This study explores the efficacy of various deep learning models for accurate classification of osteosarcoma from bone histopathology images. Leveraging state-of-the-art architectures including ResNet101, VGG16, VGG19, DenseNet201, and Xception, the research investigates their performance in detecting and diagnosing osteosarcoma based on the distinct patterns present in bone images. Through rigorous experimentation and evaluation, our findings demonstrate promising results in leveraging deep learning techniques for automated diagnosis. Notably, the Xception model emerges as particularly effective, achieving an impressive accuracy of 98.5%, surpassing previous approaches. This highlights the potential of advanced neural network architectures in improving diagnostic accuracy and efficiency for osteosarcoma detection. Furthermore, the study underscores the importance of continuous exploration and adoption of cutting-edge methodologies to enhance medical image analysis and facilitate early detection and treatment of debilitating diseases like osteosarcoma.
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