RT info:eu-repo/semantics/article
T1 Pneumonia classification from X-ray images with Inception-V3 and convolutional neural network
A1 Mujahid, Muhammad
A1 Rustam, Furqan
A1 Álvarez, Roberto
A1 Vidal Mazón, Juan Luis
A1 Torre Díez, Isabel de la
A1 Ashraf, Imran
K1 Pneumonia
K1 Neumonía
K1 Lungs - Diseases
K1 Pulmones - Enfermedades
K1 Chest X-ray
K1 Chest - Radiography
K1 Tórax - Radiografía
K1 Machine learning
K1 Aprendizaje automático
K1 Artificial intelligence
K1 3205.08 Enfermedades Pulmonares
K1 1203.04 Inteligencia Artificial
AB Pneumonia is one of the leading causes of death in both infants and elderly people, with approximately 4 million deaths each year. It may be a virus, bacterial, or fungal, depending on the contagious pathogen that damages the lung’s tiny air sacs (alveoli). Patients with underlying disorders such as asthma, a weakened immune system, hospitalized babies, and older persons on ventilators are all at risk, particularly if pneumonia is not detected early. Despite the existing approaches for its diagnosis, low accuracy and efficiency require further research for more accurate systems. This study is a similar endeavor for the detection of pneumonia by the use of X-ray images. The dataset is preprocessed to make it suitable for transfer learning tasks. Different pre-trained convolutional neural network (CNN) variants are utilized, including VGG16, Inception-v3, and ResNet50. Ensembles are made by incorporating CNN with Inception-V3, VGG-16, and ResNet50. Besides the common evaluation metrics, the performance of the pre-trained and ensemble deep learning models is measured with Cohen’s kappa as well as the area under the curve (AUC). Experimental results show that Inception-V3 with CNN attained the highest accuracy and recall score of 99.29% and 99.73%, respectively.
PB MDPI
SN 2075-4418
YR 2022
FD 2022
LK https://uvadoc.uva.es/handle/10324/61772
UL https://uvadoc.uva.es/handle/10324/61772
LA eng
NO Diagnostics, 2022, Vol. 12, Nº. 5, 1280
NO Producción Científica
DS UVaDOC
RD 07-ago-2024