Medical image prediction plays a very significant role in clinical decision-making and early detection and diagnosis of different diseases. However, the quality of medical images has a huge impact on the predictive models' accuracy. Poor-quality data usually occurs due to problems like noise, artifacts, and low resolution and poses a major challenge for reliable medical image prediction. This study develops a new framework of robust medical image prediction by exploiting adaptive reconstruction techniques that reduce the gap in low-quality data. Our method combines state-of-the-art image processing methods with machine learning algorithms to enhance the quality of medical images before feeding them into predictive models. The adaptive reconstruction-based model consists of using classic denoising techniques in images and deep learning-based approaches, selectively enhancing critical features and removing noise. It aims to provide qualities in image reconstruction suitable for prediction tasks by recovering lost or degraded information. In addition to this, the work also focuses on the use of robust machine learning algorithms to enhance prediction accuracy on the reconstructed images. The framework was tested on various datasets and had significant improvements in predictive performance when compared to the traditional approaches using low-quality images directly. The results showed that adaptive reconstruction not only boosts the visual quality of medical images but also promotes the overall predictive model performance for clinical applications. This paper provided a promising approach to overcoming such limitations from data of low quality, which will promote more accurate and reliable predictions toward clinically relevant outcomes in medical imaging.
