Department of Computer Science, College of Computer Science & Information Technology, Jazan University, Jizan, Saudi Arabia.
Department of Biomedical Engineering, NED University of Engineering and Technology, Karachi, Pakistan.
Comput Intell Neurosci. 2022 Aug 30;2022:3836539. doi: 10.1155/2022/3836539. eCollection 2022.
With an astounding five million fatal cases every year, lung cancer is among the leading causes of mortality worldwide for both men and women. The diagnosis of lung illnesses can benefit from the information a computed tomography (CT) scan can offer. The major goals of this study are to diagnose lung cancer and its seriousness and to identify malignant lung nodules from the provided input lung picture. This paper applies unique deep learning techniques to identify the exact location of the malignant lung nodules. Using a DenseNet model, mixed ground glass is analyzed in low-dose, low-resolution CT scan images of nodules (mGGNs) with a slice thickness of 5 mm in this study. This was done to categorize and identify many histological subtypes of lung cancer. Low-resolution CT scans are used to pathologically classify invasive adenocarcinoma (IAC) and minimally invasive adenocarcinoma (MIA). 105 low-resolution CT images with 5 mm thick slices from 105 patients at Lishui Central Hospital were selected. To detect and distinguish, IAC and MIA, extend and enhance deep learning two- and three-dimensional DenseNet models are used. The two-dimensional DenseNet model was shown to perform much better than the three-dimensional DenseNet model in terms of classification accuracy (76.67%), sensitivity (63.3%), specificity (100%), and area under the receiver operating characteristic curve (0.88). Finding the histological subtypes of persons with lung cancer should aid doctors in making a more precise diagnosis, even if the image quality is not outstanding.
每年有惊人的 500 万例死亡病例，肺癌是全球男性和女性的主要死亡原因之一。计算机断层扫描（CT）可以提供的信息可以帮助诊断肺部疾病。本研究的主要目的是诊断肺癌及其严重程度，并从提供的输入肺部图像中识别恶性肺结节。本文应用独特的深度学习技术来确定恶性肺结节的确切位置。本研究使用 DenseNet 模型分析了结节（mGGNs）的低剂量、低分辨率 CT 扫描图像中的混合磨玻璃（mixed ground glass），这些图像的切片厚度为 5mm。这样做是为了对肺癌的许多组织学亚型进行分类和识别。低分辨率 CT 扫描用于对浸润性腺癌（IAC）和微浸润性腺癌（MIA）进行病理分类。从丽水市中心医院的 105 名患者中选择了 105 张 5mm 厚切片的低分辨率 CT 图像，用于检测和区分 IAC 和 MIA，并扩展和增强二维和三维 DenseNet 模型的深度学习功能。二维 DenseNet 模型在分类准确率（76.67%）、敏感度（63.3%）、特异性（100%）和接收器工作特征曲线下面积（0.88）方面的表现明显优于三维 DenseNet 模型。发现肺癌患者的组织学亚型应该有助于医生做出更准确的诊断，即使图像质量不是很出色。
