Abnormal Pulmonary Sounds Classification Algorithm using Convolutional Networks

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.

Factors Affecting Student Learning Achievement in Asian Universities During the Pandemic Period

“…In terms of shortcomings, students feel unfocused and difficult to understand the material provided by lecturers because of the many obstacles and obstacles experienced, for example at home, most students do homework such as helping their parents sweep, wash clothes, quota and internet packages, even students whose residence can be categorized as remote experience minimal signal interference in the area. (Alicia et al, 2021;Fakulti Kejuruteraan Elektrik, Universiti Teknikal Malaysia Melaka, 76100 Durian Tunggal, Melaka Malaysia et al, 2022). In terms of advantages, students feel the impact of the pandemic that they can always be close to their parents, prevent covid-19 transmission, facilitate the interaction process between lecturers and students in order to reduce the spread of the covid-19 pandemic.…”

Observational Quantitative Study of Healthy Lifestyles and Nutritional Status in Firefighters of the fifth Command of Callao, Ventanilla 2023

“…Approximately 52% of adults worldwide grapple with these issues [6]. In recent years, global cases of overweight and obesity have tripled, signaling a worrying trend linked to chronic diseases, including Diabetes Mellitus II, cardiovascular disorders, and musculoskeletal problems [6][7][8][9][10][11][12].…”