| Abstrak/Abstract |
Medical imaging techniques play a pivotal role
in disease management and monitoring. Image scans offer a
magnified lens into the intricate workings of human body
parts with clear, precise information, and fast image acqui-
sition. In particular, chest imaging reveals lung conditions,
including COVID-19 and tuberculosis. However, even skilled
radiologists may find it challenging to evaluate minor variations
in the amount and nature of lung abnormalities. Artificial
intelligence (AI) emerges as a promising solution. AI can
support conventional medical imaging equipment by providing
computational power to process images more quickly and
accurately. Despite this potential, comprehensive studies on
AI’s benefits in medical imaging remain scarce, especially for
COVID-19 and tuberculosis. These conditions share structural
similarities in their radiological patterns, emphasizing the need
for targeted research. To address this research gap, this review
paper provides an AI-powered method of tracking, diagnosis,
and prognosis of COVID-19 and tuberculosis using different
types of medical imaging scans. Several models, including deep
learning architectures and convolutional neural network (CNN),
are examined in this comprehensive review. The analysis demon-
strates how well they classify lung ailments; certain models
have accuracy rates as high as 98.80% accuracy for TB and
98.31% for COVID-19. However, there are still issues, namely
the improvement of AI transparency and its incorporation into
clinical practice. Reducing diagnostic errors and enabling faster
treatment are two ways in which addressing these concerns
could improve patient care. |
