Dldss -121 New!

DLDSS‑121: A Deep‑Learning‑Driven Decision Support System for Real‑Time Medical Imaging Diagnosis

Figure 1 depicts the overall pipeline of DLDSS‑121. The system consists of four interconnected modules: dldss -121

| Configuration | AUC (ChestX) | DSC (BraTS) | Latency | |---|---|---|---| | Full DLDSS‑121 | 0.96 | 0.91 | 0.85 s | | – KG Reasoner | 0.93 | 0.88 | 0.80 s | | – Uncertainty (MC‑Dropout) | 0.95 | 0.90 | 0.78 s | | – Both | 0.92 | 0.85 | 0.73 s | and regulatory considerations

Once I have a clearer picture of the scope and context, I can: (2) differential diagnosis ranking

Your Name , Affiliation – Department of Computer Science, University Co‑author , Affiliation – Department of Radiology, Hospital

Keywords: deep learning, decision support system, medical imaging, explainable AI, radiology workflow, DLDSS‑121.

The growing demand for rapid, accurate interpretation of medical images has motivated the development of intelligent decision‑support tools that can assist radiologists in real‑time clinical workflows. In this paper we present (Deep‑Learning‑Driven Decision Support System version 121), a modular, end‑to‑end platform that integrates state‑of‑the‑art convolutional neural networks (CNNs) with a knowledge‑graph‑based reasoning engine. DLDSS‑121 is designed to operate on multi‑modal imaging data (CT, MRI, and X‑ray) and to provide three core functionalities: (1) lesion detection and segmentation, (2) differential diagnosis ranking, and (3) confidence‑aware visual explanations. We evaluate the system on three publicly available benchmarks—NIH ChestX‑ray14, LUNA16 (lung nodule detection), and the BraTS 2021 brain‑tumor segmentation dataset—achieving performance on par with or exceeding current state‑of‑the‑art models while maintaining an average inference latency of 0.85 s per study on a single NVIDIA A100 GPU. A prospective reader‑study involving 12 board‑certified radiologists demonstrates that DLDSS‑121 improves diagnostic accuracy by 6.3 % and reduces reading time by 22 % when used as a second reader. We discuss system architecture, training strategies, explainability methods, and regulatory considerations, and we release the code and pretrained weights under an open‑source license to foster reproducibility.