article · Frontiers in Medicine
Renal conditions such as kidney stones and renal cancer pose significant global health challenges. This research develops an artificial intelligence framework utilizing transfer learning to detect and classify renal diseases from computed tomography scans and microscopic histopathological images. The approach combines pre-trained convolutional neural networks with the Sparrow search algorithm to optimize model configurations. Performance was assessed on two distinct benchmarks: a four-class dataset covering cyst, normal, stone, and tumour categories, and a five-class dataset classifying tumour severity from Grade 0 to Grade 4. Across tests with eight pre-trained network architectures, DenseNet201 and MobileNet delivered the highest performance for four-class diagnosis, while DenseNet201 and Xception excelled in grading tumours. The optimized framework achieved classification accuracies of 99.98 percent on the four-class dataset and 100 percent on the five-class tumour grading dataset, outperforming existing state-of-the-art models.
Kidney stones affect up to 15 percent of the global population, and renal cancer accounts for 2.5 percent of all cancers worldwide. Automated diagnostic tools that achieve high accuracy in detecting disease and grading tumour severity can support radiologists and healthcare professionals, potentially reducing diagnostic delays and facilitating earlier clinical intervention.
The framework could be integrated into clinical decision-support software for radiologists and pathologists analysing renal scans and tissue samples. The findings reflect applied research tested on image datasets, demonstrating near-perfect classification under experimental conditions. However, the abstract does not report real-world clinical validation or prospective hospital trials, indicating that the technology remains at an early to intermediate developmental stage.
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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).
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DOI: 10.3389/fmed.2023.1106717
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