An Enhanced Paediatric Respiratory Classification Using Ensemble Machine Learning Techniques and Modified Artificial Bee Colony Optimization
Keywords:
Artificial Bee Colony Optimization, RCNN, MASK-RCNN, GLCM,SVR, VGG16, PSOAbstract
In recent years, the advancement of machine learning techniques has significantly impacted medical research, particularly in the field of pediatric respiratory diseases. This study proposes a comprehensive research model for the classification and prediction of pediatric respiratory conditions using machine learning algorithms and optimized methodologies. The model integrates several innovative techniques to enhance accuracy and efficiency in diagnosing and predicting respiratory disorders in children. The first phase of the proposed model focuses on denoising respiratory data using a Residual Noise Elimination Neural Network (RNEN). This step is crucial for improving the quality of input data and reducing noise interference, thereby enhancing the accuracy of subsequent analyses. Following denoising, the model employs an Improved Mask R-CNN (Region-based Convolutional Neural Network) algorithm for segmentation tasks. This advanced segmentation technique accurately identifies and delineates regions of interest within medical images, facilitating precise feature extraction in the subsequent steps. Feature extraction is performed using an Enhanced Gray-Level Co-occurrence Matrix (GLCM) approach, which captures intricate textural information from segmented images. The enhanced GLCM method ensures robust feature representation, capturing essential patterns and characteristics relevant to respiratory disease classification. For classification tasks, the model utilizes an improved VGG16 (Visual Geometry Group 16) convolutional neural network architecture. The enhanced VGG16 model is trained on the extracted features to classify respiratory conditions with high accuracy and reliability, leveraging deep learning capabilities for pattern recognition and disease diagnosis. To optimize the prediction process, a Modified Artificial Bee Colony (ABC) Optimization algorithm is proposed. This modified ABC algorithm enhances the efficiency of parameter tuning and model optimization, leading to improved prediction performance and reduced computational overhead. Overall, the proposed research model offers a comprehensive framework for pediatric respiratory disease analysis, integrating state-of-the-art machine learning techniques with optimized algorithms for denoising, segmentation, feature extraction, classification, and prediction. Experimental results demonstrate the efficacy and robustness of the model in accurately diagnosing and predicting respiratory conditions in pediatric patients, thereby contributing to advancements in pediatric healthcare and medical decision-making.
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