Publicación:
Abnormal Pulmonary Sounds Classification Algorithm using Convolutional Networks
Abnormal Pulmonary Sounds Classification Algorithm using Convolutional Networks
| dc.contributor.author | Alicia A.M. | es_PE |
| dc.contributor.author | Alexander A.-G. | es_PE |
| dc.contributor.author | Sebastian R.-C. | es_PE |
| dc.contributor.author | William C.F. | es_PE |
| dc.contributor.author | Michael C.-T. | es_PE |
| dc.contributor.author | Víctor H.-A. | es_PE |
| dc.date.accessioned | 2024-05-30T23:13:38Z | |
| dc.date.available | 2024-05-30T23:13:38Z | |
| dc.date.issued | 2021 | |
| dc.description | We want to thank the Image Processing Research Laboratory. (INTI-Lab) and the Universidad de Ciencias y Humanidades. (UCH) for their support in this research, the National Fund for. Scientific, Technological and Technological Innovation (FONDECYT), according to the research: ?SAMAYCOV: ?Desarrollo de un dispositivo electr?nico port?til a bajo costo para evaluar riesgo de neumon?a basado en sonido pulmonar anormal en pacientes con sospecha de COVID-19 en zonas vulnerables?. CONVENIO 054-2020-FONDECYT?; for the financing of this research and the Electronics Laboratory of the UCH for assigning us their facilities and being able to carry out the respective tests. | |
| dc.description.abstract | In the world and in Peru, Acute Respiratory Infections are the main cause of death, especially in the most vulnerable population, children under 5 years of age and older adults. Pneumonia is the leading cause of death of children in the world. 60.2% of pneumonia cases affect children under 5 years of age. Thus, prevention and timely treatment of lung diseases are crucial to reduce infant mortality in Peru. Among the main problems associated with this high is percentage the lack of medical professionals and resources, especially in remote areas, such as Puno, Huancavelica and Arequipa, which experience temperatures as low as -20°C during the cold season. This study develops an algorithm based on computational neural networks to differentiate between normal and abnormal lung sounds. The initial base of 917 sounds was used, through a process of data augmentation, this base was increased to 8253 sounds in total, and this process was carried out due to the need of a large number of data for the use of computational neural networks. From each signal, features were extracted using three methods: MFCC, Melspectogram and STFT. Three models were generated, the first one to classify normal and abnormal, which obtained a training Accuracy of 1 and a testing accuracy of 0.998. The second one classifies normal sound, pneumonia and other abnormalities and obtained training Accuracy values of 0.9959 and a testing accuracy of 0.9885. Finally, we classified by specific ailment where we obtained a training Accuracy of 0.9967 and a testing accuracy of 0.9909. This research provides interesting findings about the diagnosis and classification of lung sounds automatically using convolutional neural networks, which is the beginning for the development of a platform to assess the risk of pneumonia in the first moment, thus allowing rapid care and referral that seeks to reduce mortality associated mainly with pneumonia. © 2021 | |
| dc.description.sponsorship | Consejo Nacional de Ciencia, Tecnología e Innovación Tecnológica - Concytec | |
| dc.identifier.doi | https://doi.org/10.14569/IJACSA.2021.0120645 | |
| dc.identifier.scopus | 2-s2.0-85109194641 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12390/2963 | |
| dc.language.iso | eng | |
| dc.publisher | Science and Information Organization | |
| dc.relation.ispartof | International Journal of Advanced Computer Science and Applications | |
| dc.rights | info:eu-repo/semantics/openAccess | |
| dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/4.0/ | |
| dc.subject | pneumonia | |
| dc.subject | Algorithm | es_PE |
| dc.subject | classification | es_PE |
| dc.subject | computational neural networks | es_PE |
| dc.subject | lung sounds | es_PE |
| dc.subject | mortality | es_PE |
| dc.subject.ocde | https://purl.org/pe-repo/ocde/ford#3.02.28 | |
| dc.title | Abnormal Pulmonary Sounds Classification Algorithm using Convolutional Networks | |
| dc.type | info:eu-repo/semantics/article | |
| dspace.entity.type | Publication |