An improved CNN-based architecture for automatic lung nodule classification

Mahmood, Sozan Abdullah; Ahmed, Hunar Abubakir

doi:10.1007/s11517-022-02578-0

Cited by 10 publications

(4 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In clinical work, to reduce patient anxiety and avoid unnecessary surgery as well as reduce waste of medical resources, it is important to inform the patient whether surgical intervention is required and which lesions require priority surgery when the patient has multiple nodules. More recently, application of deep learning methods has improved lung nodule classification [ 25 ]. Jiang et al classified SSNs on CT images based on convolutional neural networks (CNN) model [ 26 ], the model showed high accuracy.…”

Section: Discussionmentioning

confidence: 99%

A CT-based nomogram for predicting the risk of adenocarcinomas in patients with subsolid nodule according to the 2021 WHO classification

Song

et al. 2022

Cancer Imaging

View full text Add to dashboard Cite

Purpose To establish a nomogram for predicting the risk of adenocarcinomas in patients with subsolid nodules (SSNs) according to the 2021 WHO classification. Methods A total of 656 patients who underwent SSNs resection were retrospectively enrolled. Among them, 407 patients were assigned to the derivation cohort and 249 patients were assigned to the validation cohort. Univariate and multi-variate logistic regression algorithms were utilized to identity independent risk factors of adenocarcinomas. A nomogram based on the risk factors was generated to predict the risk of adenocarcinomas. The discrimination ability of the nomogram was evaluated using the concordance index (C-index), its performance was calibrated using a calibration curve, and its clinical significance was evaluated using decision curves and clinical impact curves. Results Lesion size, mean CT value, vascular change and lobulation were identified as independent risk factors for adenocarcinomas. The C-index of the nomogram was 0.867 (95% CI, 0.833-0.901) in derivation cohort and 0.877 (95% CI, 0.836-0.917) in validation cohort. The calibration curve showed good agreement between the predicted and actual risks. Analysis of the decision curves and clinical impact curves revealed that the nomogram had a high standardized net benefit. Conclusions A nomogram for predicting the risk of adenocarcinomas in patients with SSNs was established in light of the 2021 WHO classification. The developed model can be adopted as a pre-operation tool to improve the surgical management of patients.

show abstract

Section: Discussionmentioning

confidence: 99%

A CT-based nomogram for predicting the risk of adenocarcinomas in patients with subsolid nodule according to the 2021 WHO classification

Song

et al. 2022

Cancer Imaging

View full text Add to dashboard Cite

show abstract

“…CNNs are widely used to screen pulmonary nodules. For example, Mahmood et al ( 17 ) developed a system based on AlexNet, a novel CNN in terms of layer ordering; it exhibits improved hyperparameters and functions. The image segmentation algorithm was used to process the lung scan sequence to generate a lung area map, and then, the lung image was generated according to the lung area map.…”

Section: Applications Of Ai In Lung Cancer Diagnosismentioning

confidence: 99%

Research in the application of artificial intelligence to lung cancer diagnosis

Liu,

Shen,

Zhang

et al. 2024

Front. Med.

View full text Add to dashboard Cite

The morbidity and mortality rates in lung cancer are high worldwide. Early diagnosis and personalized treatment are important to manage this public health issue. In recent years, artificial intelligence (AI) has played increasingly important roles in early screening, auxiliary diagnosis, and prognostic assessment. AI uses algorithms to extract quantitative feature information from high-volume and high-latitude data and learn existing data to predict disease outcomes. In this review, we describe the current uses of AI in lung cancer-focused pathomics, imageomics, and genomics applications.

show abstract

“…Mahmood and Ahmed [49] introduced an automatic CAD system based on AlexNet architecture to classify lung nodules. The proposed AlexNet architecture was tuned with several layers and hyperparameters to achieve superior performance.…”

Section: Literature Reviewmentioning

confidence: 99%

Lung Cancer Classification Using Modified U-Net Based Lobe Segmentation and Nodule Detection

et al. 2023

View full text Add to dashboard Cite

Lung cancer is the most common cause of cancer deaths worldwide. Early detection is crucial for successful treatment and increasing patient survival rates. Artificial intelligence techniques can play a significant role in the early detection of lung cancer. Various methods based on machine learning and deep learning approaches are used to detect lung cancer. This research works aims to develop automated methods to accurately identify and classify lung cancer in CT scans by using computational intelligence techniques. The process typically involves lobe segmentation, extracting candidate nodules, and classifying nodules as either cancer or non-cancer. The proposed lung cancer classification uses modified U-Net based lobe segmentation and nodule detection model consisting of three phases. The first phase segments lobe using CT slice and predicted mask using modified U-Net architecture and the second phase extracts candidate nodule using predicted mask and label employing modified U-Net architecture. Finally, the third phase is based on modified AlexNet, and a support vector machine is applied to classify candidate nodules into cancer and non-cancer. The experimental results of the proposed methodology for lobe segmentation, candidate nodule extraction, and classification of lung cancer have shown promising results on the publicly available LUAN16 dataset. The modified AlexNet-SVM classification model achieves 97.98% of accuracy, 98.84% of sensitivity, 97.47% of specificity, 97.53% of precision, and 97.70% of F1 for the classification of lung cancer.

show abstract

An improved CNN-based architecture for automatic lung nodule classification

Cited by 10 publications

References 11 publications

A CT-based nomogram for predicting the risk of adenocarcinomas in patients with subsolid nodule according to the 2021 WHO classification

A CT-based nomogram for predicting the risk of adenocarcinomas in patients with subsolid nodule according to the 2021 WHO classification

Research in the application of artificial intelligence to lung cancer diagnosis

Lung Cancer Classification Using Modified U-Net Based Lobe Segmentation and Nodule Detection

Contact Info

Product

Resources

About