Computed Tomography Images under Deep Learning Algorithm in the Diagnosis of Perioperative Rehabilitation Nursing for Patients with Lung Cancer

Sha, Yan; Huang, Qin; Yu, Siying; Liu, Zhenxing

doi:10.1155/2022/8685604

Cited by 3 publications

(2 citation statements)

References 28 publications

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“…Yan et al [109] investigated the effectiveness of a deep learning algorithm-based CT image segmentation in the diagnosis of lung cancer for perioperative rehabilitation nursing. They constructed a hybrid feature fusion model (HFFM) by fusing a 2D and 3D CNN.…”

Section: Deep Learning Technics Using Proprietary Datasetsmentioning

confidence: 99%

Deep Machine Learning for Medical Diagnosis, Application to Lung Cancer Detection: A Review

Gayap,

Akhloufi

2024

BioMedInformatics

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Deep learning has emerged as a powerful tool for medical image analysis and diagnosis, demonstrating high performance on tasks such as cancer detection. This literature review synthesizes current research on deep learning techniques applied to lung cancer screening and diagnosis. This review summarizes the state-of-the-art in deep learning for lung cancer detection, highlighting key advances, limitations, and future directions. We prioritized studies utilizing major public datasets, such as LIDC, LUNA16, and JSRT, to provide a comprehensive overview of the field. We focus on deep learning architectures, including 2D and 3D convolutional neural networks (CNNs), dual-path networks, Natural Language Processing (NLP) and vision transformers (ViT). Across studies, deep learning models consistently outperformed traditional machine learning techniques in terms of accuracy, sensitivity, and specificity for lung cancer detection in CT scans. This is attributed to the ability of deep learning models to automatically learn discriminative features from medical images and model complex spatial relationships. However, several challenges remain to be addressed before deep learning models can be widely deployed in clinical practice. These include model dependence on training data, generalization across datasets, integration of clinical metadata, and model interpretability. Overall, deep learning demonstrates great potential for lung cancer detection and precision medicine. However, more research is required to rigorously validate models and address risks. This review provides key insights for both computer scientists and clinicians, summarizing progress and future directions for deep learning in medical image analysis.

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Section: Deep Learning Technics Using Proprietary Datasetsmentioning

confidence: 99%

Deep Machine Learning for Medical Diagnosis, Application to Lung Cancer Detection: A Review

Gayap,

Akhloufi

2024

BioMedInformatics

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“…At present, imaging AI and pathological AI have been successfully applied in the fields of disease screening, prediction and diagnosis. Researchers in several fields have constructed a large-scale CT dataset that includes novel coronavirus pneumonia [6](COVID-19), common pneumonia, and normal control populations, and developed a COVID-19 AI diagnostic system based on CT images to help accurately diagnose COVID-19. However, laboratory tests have significant advantages over imaging and pathology.…”

Section: Intelligent Auxiliary Diagnostic Systems and Clinical Pathologymentioning

confidence: 99%

Intelligent medical detection and diagnosis assisted by deep learning

Tian,

Li,

et al. 2024

ACE

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The integration of artificial intelligence (AI) in healthcare has led to the development of intelligent auxiliary diagnosis systems, enhancing diagnostic capabilities across various medical domains. These AI-assisted systems leverage deep learning algorithms to aid healthcare professionals in disease screening, localization of focal areas, and treatment plan selection. With policies emphasizing innovation in medical AI technology, particularly in China, AI-assisted diagnosis systems have emerged as valuable tools in improving diagnostic accuracy and efficiency. These systems, categorized into image-assisted and text-assisted modes, utilize medical imaging data and clinical diagnosis records to provide diagnostic support. In the context of lung cancer diagnosis and treatment, AI-assisted integrated solutions show promise in early detection and treatment decision support, particularly in the detection of pulmonary nodules. Overall, the integration of AI in healthcare holds significant potential for improving diagnostic accuracy, efficiency, and patient outcomes, contributing to advancements in medical practice.

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