TL;DR
A novel quantum-inspired diagnostic model is developed for the identification of COVID-19 cases from CXR images that leverages quantum computing principles, such as superposition, entanglement, and interference, alongside established machine-learning methodologies to improve classification effectiveness and computational performance.
This paper presents a tri-class classification framework for distinguishing COVID-19, viral pneumonia, and normal cases from chest X-rays, built on a pre-trained ResNet50 and incorporates CBAM attention modules at two well-justified stages.
A deep learning-based system that uses chest X-ray pictures to automatically detect tuberculosis, using transfer learning using MobileNetV2 and DenseNet architectures to classify chest Xrays as either TB-positive or Healthy, reaching notable accuracy.
A robust deep learning framework that integrates a convolutional self-attention network, gamma correction for image enhancement, and a voting-based ensemble classifier to improving diagnostic performance is proposed, suggesting that the proposed framework is supporting automated COVID-19 diagnosis in real-world clinica...
The novel coronavirus disease (COVID-19) was initially identified in Wuhan, China, in December 2019 and subsequently had a profound global impact due to its rapid spread. Earlier symptoms and conditions of this deadly virus share common characteristics with the common cold and influenza, making the diagnosis difficult...
The diversity of biological data being generated today is increasing. Among all the data being generated, the coronavirus disease (COVID-19) outbreak has underscored the importance of rapid, reliable diagnostic techniques. In this context, automatic disease diagnosis using deep learning algorithms with medical image da...
The experiment revealed that preprocessing and feature extraction do not operate independently; Gaussian filtering actively enhanced the gradient signal that HOG depends on, while grayscale conversion preserved the intensity patterns that LBP encodes, demonstrating that alignment between preprocessing and feature descr...
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