Evidence map›Paper›PMID 37955065›Full record

ArticleTechnology and health care : official journal of the European Society for Engineering and Medicine2024

Lung cancer detection based on computed tomography image using convolutional neural networks.

Neslihan Ozcelik, Mehmet Kıvrak, Abdurrahman Kotan, İnci Selimoğlu

Expression of concernAbstract read
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In one paragraph

Article in Technology and health care : official journal of the European Society for Engineering and Medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It carries an expression of concern. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

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No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

Neslihan OzcelikRecep Tayyip Erdogan University, Chest Disease, Rize, Turkey.
Mehmet KıvrakRecep Tayyip Erdogan University, Biostatistics and Medical Informatics, Rize, Turkey.
Abdurrahman KotanErzurum Regional Training and Research Hospital, Chest Disease, Erzurum, Turkey.
İnci SelimoğluRecep Tayyip Erdogan University, Chest Disease, Rize, Turkey.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundLung cancer is the most common type of cancer, accounting for 12.8% of cancer cases worldwide. As initially non-specific symptoms occur, it is difficult to diagnose in the early stages.

objectiveImage processing techniques developed using machine learning methods have played a crucial role in the development of decision support systems. This study aimed to classify benign and malignant lung lesions with a deep learning approach and convolutional neural networks (CNNs).

methodsThe image dataset includes 4459 Computed tomography (CT) scans (benign, 2242; malignant, 2217). The research type was retrospective; the case-control analysis. A method based on GoogLeNet architecture, which is one of the deep learning approaches, was used to make maximum inference on images and minimize manual control.

resultsThe dataset used to develop the CNNs model is included in the training (3567) and testing (892) datasets. The model's highest accuracy rate in the training phase was estimated as 0.98. According to accuracy, sensitivity, specificity, positive predictive value, and negative predictive values of testing data, the highest classification performance ratio was positive predictive value with 0.984.

conclusionThe deep learning methods are beneficial in the diagnosis and classification of lung cancer through computed tomography images.

Indexed as

Deep LearningLung NeoplasmsNeural Networks, ComputerTomography, X-Ray ComputedAgedCase-Control StudiesFemaleHumansMaleMiddle AgedRetrospective StudiesSensitivity and Specificityconvolutional neural networkdeep learningGoogLeNetLung cancer

Identifiers

PMID37955065

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.