The advent of deep learning has revolutionized medical imaging, enhancing diagnostic precision, treatment planning, and patient care. This study leverages deep learning, specifically employing the VGG16 model optimized with RMSprop, to automate bone abnormality detection. Methodologically, the research encompasses data acquisition, preprocessing, and model training with RMSprop optimization. Results highlight the efficacy of this approach, showcasing RMSprop’s ability to detect various bone abnormalities. These findings underscore deep learning’s potential in medical imaging, emphasizing its applicability beyond bone abnormality detection. The study illuminates the transformative impact of RMSprop-optimized deep learning models in medical imaging, promising advancements in automated diagnosis and treatment planning.