Badawy Mahmoud, Almars Abdulqader M, Balaha Hossam Magdy, Shehata Mohamed, Qaraad Mohammed, Elhosseini Mostafa
Department of Computers and Control Systems Engineering, Faculty of Engineering, Mansoura University, Mansoura, Egypt.
Department of Computer Science and Informatics, Applied College, Taibah University, Al Madinah Al Munawwarah, Saudi Arabia.
Front Med (Lausanne). 2023 Apr 5;10:1106717. doi: 10.3389/fmed.2023.1106717. eCollection 2023.
Renal diseases are common health problems that affect millions of people around the world. Among these diseases, kidney stones, which affect anywhere from 1 to 15% of the global population and thus; considered one of the leading causes of chronic kidney diseases (CKD). In addition to kidney stones, renal cancer is the tenth most prevalent type of cancer, accounting for 2.5% of all cancers. Artificial intelligence (AI) in medical systems can assist radiologists and other healthcare professionals in diagnosing different renal diseases (RD) with high reliability. This study proposes an AI-based transfer learning framework to detect RD at an early stage. The framework presented on CT scans and images from microscopic histopathological examinations will help automatically and accurately classify patients with RD using convolutional neural network (CNN), pre-trained models, and an optimization algorithm on images. This study used the pre-trained CNN models VGG16, VGG19, Xception, DenseNet201, MobileNet, MobileNetV2, MobileNetV3Large, and NASNetMobile. In addition, the Sparrow search algorithm (SpaSA) is used to enhance the pre-trained model's performance using the best configuration. Two datasets were used, the first dataset are four classes: cyst, normal, stone, and tumor. In case of the latter, there are five categories within the second dataset that relate to the severity of the tumor: Grade 0, Grade 1, Grade 2, Grade 3, and Grade 4. DenseNet201 and MobileNet pre-trained models are the best for the four-classes dataset compared to others. Besides, the SGD Nesterov parameters optimizer is recommended by three models, while two models only recommend AdaGrad and AdaMax. Among the pre-trained models for the five-class dataset, DenseNet201 and Xception are the best. Experimental results prove the superiority of the proposed framework over other state-of-the-art classification models. The proposed framework records an accuracy of 99.98% (four classes) and 100% (five classes).
肾脏疾病是常见的健康问题，影响着全球数百万人。在这些疾病中，肾结石影响着全球1%至15%的人口，因此被认为是慢性肾脏病（CKD）的主要病因之一。除了肾结石，肾癌是第十大最常见的癌症类型，占所有癌症的2.5%。医疗系统中的人工智能（AI）可以帮助放射科医生和其他医疗保健专业人员以高可靠性诊断不同的肾脏疾病（RD）。本研究提出了一种基于AI的迁移学习框架，用于早期检测RD。该框架基于CT扫描和微观组织病理学检查的图像，将有助于使用卷积神经网络（CNN）、预训练模型和图像优化算法自动准确地对RD患者进行分类。本研究使用了预训练的CNN模型VGG16、VGG19、Xception、DenseNet201、MobileNet、MobileNetV2、MobileNetV3Large和NASNetMobile。此外，使用麻雀搜索算法（SpaSA）以最佳配置提高预训练模型的性能。使用了两个数据集，第一个数据集有四类：囊肿、正常、结石和肿瘤。对于后者，第二个数据集有五类与肿瘤严重程度相关：0级、1级、2级、3级和4级。与其他模型相比，DenseNet201和MobileNet预训练模型在四类数据集上表现最佳。此外，三种模型推荐使用SGD Nesterov参数优化器，而两种模型仅推荐AdaGrad和AdaMax。在五类数据集的预训练模型中，DenseNet201和Xception表现最佳。实验结果证明了所提出框架优于其他现有分类模型。所提出的框架在四类数据集上的准确率为99.98%，在五类数据集上的准确率为100%。
