Investigation of Deep Features in Lung Nodule Classification

Bruntha, P. Malin; Ahmed, L. Jubair; Khanna, D.; Pandian, S. Immanuel Alex; Abraham, Siril Sam

doi:10.1109/icdcs54290.2022.9780716

Cited by 12 publications

(3 citation statements)

References 12 publications

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“…He has used ResNet50. VGG16, and VGG19 [3] algorithms for feature extraction in a system. Shanchen Pang [4] has used VGG16-T and multiple VGG16-T worked as weak classifiers.…”

Section: Literature Reviewmentioning

confidence: 99%

RETRACTED: Lung Nodule Classification Using Deep Learning Algorithms and Feature Fusion

Bhagwat,

Thirupurasundari

2024

Preprint

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Today, lung cancer is the major cause of cancer death. Doctors use the CT scan and manual method for lung nodule detection and diagnosis. However such methods fail to correctly classify benign and malignant nodules. For early, automatic, and accurate detection and classification of lung nodule malignancy, a good lung nodule classification method is required. To solve this problem in the proposed system implementation, feature fusion and DL algorithms such as VGG16 and ResNet50 are used. In this system, two separate architectures, such as Architecture 1 and Architecture 2, are used for finding different features separately regarding the existing IQ-OTHNCCD lung cancer image dataset and real clinical lung CT images. Architecture 1 contains the VGG16 algorithm, and Architecture 2 contains the ResNet50 algorithm, which trains the system. The features obtained from each architecture are fused using the concatenation fusion algorithm. The concatenation fusion algorithm is used to improve classification accuracy and performance. After feature fusion, the softmax classifier layer is used for actual nodule classification. The overall system accuracy achieved is 98.08 percent, which is better than other existing systems.

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“…He has used ResNet50. VGG16, and VGG19 [3] algorithms for feature extraction in a system. Shanchen Pang [4] has used VGG16-T and multiple VGG16-T worked as weak classifiers.…”

Section: Literature Reviewmentioning

confidence: 99%

RETRACTED: Lung Nodule Classification Using Deep Learning Algorithms and Feature Fusion

Bhagwat,

Thirupurasundari

2024

Preprint

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“…Lung nodule classification was performed using features learned from two deep 3D customized mixed link network with gradient boosting machine ( 66 ). Studies have shown improved classification accuracy for malignant nodules using optimal deep feature selection from different CNN-based convolution layers and fusion of the deep features for the final classifier ( 67 – 69 ). A multi-scale cost-sensitive neural network was proposed to mitigate the issue of insufficient labeled data and class imbalance ( 70 ).…”

Section: Methods and Analysismentioning

confidence: 99%

“…Various advanced CNN models ( 41 – 45 ) and ensemble learners using multiple CNN models ( 46 , 49 – 53 ), transfer learning-based systems ( 54 – 57 ), and hybrid CNN-based systems ( 58 – 62 ) have been reported for classifying malignant and benign lung nodules. CNN with adaptive morphology and textural features ( 63 ), deep feature selection from different convolution layers ( 67 – 69 ), and 3D segmentation attention network-based systems ( 64 , 65 ) were reported for lung nodule classification. Recently, DL models based on transformers ( 73 , 74 ) along with CNN ( 75 , 76 ) have reported promising results for lung nodule detection and classification.…”

Section: Methods and Analysismentioning

confidence: 99%

A proposed methodology for detecting the malignant potential of pulmonary nodules in sarcoma using computed tomographic imaging and artificial intelligence-based models

Baidya Kayal,

Ganguly,

Sasi

et al. 2023

Front. Oncol.

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The presence of lung metastases in patients with primary malignancies is an important criterion for treatment management and prognostication. Computed tomography (CT) of the chest is the preferred method to detect lung metastasis. However, CT has limited efficacy in differentiating metastatic nodules from benign nodules (e.g., granulomas due to tuberculosis) especially at early stages (<5 mm). There is also a significant subjectivity associated in making this distinction, leading to frequent CT follow-ups and additional radiation exposure along with financial and emotional burden to the patients and family. Even 18F-fluoro-deoxyglucose positron emission technology-computed tomography (18F-FDG PET-CT) is not always confirmatory for this clinical problem. While pathological biopsy is the gold standard to demonstrate malignancy, invasive sampling of small lung nodules is often not clinically feasible. Currently, there is no non-invasive imaging technique that can reliably characterize lung metastases. The lung is one of the favored sites of metastasis in sarcomas. Hence, patients with sarcomas, especially from tuberculosis prevalent developing countries, can provide an ideal platform to develop a model to differentiate lung metastases from benign nodules. To overcome the lack of optimal specificity of CT scan in detecting pulmonary metastasis, a novel artificial intelligence (AI)-based protocol is proposed utilizing a combination of radiological and clinical biomarkers to identify lung nodules and characterize it as benign or metastasis. This protocol includes a retrospective cohort of nearly 2,000–2,250 sample nodules (from at least 450 patients) for training and testing and an ambispective cohort of nearly 500 nodules (from 100 patients; 50 patients each from the retrospective and prospective cohort) for validation. Ground-truth annotation of lung nodules will be performed using an in-house-built segmentation tool. Ground-truth labeling of lung nodules (metastatic/benign) will be performed based on histopathological results or baseline and/or follow-up radiological findings along with clinical outcome of the patient. Optimal methods for data handling and statistical analysis are included to develop a robust protocol for early detection and classification of pulmonary metastasis at baseline and at follow-up and identification of associated potential clinical and radiological markers.

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A survey on comparative study of lung nodules applying machine learning and deep learning techniques

Aishwarya,

Asuntha

2024

Multimed Tools Appl

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Investigation of Deep Features in Lung Nodule Classification

Cited by 12 publications

References 12 publications

RETRACTED: Lung Nodule Classification Using Deep Learning Algorithms and Feature Fusion

RETRACTED: Lung Nodule Classification Using Deep Learning Algorithms and Feature Fusion

A proposed methodology for detecting the malignant potential of pulmonary nodules in sarcoma using computed tomographic imaging and artificial intelligence-based models

A survey on comparative study of lung nodules applying machine learning and deep learning techniques

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