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A Comparative Analysis of Deep Learning Models for Automated Cross-Preparation Diagnosis of Multi-Cell Liquid Pap Smear Images
Routine Pap smears can facilitate early detection of cervical cancer and improve patient outcomes. The objective of this work is to develop an automated, clinically viable deep neural network for the multi-class Bethesda System diagnosis of multi-cell images in Liquid Pap smear samples. 8 deep learn...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9406372/ https://www.ncbi.nlm.nih.gov/pubmed/36010189 http://dx.doi.org/10.3390/diagnostics12081838 |
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author | Karasu Benyes, Yasmin Welch, E. Celeste Singhal, Abhinav Ou, Joyce Tripathi, Anubhav |
author_facet | Karasu Benyes, Yasmin Welch, E. Celeste Singhal, Abhinav Ou, Joyce Tripathi, Anubhav |
author_sort | Karasu Benyes, Yasmin |
collection | PubMed |
description | Routine Pap smears can facilitate early detection of cervical cancer and improve patient outcomes. The objective of this work is to develop an automated, clinically viable deep neural network for the multi-class Bethesda System diagnosis of multi-cell images in Liquid Pap smear samples. 8 deep learning models were trained on a publicly available multi-class SurePath preparation dataset. This included the 5 best-performing transfer learning models, an ensemble, a novel convolutional neural network (CNN), and a CNN + autoencoder (AE). Additionally, each model was tested on a novel ThinPrep Pap dataset to determine model generalizability across different liquid Pap preparation methods with and without Deep CORAL domain adaptation. All models achieved accuracies >90% when classifying SurePath images. The AE CNN model, 99.80% smaller than the average transfer model, maintained an accuracy of 96.54%. During consecutive training attempts, individual transfer models had high variability in performance, whereas the CNN, AE CNN, and ensemble did not. ThinPrep Pap classification accuracies were notably lower but increased with domain adaptation, with ResNet101 achieving the highest accuracy at 92.65%. This indicates a potential area for future improvement: development of a globally relevant model that can function across different slide preparation methods. |
format | Online Article Text |
id | pubmed-9406372 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-94063722022-08-26 A Comparative Analysis of Deep Learning Models for Automated Cross-Preparation Diagnosis of Multi-Cell Liquid Pap Smear Images Karasu Benyes, Yasmin Welch, E. Celeste Singhal, Abhinav Ou, Joyce Tripathi, Anubhav Diagnostics (Basel) Article Routine Pap smears can facilitate early detection of cervical cancer and improve patient outcomes. The objective of this work is to develop an automated, clinically viable deep neural network for the multi-class Bethesda System diagnosis of multi-cell images in Liquid Pap smear samples. 8 deep learning models were trained on a publicly available multi-class SurePath preparation dataset. This included the 5 best-performing transfer learning models, an ensemble, a novel convolutional neural network (CNN), and a CNN + autoencoder (AE). Additionally, each model was tested on a novel ThinPrep Pap dataset to determine model generalizability across different liquid Pap preparation methods with and without Deep CORAL domain adaptation. All models achieved accuracies >90% when classifying SurePath images. The AE CNN model, 99.80% smaller than the average transfer model, maintained an accuracy of 96.54%. During consecutive training attempts, individual transfer models had high variability in performance, whereas the CNN, AE CNN, and ensemble did not. ThinPrep Pap classification accuracies were notably lower but increased with domain adaptation, with ResNet101 achieving the highest accuracy at 92.65%. This indicates a potential area for future improvement: development of a globally relevant model that can function across different slide preparation methods. MDPI 2022-07-29 /pmc/articles/PMC9406372/ /pubmed/36010189 http://dx.doi.org/10.3390/diagnostics12081838 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Karasu Benyes, Yasmin Welch, E. Celeste Singhal, Abhinav Ou, Joyce Tripathi, Anubhav A Comparative Analysis of Deep Learning Models for Automated Cross-Preparation Diagnosis of Multi-Cell Liquid Pap Smear Images |
title | A Comparative Analysis of Deep Learning Models for Automated Cross-Preparation Diagnosis of Multi-Cell Liquid Pap Smear Images |
title_full | A Comparative Analysis of Deep Learning Models for Automated Cross-Preparation Diagnosis of Multi-Cell Liquid Pap Smear Images |
title_fullStr | A Comparative Analysis of Deep Learning Models for Automated Cross-Preparation Diagnosis of Multi-Cell Liquid Pap Smear Images |
title_full_unstemmed | A Comparative Analysis of Deep Learning Models for Automated Cross-Preparation Diagnosis of Multi-Cell Liquid Pap Smear Images |
title_short | A Comparative Analysis of Deep Learning Models for Automated Cross-Preparation Diagnosis of Multi-Cell Liquid Pap Smear Images |
title_sort | comparative analysis of deep learning models for automated cross-preparation diagnosis of multi-cell liquid pap smear images |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9406372/ https://www.ncbi.nlm.nih.gov/pubmed/36010189 http://dx.doi.org/10.3390/diagnostics12081838 |
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