Lung Cancer Detection and Severity Analysis with a 3D Deep Learning CNN Model Using CT-DICOM Clinical Dataset

K. J. Eldho, S. Nithyanandh
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Abstract

Objectives: To propose a new AI based CAD model for early detection and severity analysis of pulmonary (lung) cancer disease. A deep learning artificial intelligence-based approach is employed to maximize the discrimination power in CT images and minimize the dimensionality in order to boost detection accuracy. Methods: The AI-based 3D Convolutional Neural Network (3D-DLCNN) method is employed to learn complex patterns and features in a robust way for efficient detection and classification. The pulmonary nodules are identified by 3D Mask-R-CNN at the initial level, and classification is done by 3D-DLCNN. Kernel Density Estimation (KDE) is used to discover the error data points in the extracted features for early removal before candidate screening. The study uses the CT-DICOM dataset, which includes 355 instances and 251135 CT-DICOM images with target attributes of cancer, healthy, and severity condition (if cancer is positive). Statistical outlier detection is utilized to measure the z-score of each feature to reduce the data point deviation. The intensity and pixel masking of CT-DOCIM is measured by using the ER-NCN method to identify the severity of the disease. The performance of the 3D-DLCNN model is done using the MATLAB R2020a tool and comparative analysis is done with prevailing detection and classification approaches such as GA-PSO, SVM, KNN, and BPNN. Findings: The suggested pulmonary detection 3D-DLCNN model outperforms the prevailing models with promising results of 93% accuracy rate, 92.7% sensitivity, 93.4% specificity, 0.8 AUC-ROC, 6.6% FPR, and 0.87 C-Index, which helps the pulmonologists detect the PC and identify the severity for early diagnosis. Novelty: The novel hybrid 3D-DLCNN approach has the ability to detect pulmonary disease and analyze the severity score of the patient at an early stage during the screening process of candidates. It overcomes the limitations of the prevailing machine learning models, GA-PSO, SVM, KNN, and BPNN. Keywords: Artificial Intelligence, Disease Prediction, Lung Cancer, Deep Learning, Cancer Detection, Computational Model, 3D-DLCNN
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使用 CT-DICOM 临床数据集的三维深度学习 CNN 模型进行肺癌检测和严重程度分析
目的提出一种新的基于人工智能的 CAD 模型,用于肺癌疾病的早期检测和严重程度分析。采用基于深度学习的人工智能方法,最大限度地提高 CT 图像的分辨能力,并最小化维度,以提高检测准确率。方法采用基于人工智能的三维卷积神经网络(3D-DLCNN)方法,以稳健的方式学习复杂的模式和特征,从而实现高效的检测和分类。肺结节在初始级别由 3D Mask-R-CNN 识别,并由 3D-DLCNN 进行分类。核密度估计(KDE)用于发现提取特征中的错误数据点,以便在候选筛查之前及早剔除。研究使用了 CT-DICOM 数据集,其中包括 355 个实例和 251135 张 CT-DICOM 图像,目标属性为癌症、健康和严重程度(如果癌症为阳性)。该数据集包括 355 个实例和 251135 张 CT-DICOM 图像,目标属性为癌症、健康和严重程度(如果癌症为阳性)。统计离群点检测用于测量每个特征的 z-score,以减少数据点偏差。使用 ER-NCN 方法测量 CT-DOCIM 的强度和像素掩蔽,以识别疾病的严重程度。使用 MATLAB R2020a 工具对 3D-DLCNN 模型的性能进行了测试,并与 GA-PSO、SVM、KNN 和 BPNN 等主流检测和分类方法进行了比较分析。研究结果建议的肺部检测 3D-DLCNN 模型的准确率为 93%,灵敏度为 92.7%,特异性为 93.4%,AUC-ROC 为 0.8,FPR 为 6.6%,C-Index 为 0.87,其结果优于现有模型,可帮助肺科医生检测 PC 并识别其严重程度以进行早期诊断。新颖性:新型混合 3D-DLCNN 方法能够在筛选候选者的早期阶段检测肺部疾病并分析患者的严重程度评分。它克服了现有机器学习模型(GA-PSO、SVM、KNN 和 BPNN)的局限性。关键词人工智能 疾病预测 肺癌 深度学习 癌症检测 计算模型 3D-DLCNN
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