Ensemble framework based on attributes and deep features for benign-malignant classification of lung nodule

Qiao, Jianping; Fan, Yujiang; Zhang, Mowen; Fang, Kunlun; Li, Dengwang; Wang, Zhishun

doi:10.1016/j.bspc.2022.104217

Cited by 9 publications

(2 citation statements)

References 54 publications

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“…The authors tested their algorithm on a newly constructed dataset named CQUCH-LND along with the public LIDC-IDRI dataset. Qiao et al [18] proposed an ensemble learning method named Fuse-Long Short-Term Memory-Convolutional Neural Network (F-LSTM-CNN). Their approach combines visual attributes and deep features to distinguish between benign and malignant nodules.…”

Section: Introductionmentioning

confidence: 99%

Advancing Pulmonary Nodule Diagnosis by Integrating Engineered and Deep Features Extracted from CT Scans

Safta,

Shaffie

2024

Algorithms

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Enhancing lung cancer diagnosis requires precise early detection methods. This study introduces an automated diagnostic system leveraging computed tomography (CT) scans for early lung cancer identification. The main approach is the integration of three distinct feature analyses: the novel 3D-Local Octal Pattern (LOP) descriptor for texture analysis, the 3D-Convolutional Neural Network (CNN) for extracting deep features, and geometric feature analysis to characterize pulmonary nodules. The 3D-LOP method innovatively captures nodule texture by analyzing the orientation and magnitude of voxel relationships, enabling the distinction of discriminative features. Simultaneously, the 3D-CNN extracts deep features from raw CT scans, providing comprehensive insights into nodule characteristics. Geometric features and assessing nodule shape further augment this analysis, offering a holistic view of potential malignancies. By amalgamating these analyses, our system employs a probability-based linear classifier to deliver a final diagnostic output. Validated on 822 Lung Image Database Consortium (LIDC) cases, the system’s performance was exceptional, with measures of 97.84%, 98.11%, 94.73%, and 0.9912 for accuracy, sensitivity, specificity, and Area Under the ROC Curve (AUC), respectively. These results highlight the system’s potential as a significant advancement in clinical diagnostics, offering a reliable, non-invasive tool for lung cancer detection that promises to improve patient outcomes through early diagnosis.

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Section: Introductionmentioning

confidence: 99%

Advancing Pulmonary Nodule Diagnosis by Integrating Engineered and Deep Features Extracted from CT Scans

Safta,

Shaffie

2024

Algorithms

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“…However, when these cells and tissues grow uncontrollably, they may become cancerous, resulting in abnormal behavior and unwanted growth within the body (Forte et al 2022) . There are various types of cancer, but the prevalence of lung cancer is huge around the globe as it is the most life-threatening disease in humans (Qiao et al 2023). According to research published by GLOBOCAN (global cancer observatory) in 2020, statistical analysis of cancer spread revealed that lung cancer ranks as the second most common cancer among humans, with nearly 2.2 million cases diagnosed globally.…”

Section: Introductionmentioning

confidence: 99%

Hyperparameter optimization and development of an advanced CNN-based technique for lung nodule assessment

Shivwanshi,

Nirala

2023

Phys. Med. Biol.

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Objective: This paper aims to propose an advanced methodology for assessing lung nodules using automated techniques with computed tomography (CT) images to detect lung cancer at an early stage. Approach: The proposed methodology utilizes a fixed-size 3 x 3 kernel in a convolution neural network (CNN) for relevant feature extraction. The network architecture comprises 13 layers, including six convolution layers for deep local and global feature extraction. The nodule detection architecture is enhanced by incorporating a transfer learning-based EfficientNetV_2 network (TLEV2N) to improve training performance. The classification of nodules is achieved by integrating the EfficientNet_V2 architecture of CNN for more accurate benign and malignant classification. The network architecture is fine-tuned to extract relevant features using a deep network while maintaining performance through suitable hyperparameters. Main Results: The proposed method significantly reduces the false-negative rate, with the network achieving an accuracy of 97.56% and a specificity of 98.4%. Using the 3 x 3 kernel provides valuable insights into minute pixel variation and enables the extraction of information at a broader morphological level. The continuous responsiveness of the network to fine-tune initial values allows for further optimization possibilities, leading to the design of a standardized system capable of assessing diversified thoracic CT datasets. Significance: This paper highlights the potential of non-invasive techniques for the early detection of lung cancer through the analysis of low-dose CT images. The proposed methodology offers improved accuracy in detecting lung nodules and has the potential to enhance the overall performance of early lung cancer detection. By reconfiguring the proposed method, further advancements can be made to optimize outcomes and contribute to developing a standardized system for assessing diverse thoracic CT datasets.

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Weighted ensemble CNN for lung nodule classification: an evolutionary approach

Naik,

Edla,

Parne

et al. 2024

Multimed Tools Appl

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Ensemble framework based on attributes and deep features for benign-malignant classification of lung nodule

Cited by 9 publications

References 54 publications

Advancing Pulmonary Nodule Diagnosis by Integrating Engineered and Deep Features Extracted from CT Scans

Advancing Pulmonary Nodule Diagnosis by Integrating Engineered and Deep Features Extracted from CT Scans

Hyperparameter optimization and development of an advanced CNN-based technique for lung nodule assessment

Weighted ensemble CNN for lung nodule classification: an evolutionary approach

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