Bone Cancer Cell Prediction Using an Enhanced Deep Learning Algorithm with an Optimization Technique.

OBJECTIVE

Bone cancer is a very serious disorder that can be fatal for many people. A reliable detection and classification system is required for early-stage bone cancer diagnosis. Traditional manual approaches are time-consuming and need specific skills, thus the creation of an automated system for detecting and classifying malignant and healthy bone tissue is critical. Cancerous bone tissue often has a different texture than healthy tissue in the affected location. Despite initially using Support Vector Machine and Edge Detection methods to improve outcomes, we only reached 0.92% accuracy. As a result, moving to deep learning is important for improved performance. Our strategy will begin with feature extraction, followed by the usage of the Cuckoo Search optimization algorithm.

METHODS

The methodology emphasizes rigorous data preprocessing, model evaluation using standard metrics, and clinical integration for real-world application. It aims to develop a machine learning (ML)-driven tool for bone cancer detection by utilizing a combination of deep learning (DL) models and optimization methods. It includes enhancing detection accuracy by integrating Cuckoo Search Modified Hill Climbing (CS-MHC) optimization with ResNet for improved image classification, optimizing model performance through CSO for better feature selection and faster convergence, comparing the CS-MHC ResNet model with traditional models like VGG-16, Inception, and Xception to improve accuracy, precision, and recall, creating a clinically applicable model for early bone cancer diagnosis and contributing to medical image analysis by combining hybrid optimization and deep learning techniques. The CNN will serve as the primary model for image classification, while Cuckoo Search Optimization will enhance feature selection and hyperparameter tuning.

RESULTS

CS-MHC ResNet demonstrated superior performance over other models in classification accuracy (above 90%), sensitivity (around 85%), precision (above 88%), and F-measure (approximately 86%). Other models (VGG-16, Xception, Inception) showed lower performance, indicating that the integration of CSO with ResNet enhances feature selection and improves the method's ability to classify bone cancer more effectively. These outcomes indicate that the proposed CS-MHC ResNet method offers significant improvements in the automated detection of bone cancer, supporting its potential for clinical use in diagnostic systems.

CONCLUSION

The CS-MHC ResNet model combines Cuckoo Search Optimization (CSO) with ResNet for automated bone cancer detection. The model outperformed traditional deep learning architectures like VGG-16, Xception, and Inception in accuracy, sensitivity, precision, and F-measure. Key findings include enhanced model performance, improved feature selection via CSO, and faster convergence. The CS-MHC ResNet model shows promise for clinical applications, offering a more efficient and reliable tool for bone cancer detection. Future research will concentrate on larger multi-center datasets and simpler designs to improve resilience and applicability.