Comparison of Fine-Tuned Deep Convolutional Neural Networks for the Automated Classification of Lung Cancer Cytology Images with Integration of Additional Classifiers

Tetsuya Tsukamoto, Atsushi Teramoto, Ayumi Yamada, Yuka Kiriyama, Eiko Sakurai, Ayano Michiba, Kazuyoshi Imaizumi, Hiroshi Fujita

Research output: Contribution to journalArticlepeer-review

Abstract

Objective: It is essential to accurately diagnose and classify histological subtypes into adenocarcinoma (ADC), squamous cell carcinoma (SCC), and small cell lung carcinoma (SCLC) for the appropriate treatment of lung cancer patients. However, improving the accuracy and stability of diagnosis is challenging, especially for non-small cell carcinomas. The purpose of this study was to compare multiple deep convolutional neural network (DCNN) technique with subsequent additional classifiers in terms of accuracy and characteristics in each histology. Methods: Lung cancer cytological images were classified into ADC, SCC, and SCLC with four fine-tuned DCNN models consisting of AlexNet, GoogLeNet (Inception V3), VGG16 and ResNet50 pretrained by natural images in ImageNet database. For more precise classification, the figures of 3 histological probabilities were further applied to subsequent machine learning classifiers using Naïve Bayes (NB), Support vector machine (SVM), Random forest (RF), and Neural network (NN). Results: The classification accuracies of the AlexNet, GoogLeNet, VGG16 and ResNet50 were 74.0%, 66.8%, 76.8% and 74.0%, respectively. Well differentiated typical morphologies were tended to be correctly judged by all four architectures. However, poorly differentiated non-small cell carcinomas lacking typical structures were inclined to be misrecognized in some DCNNs. Regarding the histological types, ADC were best judged by AlexNet and SCC by VGG16. Subsequent machine learning classifiers of NB, SVV, RF, and NN improved overall accuracies to 75.1%, 77.5%, 78.2%, and 78.9%, respectively. Conclusion: Fine-tuning DCNNs in combination with additional classifiers improved classification of cytological diagnosis of lung cancer, although classification bias could be indicated among DCNN architectures.

Original languageEnglish
Pages (from-to)1315-1324
Number of pages10
JournalAsian Pacific Journal of Cancer Prevention
Volume23
Issue number4
DOIs
Publication statusPublished - 2022
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Epidemiology
  • Oncology
  • Public Health, Environmental and Occupational Health
  • Cancer Research

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