Abstract
According to theWorld Health Organization, Pneumoconiosis
affects millions of workers globally,
with an estimated 260,000 deaths annually. The
burden of Pneumoconiosis is particularly high in
low-income countries, where occupational safety
standards are often inadequate, and the prevalence
of the disease is increasing rapidly. The reduced
availability of expert medical care in rural areas,
where these diseases are more prevalent, further
adds to the delayed screening and unfavourable outcomes
of the disease. This paper aims to highlight
the urgent need for early screening and detection
of Pneumoconiosis, given its significant impact on
affected individuals, their families, and societies as
a whole. With the help of low-cost machine learning
models, early screening, detection, and prevention
of Pneumoconiosis can help reduce healthcare
costs, particularly in low-income countries. In this
direction, this research focuses on designing AI solutions
for detecting different kinds of Pneumoconiosis
from chest X-ray data. This will contribute
to the Sustainable Development Goal 3 of ensuring
healthy lives and promoting well-being for all at all
ages, and present the framework for data collection
and algorithm for detecting Pneumoconiosis
for early screening. The baseline results show that
the existing algorithms are unable to address this
challenge. Therefore, it is our assertion that this
research will improve state-of-the-art algorithms of
segmentation, semantic segmentation, and classification
not only for this disease but in general medical
image analysis literature.
Keywords
- AI for Good - Projects: Computer Vision
- AI for Good - Projects: AI Ethics, Trust, Fairness
- AI for Good - Projects: Humans and AI
- AI for Good - Projects: Machine Learning
- AI for Good - Projects: Multidisciplinary Topics and Applications
