Optimizing deep learning for bone cancer detection: Boosting diagnostic accuracy and efficiency with CNNs

Bone cancer and its metastasis pose significant challenges in clinical diagnostics, often leading to delayed or inaccurate diagnoses due to the subtle differences between benign and malignant lesions. These challenges are further exacerbated by the complex and varied manifestations of bone cancer, which can make traditional diagnostic methods less effective. Early and precise detection is crucial for improving patient outcomes, as it enables timely intervention and treatment. This paper explores the potential of deep learning and convolutional neural networks (CNNs), specifically ResNet101, to address these challenges. By leveraging advanced image analysis techniques, the proposed solution aims to significantly enhance diagnostic accuracy, offering a promising tool for distinguishing between benign and malignant bone lesions.The integration of advanced data preprocessing and augmentation techniques enhances the model's ability to learn from limited datasets, ensuring accurate lesion differentiation. Additionally, the focus on computational efficiency supports real-time application in clinical environments, enabling swift and reliable diagnostic support.