Multimodal Feature Integration for Advanced Pneumonia Diagnosis

The article outlines a detailed workflow for pneumonia diagnosis using advanced imaging and computational techniques. The process begins with the curation of a diverse chest X-ray dataset, followed by the application of Non-Local Means denoising. This preprocessing step effectively minimizes noise while preserving critical edge details essential for accurate analysis. To improve diagnostic accuracy, advanced feature extraction methods, including Colour Histogram, Local Phase Quantization, and Multimodal Feature Integration, are employed. These techniques provide a comprehensive representation of the data by capturing colour distribution, textural details, and combined feature modalities. The extracted features are then utilized to train classifiers such as decision tree and k-nearest neighbour, chosen for their pattern recognition capabilities. In addition, a neural network model is developed and optimized for pneumonia classification, further enhancing the system’s performance. Among the various approaches tested, the integration of AlexNet features with Colour Histogram demonstrated superior performance, achieving an accuracy of 99.08% using the Decision Tree classifier and 93.90% with the k-Nearest Neighbour classifier. This workflow underscores significant advancements in artificial intelligence, particularly in combining deep learning with traditional feature extraction techniques to address complex medical imaging challenges. By leveraging diverse feature modalities and integrating machine learning with neural networks, the proposed approach offers a robust, efficient solution for pneumonia diagnosis. Its versatility and adaptability make it a valuable contribution to healthcare diagnostics, enabling more accurate and efficient interpretation of chest X-ray images for clinical decision-making.