Enhancing MRI image retrieval using autoencoder-based deep learning: A solution for efficient clinical and teaching applications

Magnetic resonance imaging (MRI) image retrieval holds significant value in clinical contexts and medical education due to the shortcomings of traditional methods: slow speed, low accuracy, and limited learning capabilities. Improving this retrieval process is crucial for enhancing medical diagnostics and educational outcomes. Efforts to overcome these challenges are paramount for advancing healthcare practices and educational methodologies. This study explores the use of autoencoders in deep learning to effectively retrieve MRI images from databases for medical education, emphasizing the model's capacity to be trained with a small amount of labeled data. This work intends to improve the MRI image retrieval process by utilizing autoencoders, demonstrating the promise of deep learning technologies in medical image analysis without the need for large labeled datasets for training. Research has demonstrated the exceptional advantages of this method for MRI image retrieval tasks, with an average accuracy of 99.09%. This indicates that the technique performs exceptionally well in this particular domain and is very effective and reliable in extracting MRI images. This innovative approach can improve the archival management and diagnostic functions of medical images, providing an efficient and reliable solution for MRI image retrieval. It not only helps doctors with clinical diagnosis and medical teaching and research more quickly but also suggests a convenient solution for file management related to medical images.