The value of AI in the Diagnosis, Treatment, and Prognosis of Malignant Lung Cancer

Wang, Yue; Cai, Haihua; Pu, Yongzhu; Li, Jindan; Yang, Fake; Yang, Chungang; Chen, Long; Hu, Zhanli

doi:10.3389/fradi.2022.810731

Cited by 2 publications

(2 citation statements)

References 79 publications

(83 reference statements)

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“…Therefore, the results have shown that artificial intelligence (that is, deep learning, which is its subset) is a usable tool in the diagnosis of these diseases. The findings are in parallel with the findings of other studies in the literature ( 22 , 25 , 26 , 51 , 57 ), which suggest that deep learning networks are successful in diagnosing diseases such as COVID-19 and Viral Pneumonia. Therefore, the first of the research questions, “Can deep learning networks (ResNet101, AlexNet, GoogLeNet and Xception) be successful in detecting COVID, Viral Pneumonia and healthy X-ray images?” was answered positively.…”

Section: Discussionsupporting

confidence: 86%

“…Therefore, the results have shown that artificial intelligence (that is, deep learning, which is its subset) is a usable tool in the diagnosis of these diseases. The findings are in parallel with the findings of other studies in the literature (22,25,26,51,57) (22,25,58) for the detection of COVID-19, but no comparison has been made for the detection of different diseases using CNNs. In some studies [e.g., (28)], there has been a call to examine lung diseases such as pneumonia, lung cancer and COVID-19 together for future studies.…”

Section: Discussionsupporting

confidence: 81%

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Artificial intelligence in the healthcare sector: comparison of deep learning networks using chest X-ray images

Yenikaya,

Kerse,

Oktaysoy

2024

Front. Public Health

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PurposeArtificial intelligence has led to significant developments in the healthcare sector, as in other sectors and fields. In light of its significance, the present study delves into exploring deep learning, a branch of artificial intelligence.MethodsIn the study, deep learning networks ResNet101, AlexNet, GoogLeNet, and Xception were considered, and it was aimed to determine the success of these networks in disease diagnosis. For this purpose, a dataset of 1,680 chest X-ray images was utilized, consisting of cases of COVID-19, viral pneumonia, and individuals without these diseases. These images were obtained by employing a rotation method to generate replicated data, wherein a split of 70 and 30% was adopted for training and validation, respectively.ResultsThe analysis findings revealed that the deep learning networks were successful in classifying COVID-19, Viral Pneumonia, and Normal (disease-free) images. Moreover, an examination of the success levels revealed that the ResNet101 deep learning network was more successful than the others with a 96.32% success rate.ConclusionIn the study, it was seen that deep learning can be used in disease diagnosis and can help experts in the relevant field, ultimately contributing to healthcare organizations and the practices of country managers.

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Section: Discussionsupporting

confidence: 86%

“…Therefore, the results have shown that artificial intelligence (that is, deep learning, which is its subset) is a usable tool in the diagnosis of these diseases. The findings are in parallel with the findings of other studies in the literature (22,25,26,51,57) (22,25,58) for the detection of COVID-19, but no comparison has been made for the detection of different diseases using CNNs. In some studies [e.g., (28)], there has been a call to examine lung diseases such as pneumonia, lung cancer and COVID-19 together for future studies.…”

Section: Discussionsupporting

confidence: 81%