Enhanced Tumor Detection Using Deep Learning on NVIDIA DGX Systems
S. Navaneethan, M Shanmuga Priya, S Saranya · 2024
Medical imaging has advanced significantly with recent advances in tumor detection using deep learning (DL) on NVIDIA DGX computers. Particularly in complex cases, traditional procedures that rely on manual radiologist analysis frequently suffer from inefficiencies and human mistakes. On the other hand, the proposed system makes use of convolutional neural networks (CNNs) on powerful NVIDIA DGX hardware to improve detection speed and accuracy. By preprocessing medical images to improve consistency and quality, the proposed system teaches CNNs on a variety of datasets to recognize tumor features on their own. Extensive training and validation optimize the CNN’s parameters, made possible by the powerful computational capabilities of the DGX system. When compared to existing systems, evaluation metrics show improved performance in terms of accuracy (92%), sensitivity (89%), specificity (94%), and area under the receiver operating characteristic curve (AUC-ROC) (95%). Faster diagnosis and treatment decisions are promised through implementation into clinical workflows, supported by real-time feedback to radiologists. Better patient outcomes in clinical practice are ensured by the proposed system, which not only expedites tumor identification procedures but also establishes new benchmarks for dependability and effectiveness in medical imaging diagnostics.