Ansari Mohd Munazzer, Kumar Shailendra, Heyat Md Belal Bin, Ullah Hadaate, Bin Hayat Mohd Ammar, Parveen Saba, Ali Ahmad, Zhang Tao
Department of Electronic and Communication Engineering, Integral University, Lucknow, India.
CenBRAIN Neurotech Center of Excellence, School of Engineering, Westlake University, Hangzhou, Zhejiang, China.
Curr Med Imaging. 2025;21:e15734056348824. doi: 10.2174/0115734056348824241224100809.
Lung cancer remains a leading cause of cancer-related mortality worldwide, necessitating early and accurate detection methods. Our study aims to enhance lung cancer detection by integrating VGGNet-16 form of Convolutional Neural Networks (CNNs) and Support Vector Machines (SVM) into a hybrid model (SVMVGGNet-16), leveraging the strengths of both models for high accuracy and reliability in classifying lung cancer types in different 4 classes such as adenocarcinoma (ADC), large cell carcinoma (LCC), Normal, and squamous cell carcinoma (SCC).
Using the LIDC-IDRI dataset, we pre-processed images with a median filter and histogram equalization, segmented lung tumors through thresholding and edge detection, and extracted geometric features such as area, perimeter, eccentricity, compactness, and circularity. VGGNet-16 and SVM employed for feature extraction and classification, respectively. Performance matrices were evaluated using accuracy, AUC, recall, precision, and F1-score. Both VGGNet-16 and SVM underwent comparative analysis during the training, validation, and testing phases.
The SVMVGGNet-16 model outperformed both, with a training accuracy (97.22%), AUC (99.42%), recall (94.22%), precision (95.28%), and F1- score (94.68%). In testing, our SVMVGGNet-16 model maintained high accuracy (96.72%), with an AUC (96.87%), recall (84.67%), precision (87.40%), and F1-score (85.73%).
Our experimental results demonstrate the potential of SVMVGGNet-16 in improving diagnostic performance, leading to earlier detection and better treatment outcomes. Future work includes refining the model, expanding datasets, conducting clinical trials, and integrating the system into clinical practice to ensure practical usability.
肺癌仍是全球癌症相关死亡的主要原因，因此需要早期准确的检测方法。我们的研究旨在通过将卷积神经网络（CNN）的VGGNet - 16形式与支持向量机（SVM）集成到一个混合模型（SVMVGGNet - 16）中来增强肺癌检测，利用这两种模型的优势，在腺癌（ADC）、大细胞癌（LCC）、正常和鳞状细胞癌（SCC）等不同的4类肺癌类型分类中实现高精度和高可靠性。
使用LIDC - IDRI数据集，我们用中值滤波器和直方图均衡化对图像进行预处理，通过阈值处理和边缘检测分割肺部肿瘤，并提取面积、周长、偏心率、紧凑度和圆形度等几何特征。VGGNet - 16和SVM分别用于特征提取和分类。使用准确率、AUC、召回率、精确率和F1分数评估性能矩阵。在训练、验证和测试阶段对VGGNet - 16和SVM都进行了对比分析。
SVMVGGNet - 16模型的表现优于两者，训练准确率为97.22%，AUC为99.42%，召回率为94.22%，精确率为95.28%，F1分数为94.68%。在测试中，我们的SVMVGGNet - 16模型保持了较高的准确率（96.72%），AUC为96.87%，召回率为84.67%，精确率为87.40%，F1分数为85.73%。
我们的实验结果证明了SVMVGGNet - 16在提高诊断性能方面的潜力，从而实现更早的检测和更好的治疗效果。未来的工作包括优化模型、扩展数据集、进行临床试验以及将该系统集成到临床实践中以确保实际可用性。
