Automated Detection of Lung Cancer Using CT Scan Images

Hoque, Ariful; Farabi, A.K.M. Ashek; Ahmed, Fahad; Islam, Zahidul

doi:10.1109/tensymp50017.2020.9230861

Cited by 16 publications

(19 citation statements)

References 12 publications

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“…The studies were conducted in various countries, including Turkey, the United States, Poland, Pakistan, Italy, Bangladesh, and India, and were published between 2014 and 2022. The study designs varied among the selected articles, with five case-control studies [28][29][30][31]33], three retrospective cohort studies [26,27,34], and one prospective cohort study [32]. The quality of the included studies ranged from excellent to fair, with one study deemed excellent [26], three rated as good [29,30,32], and five considered fair [27,28,31,33,34].…”

Section: Overviewmentioning

confidence: 99%

“…The remaining five studies were case-control studies conducted in various countries, including Poland [28,31], Pakistan [29], the United States [30], and Bangladesh [33]. The quality of these studies was mixed, with two rated as good [29,30] and three considered fair [28,31,33]. The studies employed various machine learning architectures, including artificial neural network (ANN) [26], entropy degradation method (EDM) [27], probabilistic neural network (PNN) [28,31], support vector machine (SVM) [29,33,34], partially observable Markov decision process (POMDP) [30], and random forest neural network (RFNN) [32].…”

Section: Overviewmentioning

confidence: 99%

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Diagnostic Accuracy of Machine Learning AI Architectures in Detection and Classification of Lung Cancer: A Systematic Review

Pacurari,

Bhattarai,

Muhammad

et al. 2023

Diagnostics

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The application of artificial intelligence (AI) in diagnostic imaging has gained significant interest in recent years, particularly in lung cancer detection. This systematic review aims to assess the accuracy of machine learning (ML) AI algorithms in lung cancer detection, identify the ML architectures currently in use, and evaluate the clinical relevance of these diagnostic imaging methods. A systematic search of PubMed, Web of Science, Cochrane, and Scopus databases was conducted in February 2023, encompassing the literature published up until December 2022. The review included nine studies, comprising five case-control studies, three retrospective cohort studies, and one prospective cohort study. Various ML architectures were analyzed, including artificial neural network (ANN), entropy degradation method (EDM), probabilistic neural network (PNN), support vector machine (SVM), partially observable Markov decision process (POMDP), and random forest neural network (RFNN). The ML architectures demonstrated promising results in detecting and classifying lung cancer across different lesion types. The sensitivity of the ML algorithms ranged from 0.81 to 0.99, while the specificity varied from 0.46 to 1.00. The accuracy of the ML algorithms ranged from 77.8% to 100%. The AI architectures were successful in differentiating between malignant and benign lesions and detecting small-cell lung cancer (SCLC) and non-small-cell lung cancer (NSCLC). This systematic review highlights the potential of ML AI architectures in the detection and classification of lung cancer, with varying levels of diagnostic accuracy. Further studies are needed to optimize and validate these AI algorithms, as well as to determine their clinical relevance and applicability in routine practice.

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Section: Overviewmentioning

confidence: 99%

Section: Overviewmentioning

confidence: 99%

Diagnostic Accuracy of Machine Learning AI Architectures in Detection and Classification of Lung Cancer: A Systematic Review

Pacurari,

Bhattarai,

Muhammad

et al. 2023

Diagnostics

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“…Medical image analysis involves the examination and interpretation of medical images, such as MRI scans, CT scans, and X-rays [92] [224]- [241]. In this domain, 3D convolutions and dilated convolutions are frequently used.…”

Section: E Medical Image Analysismentioning

confidence: 99%

A Novel Levy Walk-Based Framework for Scheduling Power-Intensive Mobile Edge Computing Tasks

Younesi,

Fazli,

Ejlali

2023

Preprint

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“…The tissue impedances are calculated from reflection coefficients using equation (3). The Fig 1 In which is reflection coefficient, is air impedance which is 376Ω and is the tissue impedance.…”

Section: Calculating Tissue Impedancementioning

confidence: 99%

“…There are two types of calcifications, one as macro and other as micro-calcification. Macro-calcifications [3] considered to be huge calcium deposition in the lung tissue, developed due to many factors. But, smaller calcium depositionstermed micro-calcifications, in the lung tissue cause the highly susceptible to develop cancer.…”

Section: Introductionmentioning

confidence: 99%

An Early Prediction of Lung Cancer using CT Scan Images

2021

JCNS

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Lung cancer is a common occurrence type in a population and one amonglethal cancers. Recently, out of several research presented by diverse health agencies; it is obvious that the fatality ratio is rising due todelayeddiagnosis of lung cancer. Hence, an artificial intelligence-based diagnosis is required to find out the onset of lung nodule micro-calcification, which may support the doctors and radiologists to accurately predict it through image processing methods. In this paper, a novel technique is proposed to identify the nodule micro-calcification pattern by using its physical features. The physical features that considered are the reflection coefficients and mass densities of the binned CT image of lung. The physical features measurements reiteratesonce again the existence of malignant nodule. Then, by applying the methods of thresholding and in interpolation of physical features, a three-dimensional (3D) projected image of the region of interest (ROI) is achieved in respect of physical dimensions. Thus, the nodule size is calculated from 3D projection. This concept is used to verify how best in classification with 100 malignant images (the nodule presence) and 10 normal images (the nodule absence). Apart size measurement, the proposed method supports SVM classifier to act for excellentclassification from normal and malignantinput imagesby just using two physical features. The classifier exhibited an accuracy of 98%.

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Automated Detection of Lung Cancer Using CT Scan Images

Cited by 16 publications

References 12 publications

Diagnostic Accuracy of Machine Learning AI Architectures in Detection and Classification of Lung Cancer: A Systematic Review

Diagnostic Accuracy of Machine Learning AI Architectures in Detection and Classification of Lung Cancer: A Systematic Review

A Novel Levy Walk-Based Framework for Scheduling Power-Intensive Mobile Edge Computing Tasks

An Early Prediction of Lung Cancer using CT Scan Images

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