This study presents a deep learning model for efficient intracranial hemorrhage(ICH)detection and subtype classification on non-contrast head computed tomography(CT)*** refers to bleeding in the skull,leading to the m...
详细信息
This study presents a deep learning model for efficient intracranial hemorrhage(ICH)detection and subtype classification on non-contrast head computed tomography(CT)*** refers to bleeding in the skull,leading to the most critical life-threatening health condition requiring rapid and accurate *** is classified as intra-axial hemorrhage(intraventricular,intraparenchymal)and extra-axial hemorrhage(subdural,epidural,subarachnoid)based on the bleeding location inside the *** computer-aided diagnoses(CAD)-based schemes have been proposed for ICH detection and classification at both slice and scan ***,these approaches performonly binary classification and suffer from a large number of parameters,which increase storage ***,the accuracy of brain hemorrhage detection in existing models is significantly low for medically critical *** overcome these problems,a fast and efficient system for the automatic detection of ICH is *** designed a double-branch model based on xception architecture that extracts spatial and instant features,concatenates them,and creates the 3D spatial context(common feature vectors)fed to a decision tree classifier for final *** data employed for the experimentation was gathered during the 2019 Radiologist Society of North America(RSNA)brain hemorrhage detection *** model outperformed benchmark models and achieved better accuracy in intraventricular(99.49%),subarachnoid(99.49%),intraparenchymal(99.10%),and subdural(98.09%)categories,thereby justifying the performance of the proposed double-branch xception architecture for ICH detection and classification.
The convergence of vehicular networks and the Meta-verse, referred to as the Vehicular Edge Metaverse is set to revolutionize both transportation and in-car experiences. This paradigm shift envisions vehicles not only...
详细信息
Taint tracking in web browsers is a problem of profound interest because it allows developers to accurately understand the flow of sensitive data across JavaScript (JS) functions. Modern websites load JS functions fro...
详细信息
This study takes on the challenge of growing urban waste and presents a ground-breaking waste management approach. By merging Robotics and Artificial Intelligence (AI) with traditional Dirty Materials Recovery Facilit...
详细信息
With the rapid growth of wireless medical sensor networks (WMSNs) based healthcare applications, protecting both the privacy and security from illegitimate users, are major concern issues since patient’s precise info...
详细信息
Over one billion people worldwide are affected with neurological disorders and their economic impact is approximately $800 billion annually, which constitutes major medical challenge. Using neuromodulation systems cur...
详细信息
ISBN:
(纸本)9798331543617
Over one billion people worldwide are affected with neurological disorders and their economic impact is approximately $800 billion annually, which constitutes major medical challenge. Using neuromodulation systems currently available suffers from sensitivity, reaction time as well as energy consumption. The proposal in this research is to address these major issues in closed loop neuromodulation by using a Quantum enhanced Spiking Neural Network (QESNN) architecture. This paper represents the interfacing of two major fields: quantum sensing and neuromorphic computing. The QESNN architecture comprises three core components: This is implemented as an array of quantum sensors, a quantum classical hybrid interface, and a spiking neural network (SNN). Taking advantage of quantum superposition and entanglement principles, the quantum sensor array noninvasively images neural activity at the level of single action potentials using NV centers. These sensors work at ambient temperatures, which is unlike superconducting devices. For processing with neuromorphic processing, quantum-classical hybrid converts quantum sensor data into classical signals with advanced signal process such as quantum state estimation and noise reduction. By modeling biological neurons with leaky integrate and fire neurons, the SNN serves as a low power, timed neural dynamics modulation component that emulates biological event driven behavior. A key innovation in our architecture is adaptive thresholding, which dynamically adjusts detection thresholds based on signal distributions, improving sensitivity and reducing false positives by 45.6%. The system also achieves 20-30% higher power efficiency through techniques like adaptive sensor frequency control and low-power processing. Simulation results that show how the QESNN performs better than classical systems with less false positives and greater energy efficiency are presented. A new platform is demonstrated that integrates quantum sensing with neurom
Accurate pollutant forecasting serves as a crucial component in air quality monitoring and control in a smart city. Traditional pollutant forecasting models such as statistical, and machine learning models follow a si...
详细信息
Accurate segmentation of skin lesion areas is significant for the diagnosis and analysis of skin diseases. Due to the irregular and blurred boundaries between healthy skin and lesion areas and sometimes the interferen...
详细信息
For the field of fiber optic prefabricated rod manufacturing, raw material evaporation tank pressure control system of nonlinearity, hysteresis and other issues, this paper proposes to combine PID control algorithm an...
详细信息
The escalating prevalence of heart diseases necessitates innovative methods for early detection. This research introduced the Heart Disease Fast Fourier Transform Prediction (HDFFTP) approach, a novel method combining...
详细信息
暂无评论