Author: Hoesu Chun
Chest radiography is a commonly employed method for diagnosing respiratory ailments, such as COVID-19 and tuberculosis. Beyond providing an initial assessment of various respiratory conditions, chest X-rays are relatively inexpensive and widely accessible. Nonetheless, despite recent technological advancements, medical professionals must still interpret X-ray images to determine patients' health status. While such procedures may yield accurate diagnoses for experienced practitioners, it requires additional time and effort. Our team contends that machine learning algorithms, particularly those within the subfield of deep learning, offer a promising avenue for improving diagnostic accuracy in chest illnesses and for expediting such procedures.
Recent advancements in medical diagnosis have favored deep learning due to its potential to detect various diseases from medical images. Specifically, convolutional neural networks (CNNs) offer significant promise for this particular task since they can learn patterns and features in images that might prove difficult for human interpretation. Our project aims to predict various respiratory ailments, including COVID-19 and tuberculosis, using deep learning techniques on chest X-ray images. Finally, our team developed this web application where users can upload their chest X-ray images for analysis, expanding the reach of diagnostic capabilities beyond solely medical professionals.
Our team trained a deep learning model based on x-ray image datasets from Kaggle. Please refer to the below links for details. You may download the test datasets below.