Toward an Expert Level of Lung Cancer Detection and Classification Using a Deep Convolutional Neural Network

Zhang, Chao; Sun, Xing; Kang, Dong-Wook; Li, Ké; Guo, Xiaowei; Jia, Chang; Yu, Zongqiao; Huang, Feiyue; Wu, Yunsheng; Zhu, Liang; Liu, Zai‐yi; Zhang, Xue‐gong; Gao, Xiaowen; Huang, Shaohong; Qin, Jie; Feng, Weineng; Zhou, Tao; Zhang, Yanbin; Fang, Weijun; Zhao, Mingfang; Yang, Xue‐Ning; Zhou, Qing; Wu, Yi‐Long; Zhong, Wen‐Zhao

doi:10.1634/theoncologist.2018-0908

Cited by 109 publications

(47 citation statements)

References 19 publications

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“…These data are prepared from the comparison between segmented output and the tumor availability information given in the original database. Then the sensitivity, specificity and accuracy are obtained as 92%, 93.33%, and 92.72% respectively using (8) to (10).…”

Section: B Performance Evaluationmentioning

confidence: 99%

“…Many different works have been done and reported in literature previously to detect and classify the lung tumor from CT image using various types of algorithms [3][4][5][6][7][8][9][10][11][12][13][14]. Lung tumor is often segmented manually which is user and experience dependent, subjective, and may lead to erroneous diagnosis.…”

Section: Introductionmentioning

confidence: 99%

“…The performance of these algorithms declines without enough training data. Recently deep neural networks are becoming popular to address classification problems [9,10]. But large training datasets and huge computational cost are needed for deep learning.…”

Section: Introductionmentioning

confidence: 99%

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Lung Tumor Segmentation and Staging from CT Images Using Fast and Robust Fuzzy C-Means Clustering

Bhakta¹,

Hossain²

2020

IJIGSP

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Lung tumor is the result of abnormal and uncontrolled cell division and growth in lung region. Earlier detection and staging of lung tumor is of great importance to increase the survival rate of the suffered patients. In this paper, a fast and robust Fuzzy c-means clustering method is used for segmenting the tumor region from lung CT images. Morphological reconstruction process is performed prior to Fuzzy cmeans clustering to achieve robustness against noises. The computational efficiency is improved through median filtering of membership partition. Tumor masks are then reconstructed using surface based and shape based filtering. Different features are extracted from the segmented tumor region including maximum diameter and the tumor stage is determined according to the tumor staging system of American Joint Commission on Cancer. 3D shape of the segmented tumor is reconstructed from series of 2D CT slices for volume measurement. The accuracy of the proposed system is found as 92.72% for 55 randomly selected images from the RIDER Lung CT dataset of Cancer imaging archive. Lower complexity in terms of iterations and connected components as well as better noise robustness are found in comparison with conventional Fuzzy c-means and k-means clustering techniques.

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Section: B Performance Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Lung Tumor Segmentation and Staging from CT Images Using Fast and Robust Fuzzy C-Means Clustering

Bhakta¹,

Hossain²

2020

IJIGSP

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“…By using ANN, circulating miRNAs can be used as non-invasive, sensitive and specific diagnostic markers. Zhang Chaoyang et al [25] proposed an expert level for lung cancer detection and classification using the method of deep convolutional neural networks. A threedimensional convolutional neural network (CNN) was designed to detect pulmonary nodules and classify them as malignant or benign diseases according to pathology and laboratory-confirmed results.…”

Section: Research On Cancer Based On Neural Networkmentioning

confidence: 99%

An intelligent decision-making support system for the detection and staging of prostate cancer in developing countries

Zhang

Chen

et al. 2020

Preprint

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Background Most developing countries face great problems in the medical, and the threat posed by large populations, scarce medical resources and inadequate medical personnel will affect the development and stability of the society. Therefore, for most developing countries, the development of intelligent medical systems can greatly alleviate the social contradictions arising from this problem. Intelligent Medical System is a system to assist doctors to make decisions. Through effective input, the system makes corresponding decisions and provides doctors with corresponding auxiliary diagnosis and relevant treatment plans. Methods The experimental team collected and compiled about 2,156,528 data items based on the hospitalization information of more than 8,000 prostate patients in three hospitals of Xiangya. The ratio of experimental data to test data was about 7: 3. Combining prostate cancer images and physical indicators to assist diagnosis and make decisions, from the extraction of corresponding image feature data to the final diagnosis decision, a new intelligent system for prostate cancer data decision-making based on Perceptron neural network is proposed. In the comprehensive decision-making, the final decision is made by using the rule-reasoning model. Through data collection, medical data analysis and integration, image processing and analysis, and disease detection and decision-making processes, patients are assisted in diagnosis and treatment to solve the problems and social contradictions facing most developing countries. Results Through the study of hospitalization information of more than 8,000 prostate patients in three hospitals, about 2,156,528 data items were collected and compiled for experiment purposes. Experimental data shows that when the patient base increases from 200 to 8,000, the accuracy of the machine-assisted diagnostic system will increase from 61% to 87%, and the doctor's diagnosis rate will be reduced to 81%. Conclusions From the study, it is concluded that when the patient base reaches a certain number, the diagnostic accuracy of the machine-assisted diagnosis system will exceed the doctor's expertise. So the smart medical system can help doctors and Medical experts make suggest more effective treatments.

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“…[22] 2019 87.5 Ciompi, Francesco et al [29] 2017 79.5 * Jakimovski, Goran et al [30] 2019 99.6 Lakshmanaprabu, S.K. et al [31] 2018 94.5 Liao, Fangzhou et al [23] 2019 81.4 Liu, Xinglong et al [33] 2017 90.3 * Masood, Anum et al [21] 2018 96.3 Nishio, Mizuho et al [34] 2018 68 Onishi, Yuya et al [35] 2018 81.7 Polat, Huseyin et al [36] 2019 91.8 Qiang, Yan et al [37] 2017 82.8 Rangaswamy et al [38] 2019 96 Sori, Worku Jifara et al [39] 2018 87.8 Wang, Shengping et al [40] 2018 84 Wang, Yang et al [25] 2019 87.3 Yuan, Jingjing et al [41] 2017 93.9 * Zhang, Chao et al [42] 2019 92 * (c)…”

Section: Study Inclusion Criteriamentioning

confidence: 99%

The Performance of Deep Learning Algorithms on Automatic Pulmonary Nodule Detection and Classification Tested on Different Datasets That Are Not Derived from LIDC-IDRI: A Systematic Review

Vilmun

Carlsen

et al. 2019

Diagnostics

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The aim of this study was to systematically review the performance of deep learning technology in detecting and classifying pulmonary nodules on computed tomography (CT) scans that were not from the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) database. Furthermore, we explored the difference in performance when the deep learning technology was applied to test datasets different from the training datasets. Only peer-reviewed, original research articles utilizing deep learning technology were included in this study, and only results from testing on datasets other than the LIDC-IDRI were included. We searched a total of six databases: EMBASE, PubMed, Cochrane Library, the Institute of Electrical and Electronics Engineers, Inc. (IEEE), Scopus, and Web of Science. This resulted in 1782 studies after duplicates were removed, and a total of 26 studies were included in this systematic review. Three studies explored the performance of pulmonary nodule detection only, 16 studies explored the performance of pulmonary nodule classification only, and 7 studies had reports of both pulmonary nodule detection and classification. Three different deep learning architectures were mentioned amongst the included studies: convolutional neural network (CNN), massive training artificial neural network (MTANN), and deep stacked denoising autoencoder extreme learning machine (SDAE-ELM). The studies reached a classification accuracy between 68–99.6% and a detection accuracy between 80.6–94%. Performance of deep learning technology in studies using different test and training datasets was comparable to studies using same type of test and training datasets. In conclusion, deep learning was able to achieve high levels of accuracy, sensitivity, and/or specificity in detecting and/or classifying nodules when applied to pulmonary CT scans not from the LIDC-IDRI database.

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Toward an Expert Level of Lung Cancer Detection and Classification Using a Deep Convolutional Neural Network

Cited by 109 publications

References 19 publications

Lung Tumor Segmentation and Staging from CT Images Using Fast and Robust Fuzzy C-Means Clustering

Lung Tumor Segmentation and Staging from CT Images Using Fast and Robust Fuzzy C-Means Clustering

An intelligent decision-making support system for the detection and staging of prostate cancer in developing countries

The Performance of Deep Learning Algorithms on Automatic Pulmonary Nodule Detection and Classification Tested on Different Datasets That Are Not Derived from LIDC-IDRI: A Systematic Review

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