Automated classification of benign and malignant cells from lung cytological images using deep convolutional neural network

Teramoto, Akira; Yamada, Akihiro; Kiriyama, Yuka; Tsukamoto, Tetsuya; Yan, Ke; Zhang, Ling; Imaizumi, Kazuyoshi; Saito, Kuniaki; Fujita, Hiroshi

doi:10.1016/j.imu.2019.100205

Cited by 78 publications

(35 citation statements)

References 18 publications

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“…Thus, 10,000 benign and 10,000 malignant cell images, including the original images, were generated. This augmentation method was described in detail in one of our previous publication [11].…”

Section: Image Preparationmentioning

confidence: 99%

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Deep learning approach to classification of lung cytological images: Two-step training using actual and synthesized images by progressive growing of generative adversarial networks

et al. 2020

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Cytology is the first pathological examination performed in the diagnosis of lung cancer. In our previous study, we introduced a deep convolutional neural network (DCNN) to automatically classify cytological images as images with benign or malignant features and achieved an accuracy of 81.0%. To further improve the DCNN's performance, it is necessary to train the network using more images. However, it is difficult to acquire cell images which contain a various cytological features with the use of many manual operations with a microscope. Therefore, in this study, we aim to improve the classification accuracy of a DCNN with the use of actual and synthesized cytological images with a generative adversarial network (GAN). Based on the proposed method, patch images were obtained from a microscopy image. Accordingly, these generated many additional similar images using a GAN. In this study, we introduce progressive growing of GANs (PGGAN), which enables the generation of high-resolution images. The use of these images allowed us to pretrain a DCNN. The DCNN was then fine-tuned using actual patch images. To confirm the effectiveness of the proposed method, we first evaluated the quality of the images which were generated by PGGAN and by a conventional deep convolutional GAN. We then evaluated the classification performance of benign and malignant cells, and confirmed that the generated images had characteristics similar to those of the actual images. Accordingly, we determined that the overall classification accuracy of lung cells was 85.3% which was improved by approximately 4.3% compared to a previously conducted study without pretraining using GAN-generated images. Based on these results, we confirmed that our proposed method will be effective for the classification of cytological images in cases at which only limited data are acquired.

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Section: Image Preparationmentioning

confidence: 99%

“…We have previously developed a method to classify benign and malignant lung cells using a deep convolutional neural network (DCNN) [11], and have also developed a DCNN-based lung cancer type classification system [12]. The overall accuracies of benign/malignant and lung cancer type classifications were 79% and 71%, respectively.…”

Section: Introductionmentioning

confidence: 99%

Deep learning approach to classification of lung cytological images: Two-step training using actual and synthesized images by progressive growing of generative adversarial networks

et al. 2020

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“…Here, Kanavati et al 26 trained a CNN (EfficientNet-B3 architecture 27 ) to predict carcinoma using 3704 histopathology images (obtained from Kyushu Medical Center and International University of Health and Welfare, Mita Hospital) and achieved promising results for discrimination between cancer and normal cells. Although there are multiple studies on automatic lung cancer detection, the focus of most researches is the classification of normal cells versus cancerous ones 26 , 28 . However, cell-lines classification of lung cancer has more clinical values than binary classification (normal versus cancer) as it provides more detailed information to help clinicians for correct therapeutic schedules.…”

Section: Introductionmentioning

confidence: 99%

A combined microfluidic deep learning approach for lung cancer cell high throughput screening toward automatic cancer screening applications

Hashemzadeh

Shojaeilangari

Allahverdi

et al. 2021

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Lung cancer is a leading cause of cancer death in both men and women worldwide. The high mortality rate in lung cancer is in part due to late-stage diagnostics as well as spread of cancer-cells to organs and tissues by metastasis. Automated lung cancer detection and its sub-types classification from cell’s images play a crucial role toward an early-stage cancer prognosis and more individualized therapy. The rapid development of machine learning techniques, especially deep learning algorithms, has attracted much interest in its application to medical image problems. In this study, to develop a reliable Computer-Aided Diagnosis (CAD) system for accurately distinguishing between cancer and healthy cells, we grew popular Non-Small Lung Cancer lines in a microfluidic chip followed by staining with Phalloidin and images were obtained by using an IX-81 inverted Olympus fluorescence microscope. We designed and tested a deep learning image analysis workflow for classification of lung cancer cell-line images into six classes, including five different cancer cell-lines (P-C9, SK-LU-1, H-1975, A-427, and A-549) and normal cell-line (16-HBE). Our results demonstrate that ResNet18, a residual learning convolutional neural network, is an efficient and promising method for lung cancer cell-lines categorization with a classification accuracy of 98.37% and F1-score of 97.29%. Our proposed workflow is also able to successfully distinguish normal versus cancerous cell-lines with a remarkable average accuracy of 99.77% and F1-score of 99.87%. The proposed CAD system completely eliminates the need for extensive user intervention, enabling the processing of large amounts of image data with robust and highly accurate results.

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“…Additionally, not everyone can install a high-performance machine that can withstand a substantial computational load [15,16]. Therefore, fine-tuning a model pretrained by natural images contributes toward reducing the amount of medical image data required as well as the computational cost [17,18]. However, it was difficult to adapt the model to 3D images, such as to brain MR images.…”

Section: Introductionmentioning

confidence: 99%

Development of a Fully Automated Glioma-Grading Pipeline Using Post-Contrast T1-Weighted Images Combined with Cloud-Based 3D Convolutional Neural Network

2021

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Glioma is the most common type of brain tumor, and its grade influences its treatment policy and prognosis. Therefore, artificial-intelligence-based tumor grading methods have been studied. However, in most studies, two-dimensional (2D) analysis and manual tumor-region extraction were performed. Additionally, deep learning research that uses medical images experiences difficulties in collecting image data and preparing hardware, thus hindering its widespread use. Therefore, we developed a 3D convolutional neural network (3D CNN) pipeline for realizing a fully automated glioma-grading system by using the pretrained Clara segmentation model provided by NVIDIA and our original classification model. In this method, the brain tumor region was extracted using the Clara segmentation model, and the volume of interest (VOI) created using this extracted region was assigned to a grading 3D CNN and classified as either grade II, III, or IV. Through evaluation using 46 regions, the grading accuracy of all tumors was 91.3%, which was comparable to that of the method using multi-sequence. The proposed pipeline scheme may enable the creation of a fully automated glioma-grading pipeline in a single sequence by combining the pretrained 3D CNN and our original 3D CNN.

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Automated classification of benign and malignant cells from lung cytological images using deep convolutional neural network

Cited by 78 publications

References 18 publications

Deep learning approach to classification of lung cytological images: Two-step training using actual and synthesized images by progressive growing of generative adversarial networks

Deep learning approach to classification of lung cytological images: Two-step training using actual and synthesized images by progressive growing of generative adversarial networks

A combined microfluidic deep learning approach for lung cancer cell high throughput screening toward automatic cancer screening applications

Development of a Fully Automated Glioma-Grading Pipeline Using Post-Contrast T1-Weighted Images Combined with Cloud-Based 3D Convolutional Neural Network

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