitatively compare two automated machine learning methods for thyroid gland tumor classification on two datasets to assist pathologists in decision-making regarding these methods and their parameters. The first method is a feature-based classification originating from common image processing and consists of cell nucleus segmentation, feature extraction, and subsequent thyroid gland tumor classification utilizing different classifiers. The second method is a deep learning-based classification which directly classifies the input images with a convolutional neural network without the need for cell nucleus segmentation. On the Tharun and Thompson dataset, the feature-based classification achieves an accuracy of 89.7% (Cohen’s Kappa 0.79), compared to the deep learning-based classification of 89.1% (Cohen’s Kappa 0.78). On the Nikiforov dataset, the feature-based classification achieves an accuracy of 83.5% (Cohen’s Kappa 0.46) compared to the deep learning-based classification 77.4% (Cohen’s Kappa 0.35). Thus, both automated thyroid tumor classification methods can reach the classification level of an expert pathologist. To our knowledge, this is the first study comparing feature-based and deep learning-based classification regarding their ability to classify samples with and without papillary thyroid carcinoma-like nuclei on two large-scale datasets.
- Subjects :
- Computer and Information Sciences
Neural Networks
Science
Gene Expression
Endocrine System
Lung and Intrathoracic Tumors
Machine Learning
Automation
Thymic Tumors
Artificial Intelligence
Medicine and Health Sciences
Genetics
Image Processing, Computer-Assisted
Humans
Medical Personnel
Thyroid Neoplasms
Endocrine Tumors
Preprocessing
Thyroid
Chromosome Biology
Carcinoma
DATA processing & computer science
Thyroid Carcinoma
Biology and Life Sciences
Cancers and Neoplasms
Software Engineering
Cell Biology
Papillary Thyroid Carcinoma
Chromatin
Pathologists
Professions
Oncology
ROC Curve
Thyroid Cancer, Papillary
Area Under Curve
People and Places
Engineering and Technology
Medicine
Population Groupings
Epigenetics
Anatomy
ddc:004
Research Article
Neuroscience
Subjects
Details
- Language :
- English
- ISSN :
- 19326203
- Volume :
- 16
- Issue :
- 9
- Database :
- OpenAIRE
- Journal :
- PLoS ONE
- Accession number :
- edsair.doi.dedup.....ab3c52ac1e0d87609516f2ce9d3e5dd4