End-to-End Lung Nodule Detection Framework with Model-Based Feature Projection Block

Drokin, Ivan; Ericheva, Elena

doi:10.1007/978-3-030-87589-3_10

Cited by 5 publications

(3 citation statements)

References 25 publications

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“…Fang utilizes multi-view features of three-dimensional CT scans employing MIP for automatic detection of lung cancer nodules [ 32 ]. Drokin and fellow authors propose an end-to-end framework for detecting suspicious pulmonary nodules using MIP images based on U-Net like CNN, achieving an average sensitivity of 95% [ 33 ]. DL with MIP feature helps in achieving higher classification performance for distinguishing benign and malignant lung cancer nodules [ 34 ].…”

Section: Discussionmentioning

confidence: 99%

An Effective Method for Lung Cancer Diagnosis from CT Scan Using Deep Learning-Based Support Vector Network

Shafi

Din

Khan

et al. 2022

Cancers

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The diagnosis of early-stage lung cancer is challenging due to its asymptomatic nature, especially given the repeated radiation exposure and high cost of computed tomography(CT). Examining the lung CT images to detect pulmonary nodules, especially the cell lung cancer lesions, is also tedious and prone to errors even by a specialist. This study proposes a cancer diagnostic model based on a deep learning-enabled support vector machine (SVM). The proposed computer-aided design (CAD) model identifies the physiological and pathological changes in the soft tissues of the cross-section in lung cancer lesions. The model is first trained to recognize lung cancer by measuring and comparing the selected profile values in CT images obtained from patients and control patients at their diagnosis. Then, the model is tested and validated using the CT scans of both patients and control patients that are not shown in the training phase. The study investigates 888 annotated CT scans from the publicly available LIDC/IDRI database. The proposed deep learning-assisted SVM-based model yields 94% accuracy for pulmonary nodule detection representing early-stage lung cancer. It is found superior to other existing methods including complex deep learning, simple machine learning, and the hybrid techniques used on lung CT images for nodule detection. Experimental results demonstrate that the proposed approach can greatly assist radiologists in detecting early lung cancer and facilitating the timely management of patients.

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Section: Discussionmentioning

confidence: 99%

An Effective Method for Lung Cancer Diagnosis from CT Scan Using Deep Learning-Based Support Vector Network

Shafi

Din

Khan

et al. 2022

Cancers

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“…и 3000 исследований, дополнительно содержащих исследования с признаками COVID-19 (дополнительные 500 исследований к 2500 первого датасета). Описание архитектуры и методики обучения моделей приведено в работах [11,12].…”

Section: описание использования моделей искусственного интеллектаunclassified

Evaluation of the prospects for using artificial intelligence technologies to analyze CT scans of the chest organs in order to identify signs of malignant neoplasms in the lungs

Pilus

Drokin

Bazhenova

et al. 2023

Medicinskaâ vizualizaciâ

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The purpose of the study. To analyze the possibility of using artificial intelligence as a decision support system for radiologists for pulmonary nodules detection on Chest CT before and during the COVID-19 pandemic on the example of the system Botkin.AI.Materials and methods. Two groups of Chest CT studies were identified: those performed before (group 1) and during the COVID-19 pandemic (group 2). Each group contains anonymized CT data of 150 patients. Chest CT scans for group 2 were selected based on the percentage of coronavirus lung damage from 0 to 25%. The research was analyzed by the artificial intelligence system Botkin. AI for the presence of peripheral pulmonary nodes up to 6 mm, followed by a “blind” check of the analysis results by three radiologists.Results. In group 1, the sensitivity of the method was 1.0; specificity – 0.88 and AUC – 0.94. In the 2nd group 0.93; 0.81 and 0.86, respectively.In group 2, a slight decrease in specificity is mainly associated with an increase in false positive results in the pulmonary opcities, as manifestations of coronavirus lung damage, taken by the AI model for pulmonary nodes.Conclusion. The platform has a high accuracy of detecting pulmonary nodules on computed tomography of the chest both in studies conducted before and during the COVID-19 pandemic. It can be useful to prevent possible omissions of important findings in conditions of increased workload for radiologists.

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“…Lung cancer begins in the lungs and spreads throughout the rest of the body ( 1 ), including the brain. Lung cancer is the most common cause of cancer-related mortality worldwide ( 2 ).…”

Section: Introductionmentioning

confidence: 99%

Hybrid healthcare unit recommendation system using computational techniques with lung cancer segmentation

Albalawi,

Neal Joshua,

Joys

et al. 2024

Front. Med.

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IntroductionOur research addresses the critical need for accurate segmentation in medical healthcare applications, particularly in lung nodule detection using Computed Tomography (CT). Our investigation focuses on determining the particle composition of lung nodules, a vital aspect of diagnosis and treatment planning.MethodsOur model was trained and evaluated using several deep learning classifiers on the LUNA-16 dataset, achieving superior performance in terms of the Probabilistic Rand Index (PRI), Variation of Information (VOI), Region of Interest (ROI), Dice Coecient, and Global Consistency Error (GCE).ResultsThe evaluation demonstrated a high accuracy of 91.76% for parameter estimation, confirming the effectiveness of the proposed approach.DiscussionOur investigation focuses on determining the particle composition of lung nodules, a vital aspect of diagnosis and treatment planning. We proposed a novel segmentation model to identify lung disease from CT scans to achieve this. We proposed a learning architecture that combines U-Net with a Two-parameter logistic distribution for accurate image segmentation; this hybrid model is called U-Net++, leveraging Contrast Limited Adaptive Histogram Equalization (CLAHE) on a 5,000 set of CT scan images.

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End-to-End Lung Nodule Detection Framework with Model-Based Feature Projection Block

Cited by 5 publications

References 25 publications

An Effective Method for Lung Cancer Diagnosis from CT Scan Using Deep Learning-Based Support Vector Network

An Effective Method for Lung Cancer Diagnosis from CT Scan Using Deep Learning-Based Support Vector Network

Evaluation of the prospects for using artificial intelligence technologies to analyze CT scans of the chest organs in order to identify signs of malignant neoplasms in the lungs

Hybrid healthcare unit recommendation system using computational techniques with lung cancer segmentation

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