The research project on 'Deep Learning-Based Text Summarization System using T5 small and gTTS' introduces a method to automatically extract and understand information from PDFs. The first step is to extract t...
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Coronary artery disease (CAD) is the primary cause of mortality and a key driver of healthcare expenses globally. Accurately segmenting stenotic regions from coronary angiograms is decisive in identifying and treating...
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ISBN:
(纸本)9798350389609
Coronary artery disease (CAD) is the primary cause of mortality and a key driver of healthcare expenses globally. Accurately segmenting stenotic regions from coronary angiograms is decisive in identifying and treating cardiovascular diseases. However, it is a challenging task for medical professionals to use X-ray Coronary Angiogram (XCA) due to the reduced signal quality, the existence of obstructive contextual elements, and various types of noise. Furthermore, handcrafted segmentation is arduous, laborious, and prone to inconsistencies and human errors. In this context, this research aim to develop an automatic stenosis segmentation system using a deep network. Initially, the input image is processed by Gaussian filters and the improved angiogram is filtered by Hessian-based Vessel Filtering (HVF) technique to increase the clarity of vascular components in the angiogram images. This study identifies the branch points (BP) in the angiograms based on the eigenvalues of the Hessian matrix. The proposed model employ a Mask Region-based Convolutional Neural Network (Mask R-CNN) to provide precise pixel-wise masks for every detected stenosis. The proposed Mask R-CNN includes (i) ResNet50 as the backbone network to extract significant attributes;(ii) Region Proposal Network (RPN) to identify possible Regions of Interest (RoIs) that may have stenosis;(iii) RoI Align to ensure precise alignment of the RoIs for improved mask prediction;and (iv) a mask branch to create a pixel-level segmentation mask for each RoI. The effectiveness of the model is assessed by applying an open-access ARCADE Phase 1 (Automatic Region-based CAD diagnostics using XCA images) dataset. The Mask R-CNN model achieves better results with 97.8% dice score, 92.9% sensitivity, and 96.6% specificity. Besides, it provides reduced standard deviation (SD) in the segmentation task with a 0.8% dice score, 1.0% sensitivity, and 1.0% specificity. These results shows that the Mask R-CNN model provides more relia
Lung cancer stands as a formidable and prevalent threat, necessitating urgent attention to early diagnosis and precise treatment to mitigate its high fatality rates. In this context, the utilization of computed tomogr...
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With the advent of the information age, data storage has not only developed from paper information systems to electronic information system storage, but has also extended to cloud database storage methods. To date, we...
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This study presents a new approach for administering point-of-view-based assessment exams. The purpose of our ranking is to gain a sense of the best and most hilariously bad works of art. Given a collection of free-te...
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This paper compares the time complexity of various sorting algorithms for the logic, code and time complexity of each algorithm. The sorting algorithms that this paper discusses are Selection sort, Bubble sort, Insert...
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In urban environments, efficient ambulance response times are critical for saving lives. This paper proposes a novel approach utilizing a multi-sensor integration system for improving ambulance control and traffic man...
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ISBN:
(纸本)9798350364828
In urban environments, efficient ambulance response times are critical for saving lives. This paper proposes a novel approach utilizing a multi-sensor integration system for improving ambulance control and traffic management. The system combines Radio Frequency Identification (RFID) sensors, cameras, and microphones to enhance the responsiveness of ambulance drivers and alleviate traffic congestion. The RFID sensors are strategically placed along the ambulance route to facilitate seamless communication between the ambulance and traffic signals. When an ambulance approaches, the RFID sensors trigger pre-programmed traffic signal adjustments, such as extending green lights or halting conflicting traffic flow, to expedite the ambulance's passage. Simultaneously, the camera-based detection system identifies the presence of ambulances in traffic and assesses congestion levels in real-time. Utilizing computer vision algorithms, the system analyzes live camera feeds to detect ambulance vehicles and evaluate traffic density and movement patterns. This information enables dynamic rerouting of ambulances to less congested routes, optimizing response times and minimizing delays. Furthermore, a microphone array is employed to detect the distinct audio signature of ambulance sirens. By leveraging sound analysis techniques, the system accurately identifies the approach of an ambulance and triggers additional traffic management measures, such as prioritizing ambulance lanes or temporarily rerouting vehicles to clear a path. Integration of these sensor technologies into a unified control system offers a comprehensive solution for improving ambulance navigation through urban traffic. Through proactive traffic signal adjustments, dynamic route optimization, and real-time siren detection, the proposed system enhances overall emergency response effectiveness while reducing the risk of traffic-related delays and accidents. Moreover, the system's adaptability and scalability make it suit
Human sentiments are analyzed based on two prominent technologies, electroencephalography and image processing. Electroencephalography helps in detecting the brains activity by attaching the electrodes to the scalp. T...
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The paper introduces the BioSentinel Neural Network (BSNN), a novel hybrid deep learning model designed to enhance malware detection, particularly focusing on zero-day threats. The BSNN model integrates diverse neural...
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Within the dynamic field of planetary defence, machine learning has emerged as a key component that is essential to early warning systems that are tasked with forecasting the orbits and trajectories of potentially dan...
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