V R Nitha, Chandra S S Vinod
Department of Computer Science, University of Kerala, Thiruvananthapuram 695581, India.
Diagnostics (Basel). 2023 Jun 29;13(13):2206. doi: 10.3390/diagnostics13132206.
Lung cancer is an abnormality where the body's cells multiply uncontrollably. The disease can be deadly if not detected in the initial stage. To address this issue, an automated lung cancer malignancy detection (ExtRanFS) framework is developed using transfer learning. We used the IQ-OTH/NCCD dataset gathered from the Iraq Hospital in 2019, encompassing CT scans of patients suffering from various lung cancers and healthy subjects. The annotated dataset consists of CT slices from 110 patients, of which 40 were diagnosed with malignant tumors and 15 with benign tumors. Fifty-five patients were determined to be in good health. All CT images are in DICOM format with a 1mm slice thickness, consisting of 80 to 200 slices at various sides and angles. The proposed system utilized a convolution-based pre-trained VGG16 model as the feature extractor and an Extremely Randomized Tree Classifier as the feature selector. The selected features are fed to the Multi-Layer Perceptron (MLP) Classifier for detecting whether the lung cancer is benign, malignant, or normal. The accuracy, sensitivity, and F1-Score of the proposed framework are 99.09%, 98.33%, and 98.33%, respectively. To evaluate the proposed model, a comparison is performed with other pre-trained models as feature extractors and also with the existing state-of-the-art methodologies as classifiers. From the experimental results, it is evident that the proposed framework outperformed other existing methodologies. This work would be beneficial to both the practitioners and the patients in identifying whether the tumor is benign, malignant, or normal.
肺癌是一种身体细胞不受控制地增殖的异常情况。如果在初始阶段未被检测到，这种疾病可能会致命。为了解决这个问题，利用迁移学习开发了一种自动肺癌恶性检测（ExtRanFS）框架。我们使用了2019年从伊拉克医院收集的IQ-OTH/NCCD数据集，其中包括患有各种肺癌的患者和健康受试者的CT扫描图像。带注释的数据集由110名患者的CT切片组成，其中40名被诊断患有恶性肿瘤，15名患有良性肿瘤。55名患者被确定为健康状况良好。所有CT图像均为DICOM格式，切片厚度为1mm，在不同的侧面和角度由80至200个切片组成。所提出的系统利用基于卷积的预训练VGG16模型作为特征提取器，并使用极端随机树分类器作为特征选择器。所选特征被输入到多层感知器（MLP）分类器中，以检测肺癌是良性、恶性还是正常。所提出框架的准确率、灵敏度和F1分数分别为99.09%、98.33%和98.33%。为了评估所提出的模型，将其与作为特征提取器的其他预训练模型以及作为分类器的现有最先进方法进行了比较。从实验结果可以明显看出，所提出的框架优于其他现有方法。这项工作对于从业者和患者识别肿瘤是良性、恶性还是正常都将是有益的。
