Because the application of data augmentation and regularization techniques, the training and validation loss curves of MobileNetV2 con-verged effectively, which in turn suppressed the issue of overfitting, confirming the superiority of MobileNetV2 in end-to-end feature extraction for COVID-19 imaging.
The proposed deep learning models provide an efficient and accurate tool for multiclass pneumonia detection from CXR images and have the potential to support healthcare professionals in making more accurate diagnoses.
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 hybrid ensemble learning approach to classify chest X-ray images into four classes—Normal, COVID-19, Pneumonia, and Tuberculosis exhibited high sensitivity in detecting Tuberculosis with considerable stability in classifying Normal, COVID-19, and Pneumonia.
Pneumonia is a respiratory condition, which is critical and needs an accurate diagnosis to minimize death. The following paper introduces a deep learning model, which can be explained, to identify pneumonia in the X-ray images of the chest, using the Convolutional Neural Networks (CNN) architecture. The image resizing,...
This paper proposes ResKAN18, a hybrid structure that embeds the learnable spline function of the Kolmogorov–Arnold network (KAN) into the ResNet18 classification head for intelligent diagnosis of COVID-19 in chest X-ray images.
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