Dr. Qinghe Zheng Profile

Dr. Qinghe Zheng

at Shandong Univ

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Publications (2)

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Proceedings Article | 20 June 2024 Presentation + Paper

Real-time stroke detection using deep learning and federated learning

Abdussalam Elhanashi, Pierpaolo Dini, Sergio Saponara, Qinghe Zheng, Ibrahim Alsharif

Proceedings Volume 13000, 130000L (2024) https://doi.org/10.1117/12.3012948

KEYWORDS: Object detection, Machine learning, Deep learning, Medicine, Data privacy, Artificial intelligence, Performance modeling, Medical imaging

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Stroke is a devastating and life-threatening medical condition that demands immediate intervention. Timely diagnosis and treatment are paramount in reducing mortality and mitigating long-term disabilities associated with stroke. This research aims to address these critical needs by proposing a real-time stroke detection system based on Deep Learning (DL) with the incorporation of Federated Learning (FL), which offers improved accuracy and privacy preservation. The purpose of this research is to develop an efficient and accurate model capable of distinguishing between stroke and non-stroke cases in real-time, assisting healthcare professionals in making rapid and informed decisions. Stroke detection has traditionally relied on manual interpretation of medical images, which is time-consuming and prone to human error. DL techniques have shown significant promise in automating this process, but the need for large and diverse datasets, as well as privacy concerns, remains challenging. To achieve this goal, our methodology involves training the DL model on extensive datasets containing both stroke and non-stroke medical images. This training process will enable the model to learn complex patterns and features associated with stroke, thereby improving its diagnostic accuracy. Furthermore, we will employ Federated Learning, a decentralized training approach, to enhance privacy while maintaining model performance. This approach allows the model to learn from data distributed across multiple healthcare institutions without sharing sensitive patient information. The proposed approach has been executed on NVIDIA platforms, taking advantage of their advanced GPU capabilities to enable real-time processing and analysis. This optimized model has the potential to revolutionize stroke diagnosis and patient care, ultimately saving lives and improving the quality of healthcare services in the field of neurology.

Proceedings Article | 7 June 2023 Presentation + Paper

An automated AI and video measurement techniques for monitoring social distancing, mask detection, and facial temperature screening for COVID-19

Abdussalam Elhanashi, Sergio Saponara, Qinghe Zheng

Proceedings Volume 12571, 125710D (2023) https://doi.org/10.1117/12.2663754

KEYWORDS: Object detection, Temperature metrology, Facial recognition systems, Video, COVID 19, Deep learning, Cameras

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COVID-19 is an infectious virus caused by acute respiratory syndrome SARS-CoV-2. It was first discovered in December 2019 in Wuhan, China. This ongoing pandemic caused infected cases, including many deaths around the world. Coronavirus is spread mainly by air droplets near the infected person due to sneezing, coughing, and talking. Pretrained DL models utilize large CNN layers, which require more disk size on IoTembedded devices and affect real-time detection. This research presents an integrated lightweight DL approach for real-time and multi-task (social distancing, mask detection, and facial temperature) video measurement to control the spread of coronavirus among individuals. The three tasks have used the most recent YOLO detectors (YOLOv7-tiny). It is an object detection model optimized based on the original YOLOv7 to simply the neural network architecture. The trained models have been evaluated in terms of mean average precision, Recall, and Precision to assess the algorithm performance. The proposed approach has been deployed and executed on NVIDIA devices (Jetson nano, Jetson Xavier AGX) composed of visible and thermal cameras. The visible camera is used for face mask detection, while the thermal camera is used for facial temperature measurement and social distancing. This research enriched the prevention system of COVID-19 by the integrated approach compared to the state-of-the-art methodologies. In addition, we obtained promising results for real-time detection. The proposed approach is suitable for a surveillance system to monitor social distancing, Face mask detection, and measuring the facial temperature among individuals.

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