An efficient convolutional global gated recurrent-based adaptive gazelle algorithm for enhanced disease detection and classification

In modern healthcare, medical image processing plays a vital role in enabling early disease detection, treatment planning, and improved patient care. However, traditional methods face challenges such as handling big data, scalability, and computational intensity. To address these issues, this paper proposes a Convolutional Global Gated Recurrent-based Adaptive Gazelle (CGGR-AG) algorithm for medical image processing applications. The CGGR-AG algorithm detects abnormalities and classifies specific objects within images by leveraging Convolutional Neural Networks (CNNs) for feature extraction and Gated Recurrent Units (GRUs) for capturing sequential patterns. Additionally, the Adaptive Gazelle Optimization algorithm fine-tunes parameters to enhance the effectiveness of the CGGR-AG method. Experimental validation is conducted on Tuberculosis and heart disease datasets, evaluating performance metrics including recall, specificity, accuracy, Area Under the Curve – Receiver Operating Characteristic (AUC-ROC), precision, and F1-score. Comparative analysis with state-of-the-art methods demonstrates the effectiveness of the CGGR-AG method in medical image processing applications.