Evaluation Of 3dcnn Architecture For Classification Of Lung Diseases. - Dhivya, T.Evangeline; R.Balasubramanian, R.Balasubramanian (Journal of Neonatal Surgery, 2025)
Globally, infectious disease-related illnesses have long been a problem. Lung Cancer, COVID-19 and pneumonia (bacterial and viral pneumonia), all impact the lungs and result in millions of fatalities annually. In every situation, opportunities for improved care can be created by early identification and diagnosis. Computerized Tomography (CT) is utilized, nevertheless, to identify lung disease and identify symptoms. Even for doctors, using various imaging modalities is a challenging and time-consuming activity that is subject to varying opinions from different observers. Thus, there is considerable interest in creating algorithms that can automatically distinguish between people with lung disease and those who are healthy. Our research compares different 3DCNN architecture to accurately detect a subset of lung disorders using patient respiratory images, with the goal of improving the diagnostic accuracy of respiratory ailments. We demonstrated that 3D-VoxResNet produces superior outcomes when compared to other frameworks like 3D-VGG16,3D-AlexNet,3D-GoogleNet..