Deep Learning-Based Bone Cancer Detection and Classification from X-Ray Images Using the Owl Search Algorithm

Bone cancer is a serious health condition that often leads to high mortality rates. Early detection plays a crucial role in limiting the spread of cancerous cells and improving patient outcomes. However, manual detection is a time-consuming and labor-intensive process, necessitating th6+e development of an automated system to efficiently classify and identify cancerous and healthy bone tissues. This paper introduces the Owl Search Algorithm with Deep Learning-Driven Bone Cancer Detection and Classification (OSADL-BCDC) model, which combines transfer learning and hyperparameter optimization for effective bone cancer detection. The OSADL-BCDC approach utilizes the Inception v3 model as a pretrained feature extractor, eliminating the need for manual image segmentation. The Owl Search Algorithm (OSA) is employed as a hyperparameter optimizer to enhance the performance of Inception v3. Additionally, Long Short-Term Memory (LSTM) networks are leveraged to detect the presence of bone cancer. The proposed OSADL-BCDC method reduces diagnosis time and achieves faster convergence, demonstrating superior performance in experimental evaluations compared to existing algorithms. The results of the comparison emphasize the effectiveness and improved accuracy of the OSADL-BCDC model for bone cancer detection.