Lung Cancer Detection: A Deep Learning Approach

Bhatia, Siddharth; Sinha, Yash; Goel, Lavika

doi:10.1007/978-981-13-1595-4_55

Cited by 142 publications

(50 citation statements)

References 19 publications

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“…Bhatia et al [159] delineated a pipeline of pre-processing techniques highlighting lung regions and extracting features using U-net and ResNet models. Multiple classifiers are used after then to predict the probability of the CT scan being cancerous, trained on LIDC-IDRI dataset.…”

Section: Lung Cancermentioning

confidence: 99%

Deep learning applications in pulmonary medical imaging: recent updates and insights on COVID-19

Farhat

Sakr

Kilany

2020

Machine Vision and Applications

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Shortly after deep learning algorithms were applied to Image Analysis, and more importantly to medical imaging, their applications increased significantly to become a trend. Likewise, deep learning applications (DL) on pulmonary medical images emerged to achieve remarkable advances leading to promising clinical trials. Yet, coronavirus can be the real trigger to open the route for fast integration of DL in hospitals and medical centers. This paper reviews the development of deep learning applications in medical image analysis targeting pulmonary imaging and giving insights of contributions to COVID-19. It covers more than 160 contributions and surveys in this field, all issued between February 2017 and May 2020 inclusively, highlighting various deep learning tasks such as classification, segmentation, and detection, as well as different pulmonary pathologies like airway diseases, lung cancer, COVID-19 and other infections. It summarizes and discusses the current state-of-the-art approaches in this research domain, highlighting the challenges, especially with COVID-19 pandemic current situation.

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Section: Lung Cancermentioning

confidence: 99%

Deep learning applications in pulmonary medical imaging: recent updates and insights on COVID-19

Farhat

Sakr

Kilany

2020

Machine Vision and Applications

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“…67 In usual, diagnosis, type, and degree of lung cancer are performed by CT scan imaging. 68,69 Unfortunately, the low-dose CT scans may bring falsepositive answers about having lung cancer. 70 Today, the biomarkers of both protein and genetic modifications are known for lung cancer.…”

Section: Using Carbon Nanotubes In Lung Cancer Detectionmentioning

confidence: 99%

<p>The Applications of Carbon Nanotubes in the Diagnosis and Treatment of Lung Cancer: A Critical Review</p>

Sheikhpour¹,

Naghinejad²,

Kasaeian³

et al. 2020

IJN

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The importance of timely diagnosis and the complete treatment of lung cancer for many people with this deadly disease daily increases due to its high mortality. Diagnosis and treatment with helping the nanoparticles are useful, although they have reasonable harms. This article points out that the side effects of using carbon nanotube (CNT) in this disease treatment process such as inflammation, fibrosis, and carcinogenesis are very problematic. Toxicity can reduce to some extent using the techniques such as functionalizing to proper dimensions as a longer length, more width, and greater curvature. The targeted CNT sensors can be connected to various modified vapors. In this regard, with helping this method, screening makes non-invasive diagnosis possible. Researchers have also found that nanoparticles such as CNTs could be used as carriers to direct drug delivery, especially with chemotherapy drugs. Most of these carriers were multi-wall carbon nanotubes (MWCNT) used for cancerous cell targeting. The results of laboratory and animal researches in the field of diagnosis and treatment became very desirable and hopeful. The collection of researches summarized has highlighted the requirement for a detailed assessment which includes CNT dose, duration, method of induction, etc., to achieve the most controlled conditions for animal and human studies. In the discussion section, 4 contradictory issues are discussed which are invited researchers to do more research to get clearer results.

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“…Here we used an MDA-MB-231 cancer cell-based tumor model to train the algorithms. While training deep networks in general may require large training datasets to diversify their applications, the Unet-like architecture in the core of DeepMACT can be easily adopted to other cancer models [33][34][35][36] . In other words, DeepMACT learned to detect the characteristic shape and appearance of micrometastases against the background signal, and thus, is independent of the cancer cell model used.…”

Section: Deepmact Technologymentioning

confidence: 99%

Deep learning reveals cancer metastasis and therapeutic antibody targeting in whole body

Pan

Schoppe

Parra-Damas

et al. 2019

Preprint

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Authors Graphical AbstractSupplementary Movies and deep learning algorithms of DeepMACT are available at http://discotechnologies.org/DeepMACT/ Deep learning reveals cancer metastasis and therapeutic antibody targeting in whole body SUMMARY Reliable detection of disseminated tumor cells and of the biodistribution of tumor-targeting therapeutic antibodies within the entire body has long been needed to better understand and treat cancer metastasis. Here, we developed an integrated pipeline for automated quantification of cancer metastases and therapeutic antibody targeting, named DeepMACT. First, we enhanced the fluorescent signal of tumor cells more than 100-fold by applying the vDISCO method to image single cancer cells in intact transparent mice. Second, we developed deep learning algorithms for automated quantification of metastases with an accuracy matching human expert manual annotation. Deep learning-based quantifications in a model of spontaneous metastasis using human breast cancer cells allowed us to systematically analyze clinically relevant features such as size, shape, spatial distribution, and the degree to which metastases are targeted by a therapeutic monoclonal antibody in whole mice. DeepMACT can thus considerably improve the discovery of effective therapeutic strategies for metastatic cancer.

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Lung Cancer Detection: A Deep Learning Approach

Cited by 142 publications

References 19 publications

Deep learning applications in pulmonary medical imaging: recent updates and insights on COVID-19

Deep learning applications in pulmonary medical imaging: recent updates and insights on COVID-19

<p>The Applications of Carbon Nanotubes in the Diagnosis and Treatment of Lung Cancer: A Critical Review</p>

Deep learning reveals cancer metastasis and therapeutic antibody targeting in whole body

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