Machine learning and machine learned prediction in chest X-ray images
This work addresses medical diagnosis from X-rays, but it is incremental as it applies existing methods to a specific dataset without major innovations.
The study tackled the problem of predicting patient ailments from chest X-ray images using machine learning, achieving strong performance with both a baseline CNN and DenseNet-121 in binary classification, as evidenced by gradient-weighted class activation mapping showing DenseNet-121's better focus on essential image parts.
Machine learning and artificial intelligence are fast-growing fields of research in which data is used to train algorithms, learn patterns, and make predictions. This approach helps to solve seemingly intricate problems with significant accuracy without explicit programming by recognizing complex relationships in data. Taking an example of 5824 chest X-ray images, we implement two machine learning algorithms, namely, a baseline convolutional neural network (CNN) and a DenseNet-121, and present our analysis in making machine-learned predictions in predicting patients with ailments. Both baseline CNN and DenseNet-121 perform very well in the binary classification problem presented in this work. Gradient-weighted class activation mapping shows that DenseNet-121 correctly focuses on essential parts of the input chest X-ray images in its decision-making more than the baseline CNN.