Topaloglu, IhsanBarua, Prabal DattaYildiz, Arif MetehanKeles, TugceDogan, SengulBaygin, MehmetAcharya, U. Rajendra2026-03-262026-03-2620230952-19761873-676910.1016/j.engappai.2023.1068872-s2.0-85170405880https://doi.org/10.1016/j.engappai.2023.106887https://hdl.handle.net/20.500.14901/2898Tan, Ru San/0000-0003-2086-6517; Yıldız, Arif Metehan/0000-0003-0451-8600This study proposes an accurate asthma detection model using an attention network and machine learning technique. The objective of this study is the automated detection of asthma using an attention network. The lung sounds from 203 subjects involving 767 segments from asthma and 722 segments from healthy subjects were collected using a stethoscope. A novel Attention ResNet18-based deep feature engineering model has been developed in five phases: preprocessing, training the Attention ResNet18 network, extracting deep features, iterative feature selection, and classification using k-nearest neighbor (kNN) or support vector machine (SVM). Gradient-weighted class activation mapping (Grad-CAM) was used to generate heat maps, effectively distinguishing asthma lung sounds from those of normal individuals. By using Grad-CAM, explainable results have been presented. Our proposed model obtained an accuracy of 99.73% using SVM with 10-fold cross-validation, surpassing the performance obtained by previous models. Hence, the developed model has the potential to detect asthma in the real-world scenario. scenario to detect.eninfo:eu-repo/semantics/closedAccessDeep Feature ExtractionAttention MechanismLung Sound ClassificationAsthma DetectionFeature SelectionArticle