Robust Medical Image Prediction via Adaptive Reconstruction: Bridging the Gap in Low-Quality Data

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. Framework advances medical image analysis through three novel contributions firstly, A hybrid architecture combining wavelet-based denoising with deep learning (DL) enhancement (unlike existing single-approach methods). Secondly, Cross-modality robustness validated on low-quality CT/MRI/X-rays from real clinics (versus modality-specific solutions), and lastly, A closed-loop system where diagnostic predictions guide iterative image refinement (absent in current workflows). Benchmarks show 98.5% accuracy at 0.6ms latency, with 19% fewer false positives than cascaded approaches. This reduces 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 DL-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. Additionally, the work focuses on utilising robust machine learning algorithms to improve 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 findings indicated that adaptive reconstruction improves visual quality of medical images and improves the overall predictive model performance for clinical use cases. The proposed adaptive reconstruction model also represents a promising strategy for overcoming constraints posed by low-quality data and will improve the accuracy and reliability evidencing clinically relevant outcomes in medical imaging.