A5014219138- COVID-19 diagnosis using AI
- Radiomics and Machine Learning in Medical Imaging
- Autonomous Vehicle Technology and Safety
- Image Retrieval and Classification Techniques
- Big Data and Business Intelligence
- Sports Analytics and Performance
- Advanced Neural Network Applications
- Lung Cancer Diagnosis and Treatment
- Anomaly Detection Techniques and Applications
- AI in cancer detection
In the aviation sector, ensuring safe landings while prioritizing safety of runways is crucial to prevent accidents and incidents during landing phase flights. However, many studies analyzing unsafe events, such as runway cracks or inadequate friction, often fail quantify their impacts on flight landing. airport pavement management systems (APMS), condition surface a critical factor in operational aircraft take-off Therefore, it essential provide pilots with reports conditions, including...
This article describes a novel ensemble approach to achieve high quality segmentation of Chest X-Ray (CXR) images. Specifically, the Japan Society Radiological Technology (JSRT) dataset consisting 247 CXR images has been used study effectiveness this approach. The carried out in three phases: preprocessing, segmentation, and validation by classification. novelty comes from combining Denoising Autoencoder with Contrast Limited Adaptive Histogram Equalization (CLAHE) for preprocessing; an...
Abstract Automatic image segmentation and classification of medical images plays significant role in detection diagnosis various pathological process. Normally chest radiography is a basic representation to find many abnormalities present the chest. Radiology services delayed due proper detection, diseases. improved both radiological In recent days deep learning with CNN methods provides remarkable successes time limit minimum cost. The proposed method handles for automatic lung x-ray as...
This paper investigates the application of several deep learning architectures such as VGG-16, VGG-19, ResNet-50, and Xception Net for lung chest X-ray images, with 5, 10, 15 epochs, different optimizers Adam, SGD, RMSProp, a rate 0.0001. The investigation finds that when adaptive gradient is used, VCG-16 architecture achieves 68% accuracy; VCG-19 67% ResNet-50 98.67% less than 50% accuracy. With further experimentation using epochs 100% accuracy was achieved architectures. However, has been...