Optimal deep learning model for classification of lung cancer on CT images

Lakshmanaprabu, S.K.; Mohanty, Sachi Nandan; Shankar, K.; Arunkumar, N.; Ramı́rez, Gustavo

doi:10.1016/j.future.2018.10.009

Cited by 372 publications

(71 citation statements)

References 12 publications

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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%

Section: Classification Only (16 Studies)mentioning

confidence: 99%

“…Four studies [31][32][33]41] tested their CNN-based algorithm on the Early Lung Cancer Action Program (ELCAP) public lung database [46]. Besides Liu et al [32], who did not provide reports on accuracy, the other studies [31,33,41] reached classification accuracies between 90.3-94.5%. Both Liu et al [33] and Yuan et al [41] classified nodules into multiple categories and calculated the proportion of a specific nodule type, e.g., the proportion of classified well-circumscribed nodules actually well circumscribed, which was 95.0% for Liu et al and 96.1% for Yuan et al Lakshmanaprabu et al [31] tested whether different CT images were categorized correctly as to whether an image was normal or contained malign or benign nodules; results are displayed in Table 2.…”

Section: Classification Only (16 Studies)mentioning

confidence: 99%

“…For the above-mentioned studies, which only investigated classification performance, four studies [29,33,41,42] trained and tested on different types of datasets and achieved accuracies between 79.5-93.6%. The rest of the studies [27,28,[30][31][32][34][35][36][37][38][39][40] trained and tested on the same types of datasets and achieved accuracies between 68-99.6% (Tables 2 and 3).…”

Section: Classification Only (16 Studies)mentioning

confidence: 99%

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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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Section: Study Inclusion Criteriamentioning

confidence: 99%

Section: Classification Only (16 Studies)mentioning

confidence: 99%

Section: Classification Only (16 Studies)mentioning

confidence: 99%

Section: Classification Only (16 Studies)mentioning

confidence: 99%

See 2 more Smart Citations

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 introduction of information technology and the e-healthcare system in the area of medical diagnosis has assisted clinical professionals in offering considerably better health care for patients. Different classification techniques, especially convolutional neural networks, have been proposed in recent years [1][2][3][4][5][6] however, these proposed techniques have failed to acquire high accuracy. Therefore, there is a need to develop new techniques for the detection of brain tumor.…”

Section: Introductionmentioning

confidence: 99%

Incremental Dilations Using CNN for Brain Tumor Classification

2020

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Brain tumor classification is a challenging task in the field of medical image processing.Technology has now enabled medical doctors to have additional aid for diagnosis. We aim toclassify brain tumors using MRI images, which were collected from anonymous patients andartificial brain simulators . In this article, we carry out a comparative study between SimpleArtificial Neural Networks with dropout, Basic Convolutional Neural Networks (CNN), and DilatedConvolutional Neural Networks. The experimental results shed light on the high classificationperformance (accuracy 97%) of Dilated CNN. On the other hand, Dilated CNN suffers from thegridding phenomenon. An incremental, even number dilation rate takes advantage of the reducedcomputational overhead and also overcomes the adverse effects of gridding. Comparative analysisbetween different combinations of dilation rates for the different convolution layers, help validatethe results. The computational overhead in terms of efficiency for training the model to reach anacceptable threshold accuracy of 90% is another parameter to compare the model performance.

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