eprintid: 2119 rev_number: 9 eprint_status: archive userid: 2 dir: disk0/00/00/21/19 datestamp: 2022-05-31 17:58:11 lastmod: 2023-07-17 23:30:26 status_changed: 2022-05-31 17:58:11 type: article metadata_visibility: show creators_name: Mujahid, Muhammad creators_name: Rustam, Furqan creators_name: Álvarez, Roberto Marcelo creators_name: Vidal Mazón, Juan Luis creators_name: Díez, Isabel de la Torre creators_name: Ashraf, Imran creators_id: creators_id: creators_id: roberto.alvarez@uneatlantico.es creators_id: juanluis.vidal@uneatlantico.es creators_id: creators_id: title: Pneumonia Classification from X-ray Images with Inception-V3 and Convolutional Neural Network ispublished: pub subjects: uneat_eng divisions: uneatlantico_produccion_cientifica divisions: uninimx_produccion_cientifica divisions: unic_produccion_cientifica full_text_status: public keywords: pneumonia; chest X-ray; ensemble learning; deep learning abstract: 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 date: 2022-05 date_type: published publication: Diagnostics volume: 12 number: 5 pagerange: 1280 id_number: doi:10.3390/diagnostics12051280 refereed: TRUE issn: 2075-4418 official_url: http://doi.org/10.3390/diagnostics12051280 access: open language: en citation: Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica Universidad Internacional Iberoamericana México > Investigación > Producción Científica Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés 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 metadata Mujahid, Muhammad; Rustam, Furqan; Álvarez, Roberto Marcelo; Vidal Mazón, Juan Luis; Díez, Isabel de la Torre y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, roberto.alvarez@uneatlantico.es, juanluis.vidal@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR (2022) Pneumonia Classification from X-ray Images with Inception-V3 and Convolutional Neural Network. Diagnostics, 12 (5). p. 1280. ISSN 2075-4418 document_url: http://repositorio.uneatlantico.es/id/eprint/2119/1/diagnostics-12-01280-v2.pdf