Multiclass Osteoporosis Detection: Enhancing Accuracy with Woodpecker-Optimized CNN-XGBoost

• Published in: International journal of advanced computer science & applications 2024, Vol.15 (8)
• Main Authors: DSouza, Mithun, Nimma, Divya, Pokkuluri, Kiran Sree, Ramesh, Janjhyam Venkata Naga, Kondaveeti, Suresh Babu, Kongala, Lavanya
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Artificial neural networks; Bone density; Classification; Diagnosis; Feature extraction; Image enhancement; Machine learning; Optimization; Optimization algorithms; Osteoporosis; Parameter identification; Structured data; Task complexity
• ISSN: 2158-107X; 2156-5570
• DOI: 10.14569/IJACSA.2024.0150889

In the realm of medical diagnostics, accurately identifying osteoporosis through multiclass classification poses a significant challenge due to the subtle variations in bone density and structure. This study proposes a novel approach to enhance detection accuracy by integrating the Woodpecker Optimization Algorithm with a hybrid Convolutional Neural Network (CNN) and XGBoost model. The Woodpecker Optimization Algorithm is employed to fine-tune the CNN-XGBoost model parameters, leveraging its ability to efficiently search for optimal configurations amidst complex data landscapes. The proposed framework begins with the CNN component, designed to automatically extract hierarchical features from bone density images. This initial stage is crucial for capturing intricate patterns that signify osteoporotic conditions across multiple classes. Subsequently, the extracted features are fed into an XGBoost classifier, renowned for its robust performance in handling structured data and multiclass classification tasks. By combining these two powerful techniques, the model aims to synergistically utilize the strengths of deep learning in feature extraction and gradient boosting in decision-making. Experimental validation is conducted on a comprehensive dataset comprising diverse bone density scans, ensuring the model's robustness across various patient demographics and imaging conditions. Performance criteria including recall, precision, reliability, and F1-score are assessed to show how well the suggested Woodpecker-optimized CNN-XGBoost framework performs in comparison to other approaches when it comes to obtaining better accuracy in diagnosis. The findings underscore the potential of hybrid models in advancing osteoporosis detection, offering clinicians a reliable tool for early and precise diagnosis, thereby facilitating timely interventions to mitigate the debilitating effects of bone-related diseases. Osteoporosis detection model with a classification accuracy of 97.1% implemented in Python.