Classification of lung cancer computed tomography images using a 3-dimensional deep convolutional neural network with multi-layer filter

Siddiqui, Ebtasam Ahmad; Chaurasia, Vijayshri; Shandilya, Madhu

doi:10.1007/s00432-023-04992-9

J Cancer Res Clin Oncol

2023

DOI: 10.1007/s00432-023-04992-9

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Classification of lung cancer computed tomography images using a 3-dimensional deep convolutional neural network with multi-layer filter

Ebtasam Ahmad Siddiqui

Vijayshri Chaurasia

Madhu Shandilya

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Cited by 9 publications

References 67 publications

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Artificial intelligence in lung cancer screening: Detection, classification, prediction, and prognosis

Quanyang,

Yao,

Sicong

et al. 2024

Cancer Medicine

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BackgroundThe exceptional capabilities of artificial intelligence (AI) in extracting image information and processing complex models have led to its recognition across various medical fields. With the continuous evolution of AI technologies based on deep learning, particularly the advent of convolutional neural networks (CNNs), AI presents an expanded horizon of applications in lung cancer screening, including lung segmentation, nodule detection, false‐positive reduction, nodule classification, and prognosis.MethodologyThis review initially analyzes the current status of AI technologies. It then explores the applications of AI in lung cancer screening, including lung segmentation, nodule detection, and classification, and assesses the potential of AI in enhancing the sensitivity of nodule detection and reducing false‐positive rates. Finally, it addresses the challenges and future directions of AI in lung cancer screening.ResultsAI holds substantial prospects in lung cancer screening. It demonstrates significant potential in improving nodule detection sensitivity, reducing false‐positive rates, and classifying nodules, while also showing value in predicting nodule growth and pathological/genetic typing.ConclusionsAI offers a promising supportive approach to lung cancer screening, presenting considerable potential in enhancing nodule detection sensitivity, reducing false‐positive rates, and classifying nodules. However, the universality and interpretability of AI results need further enhancement. Future research should focus on the large‐scale validation of new deep learning‐based algorithms and multi‐center studies to improve the efficacy of AI in lung cancer screening.

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Artificial intelligence in lung cancer screening: Detection, classification, prediction, and prognosis

Quanyang,

Yao,

Sicong

et al. 2024

Cancer Medicine

View full text Add to dashboard Cite

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A novel dense-net deep neural network with enhanced feature selection method for classification of different stages of tuberculosis using chest X-ray images

Patankar,

Chaurasia,

Shandilya

2024

Multimed Tools Appl

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A scoping review of deep learning approaches for lung cancer detection using chest radiographs and computed tomography scans

Nguyen

2025

Biomedical Engineering Advances

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Classification of lung cancer computed tomography images using a 3-dimensional deep convolutional neural network with multi-layer filter

Cited by 9 publications

References 67 publications

Artificial intelligence in lung cancer screening: Detection, classification, prediction, and prognosis

Artificial intelligence in lung cancer screening: Detection, classification, prediction, and prognosis

A novel dense-net deep neural network with enhanced feature selection method for classification of different stages of tuberculosis using chest X-ray images

A scoping review of deep learning approaches for lung cancer detection using chest radiographs and computed tomography scans

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