Leveraging ensemble convolutional neural networks and metaheuristic strategies for advanced kidney disease screening and classification - Abeer saber; Esraa Hassan; Samar Elbedwehy; Wael A. Awad; Tamer Z. Emara (Scientific Reports)
Abstract To address the public health issue of renal failure and the global shortage of nephrologists, an AI-based system has been developed to automatically identify kidney diseases. Recent advancements in machine learning, deep learning (DL), and artificial intelligence (AI) have unlocked new possibilities in healthcare. By harnessing these technologies, we can analyze data to gain insights into symptoms and patterns, ultimately facilitating remote patient care. To create an AI-based diagnosis system for kidney disease, this paper focused on the three major categories of kidney diseases: stones, cysts, and tumors, which were collected and annotated on 12,446 computed tomography (CT) whole abdomen and urogram images. To effectively aid in the automatic identification and diagnosis of kidney diseases, a novel DL model built on the transfer-learning (TL) technology is implemented in this work. DL models are designed to focus on problems, whereas TL uses the knowledge acquired while resolving one issue to another pertinent issue. The proposed model combines multiple DL models to improve overall performance by leveraging the strengths of different architectures, ensembles can enhance accuracy, robustness, and generalization. It enhances the features extracted from MobileNet-V2, ResNet50, and EfficientNet-B0 networks using metaheuristic algorithms and bidirectional long-short-term memory (Bi-LSTM) from the CT image. MobileNetV2, ResNet50, and EfficientNet-B0 hyperparameters have been optimized using a modified grey wolf optimization (GWO) approach for better performance. The suggested model’s performance has been measured using five assessment metrics: accuracy, sensitivity, specificity, precision, and area under the ROC curve (AUC) and achieved 99.85% accuracy, 99.8% sensitivity, 99.3% specificity, 98.1% precision, and 1.0 AUC.