# A Deep Learning Framework for Real-Time Image Processing in Medical Diagnostics: Enhancing Accuracy and Speed in Clinical Applications

arXiv: https://arxiv.org/abs/2510.16611 · HTML: https://mlsanity.com/paper/2510.16611/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 36 references that are in the map.
- First posted on arXiv 18 Oct 2025.

## Builds on (most cited first, 7 of 7)

- [MultiResUNet : Rethinking the U-Net Architecture for Multimodal Biomedical Image Segmentation](https://mlsanity.com/paper/1902.04049/) (2019)
- [LV-UNet: A Lightweight and Vanilla Model for Medical Image Segmentation](https://mlsanity.com/paper/2408.16886/) (2024)
- [CACTUS: An Open Dataset and Framework for Automated Cardiac Assessment and Classification of Ultrasound Images Using Deep Transfer Learning](https://mlsanity.com/paper/2503.05604/) (2025)
- [Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders](https://mlsanity.com/paper/2504.14122/) (2025)
- [GenXSS: an AI-Driven Framework for Automated Detection of XSS Attacks in WAFs](https://mlsanity.com/paper/2504.08176/) (2025)
- [Fusion-Based Brain Tumor Classification Using Deep Learning and Explainable AI, and Rule-Based Reasoning](https://mlsanity.com/paper/2508.06891/) (2025)
- [Improved design of an active landing gear for a passenger aircraft using multi-objective optimization technique](https://mlsanity.com/paper/2506.22870/) (2025)

## Built on by (most cited first, 1 of 1)

- [Robust Pre-Training of Medical Vision-and-Language Models with Domain-Invariant Multi-Modal Masked Reconstruction](https://mlsanity.com/paper/2602.17689/) (2026)

## Abstract (arXiv)

Medical imaging plays a vital role in modern diagnostics; however, interpreting high-resolution radiological data remains time-consuming and susceptible to variability among clinicians. Traditional image processing techniques often lack the precision, robustness, and speed required for real-time clinical use. To overcome these limitations, this paper introduces a deep learning framework for real-time medical image analysis designed to enhance diagnostic accuracy and computational efficiency across multiple imaging modalities, including X-ray, CT, and MRI. The proposed system integrates advanced neural network architectures such as U-Net, EfficientNet, and Transformer-based models with real-time optimization strategies including model pruning, quantization, and GPU acceleration. The framework enables flexible deployment on edge devices, local servers, and cloud infrastructures, ensuring seamless interoperability with clinical systems such as PACS and EHR. Experimental evaluations on public benchmark datasets demonstrate state-of-the-art performance, achieving classification accuracies above 92%, segmentation Dice scores exceeding 91%, and inference times below 80 milliseconds. Furthermore, visual explanation tools such as Grad-CAM and segmentation overlays enhance transparency and clinical interpretability. These results indicate that the proposed framework can substantially accelerate diagnostic workflows, reduce clinician workload, and support trustworthy AI integration in time-critical healthcare environments.