OsteoCancerNet: An Efficient and Fast Bone Cancer Diagnostic Model Combining EfficientNet B4 and SVM with RBF Kernel for X-ray Image Analysis

Bone cancer diagnosis is imperative for diagnosing and treating many forms of primary and metastatic bone cancers early. Traditional imaging techniques, such as CT, MRI, and X-ray scans, are effective but typically involve manual interpretation, which is laborious and prone to human mistake. With increased accuracy, dependability, and efficiency over conventional techniques, automated bone cancer diagnosis systems have been made possible by recent developments in machine learning (ML) and deep learning (DL). Even though a large portion of the recent literature has made significant strides in the diagnosis of bone cancer using deep learning techniques, many of these methods have numerous drawbacks, including computational complexity, overfitting in certain situations, and a lack of reliable databases. The objective of this research is to develop a method that can diagnose bone cancer as quickly, efficiently, and affordably as feasible. This method's main contribution is the combination of EfficientNetB4 and SVM algorithms, which improves accuracy, speed, and accessibility while making use of large datasets and reliable assessment measures. Combining EfficientNetB4 and SVM is crucial for diagnosing bone cancer because it harnesses the EfficientNetB4 technique's amazing capacity to extract useful features both quantitatively and qualitatively, as well as the SVM's significant advantage when it comes to binary separation. After a thorough analysis of numerous research, it was determined to combine these methods as they are distinguished by their great efficiency and simplicity in job implementation, especially in medical environments where accuracy and interpretability are of utmost importance The success of the suggested methods was shown by experiments on a large dataset (which contains 35244 X-ray images), which produced 98% precision, 97.47% recall, 98% accuracy, and 98% F1-score. The suggested method's better performance and computational economy are highlighted by comparison with machine learning, deep learning, and transfer learning techniques. Furthermore, the suggested system encounters a quick inference time of 41 ms, which qualifies it for clinical real-time applications. This study offers a viable strategy for early detection and better treatment outcomes by demonstrating the potential of integrating deep learning and traditional machine learning approaches for better bone cancer diagnosis.