Multimodality Attention-Guided 3-D Detection of Nonsmall Cell Lung Cancer in <sup>18</sup>F-FDG PET/CT Images

Chen, Ling; Liu, Kanfeng; Shen, Hui; Ye, Hongwei; Liu, Huafeng; Yu, Lijuan; Li, Jingsong; Zhao, Kui; Zhu, Wentao

doi:10.1109/trpms.2021.3072064

Cited by 13 publications

(5 citation statements)

References 39 publications

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“…[43] and [45] stress automated extraction and invasive-free detection of lung cancer, respectively, yet are confined by specific dataset requirements. [46] enhances diagnostic precision in non-small cell lung cancer detection, albeit constrained by dataset size requirements. [47] proposes early detection of lung nodules using machine learning, [48] improves access to lung cancer screening, and [49] enhances accuracy in nodule detection, all encountering challenges related to sample sizes.Lastly, [50] and [51] introduce novel approaches for cancer prediction and detection, respectively, while [52] focuses on reducing false positives in detection tasks, each grappling with specific limitations such as the absence of integration with optimization techniques or restricted dataset usage.…”

Section: Related Workmentioning

confidence: 99%

DLCT LUNG Detect Net: Leveraging Deep Learning for Lung Tumor Detection in CT scans

Lata L. Ragha

2024

jes

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Lung cancer is a critical global health concern, necessitating precise early diagnosis and intervention for better patient outcomes. Computed Tomography (CT) scans are pivotal in lung cancer detection, and leveraging advanced technology is crucial. This study introduces "DLCTLungDetectNet," a Convolutional Neural Network (CNN) based deep learning framework, with a focus on early lung cancer detection using CT scan images. The core innovation lies in the integration of the robust "FusionNet," a hybrid model amalgamating feature from ResNet50 and InceptionV3. We conduct a comprehensive comparative analysis, showcasing the superior performance of DLCTLungDetectNet over established architectures such as VGG16, VGG19, and Inception v3. Rigorous evaluation based on standard metrics substantiates DLCTLungDetectNet's high accuracy, precision, Area Under Curve (AUC), and F1 score. This research not only highlights the potential of deep learning in enhancing lung cancer diagnosis but also establishes a benchmark, showcasing the efficacy of the FusionNet hybrid model for achieving superior accuracy in automated lung tumor detection.

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Section: Related Workmentioning

confidence: 99%

DLCT LUNG Detect Net: Leveraging Deep Learning for Lung Tumor Detection in CT scans

Lata L. Ragha

2024

jes

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“…Chen et al [32] have introduced a 3D detection framework for nonsmall cell lung cancer (NSCLC) using 18F-FDG PET/CT images, guided by multimodality attention fusion. Their customized dual-path 3D CenterNet and multimodality attention module achieve improved sensitivity for NSCLC detection, outperforming previous methods.…”

Section: Literature Surveymentioning

confidence: 99%

Lung Cancer Detection in CT Images Using Deep Learning Techniques: A Survey Review

Usharani,

Revathi,

Selvapandian

et al. 2024

EAI Endorsed Trans Perv Health Tech

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INTRODUCTION: The Computed Tomography (CT) imaging-based Lung cancer detection is crucial for early diagnosis. This survey paper presents an overview of the techniques and advancements in CT-based lung cancer detection. It covers the fundamentals of CT imaging, including principles, types, and protocols. OBJECTIVES: The paper explores image processing techniques for pre-processing, such as noise reduction, enhancement, and segmentation. METHODS: Additionally, it discusses feature extraction methods, including shape, texture, and intensity-based features, as well as Deep Learning (DL) and Machine Learning (ML) methods for automated classification. RESULTS: Computerised systems and their integration is examined with CT imaging along with performance evaluation metrics. The survey concludes by addressing challenges, limitations, and future directions. The imaging modalities and artificial intelligence techniques are used to improve lung cancer detection. CONCLUSION: This comprehensive survey aims to provide a concise understanding of CT-based lung cancer detection for researchers and healthcare professionals.

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“…They obtained great accuracy in differentiating between the two groups by analyzing breath samples from lung cancer patients as well as healthy controls using an electronic nasal device. Using 18 F-FDG PET/CT images, Chen et al [ 60 ] offers a multimodality attention-guided 3D detection approach for non-small cell lung cancer. The accuracy of lung cancer detection in PET/CT scans was improved by the authors using deep learning algorithms, which can help with the early diagnosis and treatment formulation of lung cancer.…”

Section: Computer-assisted Lung Cancer Detection Using Ct Imagesmentioning

confidence: 99%

A Review of Deep Learning Techniques for Lung Cancer Screening and Diagnosis Based on CT Images

et al. 2023

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One of the most common and deadly diseases in the world is lung cancer. Only early identification of lung cancer can increase a patient’s probability of survival. A frequently used modality for the screening and diagnosis of lung cancer is computed tomography (CT) imaging, which provides a detailed scan of the lung. In line with the advancement of computer-assisted systems, deep learning techniques have been extensively explored to help in interpreting the CT images for lung cancer identification. Hence, the goal of this review is to provide a detailed review of the deep learning techniques that were developed for screening and diagnosing lung cancer. This review covers an overview of deep learning (DL) techniques, the suggested DL techniques for lung cancer applications, and the novelties of the reviewed methods. This review focuses on two main methodologies of deep learning in screening and diagnosing lung cancer, which are classification and segmentation methodologies. The advantages and shortcomings of current deep learning models will also be discussed. The resultant analysis demonstrates that there is a significant potential for deep learning methods to provide precise and effective computer-assisted lung cancer screening and diagnosis using CT scans. At the end of this review, a list of potential future works regarding improving the application of deep learning is provided to spearhead the advancement of computer-assisted lung cancer diagnosis systems.

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Multimodality Attention-Guided 3-D Detection of Nonsmall Cell Lung Cancer in ¹⁸F-FDG PET/CT Images

Cited by 13 publications

References 39 publications

DLCT LUNG Detect Net: Leveraging Deep Learning for Lung Tumor Detection in CT scans

DLCT LUNG Detect Net: Leveraging Deep Learning for Lung Tumor Detection in CT scans

Lung Cancer Detection in CT Images Using Deep Learning Techniques: A Survey Review

A Review of Deep Learning Techniques for Lung Cancer Screening and Diagnosis Based on CT Images

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Multimodality Attention-Guided 3-D Detection of Nonsmall Cell Lung Cancer in 18F-FDG PET/CT Images

Cited by 13 publications

References 39 publications

DLCT LUNG Detect Net: Leveraging Deep Learning for Lung Tumor Detection in CT scans

DLCT LUNG Detect Net: Leveraging Deep Learning for Lung Tumor Detection in CT scans

Lung Cancer Detection in CT Images Using Deep Learning Techniques: A Survey Review

A Review of Deep Learning Techniques for Lung Cancer Screening and Diagnosis Based on CT Images

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Multimodality Attention-Guided 3-D Detection of Nonsmall Cell Lung Cancer in ¹⁸F-FDG PET/CT Images