Development of a Deep Mask Region-Based Convolutional Neural Network with Improved Weighted Quantum Wolf Optimization for Sickle Cell Anaemia Detection and Classification

Abstract

To effectively manage Sickle Cell Anaemia (SCA), a severe inherited blood condition, early detection and accurate categorization are essential. Timely and precise identification is hindered by the high error rates, limited scalability, and dependency on personal expertise associated with conventional diagnostic approaches. To address these challenges, this study proposes a novel Deep Mask Region-Based Convolutional Neural Network (DMRCNN) combined with Improved Weighted Quantum Wolf Optimization (IWQWO) for SCA identification and classification. By leveraging advanced feature extraction and segmentation methods, the DMRCNN enables precise localization and identification of abnormal cells in blood smear images. The IWQWO method optimizes the DMRCNN's hyper parameters enhancing the network's efficiency by achieving an optimal balance between convergence speed and prediction accuracy. The primary objectives of this study are to improve classification accuracy, reduce computational overhead, and minimize false positives and false negatives in SCA diagnostics. Experimental results demonstrate that the proposed system achieves 96.8% precision, 97.2% accuracy, 96.5% recall and 96.8% F1-score, surpassing existing approaches with superior metrics. These findings underscore the effectiveness of the proposed method in automating SCA detection and classification, paving the way for potential integration into clinical workflows for accurate and timely assessments.