Addressing issues related to multi-label classification is relevant in many fields of
applications. In this work. We present a multi-label classification architecture based on Multi …

In this study, multiple lung diseases are diagnosed with the help of the Neural Network
algorithm. Specifically, Emphysema, Infiltration, Mass, Pleural Thickening, Pneumonia …

Transfer learning from natural images to medical images has been established as one of the
most practical paradigms in deep learning for medical image analysis. To fit this paradigm …

Deep learning has demonstrated remarkable performance in the medical domain, with
accuracy that rivals or even exceeds that of human experts. However, it has a significant …

Abstract Vision Transformer (ViT) has become one of the most popular neural architectures
due to its simplicity, scalability, and compelling performance in multiple vision tasks …

Background and objectives The traditional method of detecting COVID-19 disease mainly
rely on the interpretation of computer tomography (CT) or X-ray images (X-ray) by doctors or …

COVID-19, pneumonia, and tuberculosis have had a significant effect on recent global
health. Since 2019, COVID-19 has been a major factor underlying the increase in respiratory …

Automated diagnosis using deep neural networks in chest radiography can help radiologists
detect life-threatening diseases. However, existing methods only provide predictions without …

Hyperspectral imaging technology (HSI) can capture pathological tissue's spatial and
spectral information simultaneously, with wide coverage and high accuracy characteristics …

Multi-label classification algorithms based on supervised learning use all the labeled data to
train classifiers. However, in real life, many of the data are unlabeled, and it is costly to label …