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- MakaleAçık Erişim2026Scopus SJRQ2Deep Learning-Based Classification of Common Lung Sounds via Auto-Detected Respiratory Cycles
Engin, Mustafa Alptekin; Uzun Arslan, Rukiye; Senyer Yapici, İrem; Aras, Selim; Gangal, Ali
Chronic respiratory diseases, the third leading cause of mortality on a global scale, can be diagnosed at an early stage through non-invasive auscultation. However, effective manual differentiation of lung sounds (LSs) requires not only sharp auditory skills but also significant clinical experience. With technological advancements, artificial intelligence (AI) has demonstrated the capability to distinguish LSs with accuracy comparable to or surpassing that of human experts. This study broadly compares the methods used in AI-based LSs classification. Firstly, respiratory cycles—consisting of inhalation and exhalation parts in LSs of different lengths depending on individual variability, obtained and labelled under expert guidance—were automatically detected using a series of signal processing procedures and a database was obtained in this way. This database of common LSs was then classified using various time-frequency representations such as spectrograms, scalograms, Mel-spectrograms and gammatonegrams for comparison. The utilisation of proven, convolutional neural network (CNN)-based pre-trained models through the application of transfer learning facilitated the comparison, thereby enabling the acquisition of the features to be employed in the classification process. The performances of CNN, CNN and Long Short-Term Memory (LSTM) hybrid architecture and support vector machine methods were compared in the classification process. When the spectral structure of gammatonegrams, which capture the spectral structure of signals in the low-frequency range with high fidelity and their noise-resistant structures, is combined with a CNN architecture, the best classification accuracy of 97.3% ± 1.9 is obtained.
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İncele - MakaleAçık Erişim2022Scopus SJRQ2Extraction of low-dimensional features for single-channel common lung sound classification
Engin, M. Alptekin; Aras, Selim; Gangal, Ali
In this study, feature extraction methods used in the classification of single-channel lung sounds obtained by automatic identification of respiratory cycles were examined in detail in order to extract distinctive features at the lowest size. In this way, it will be possible to design a system for the detection of lung diseases, completely autonomously. In the study, automatic separation and classification of 400 respiratory cycles were performed from the single-channel common lung sounds obtained from 94 people. Leave one out cross validation (LOOCV) was used for the calibration and validation of the classification model. The Mel frequency cepstrum coefficients (MFCC), time domain features, frequency domain features, and linear predictive coding (LPC) were used for classification. The performance of the features was tested using linear discriminant analysis (LDA), k-nearest neighbors (k-NN), support vector machines (SVM), and naive Bayes (NB) classification algorithms. The success of combinations of features was explored and enhanced using the sequential forward selection (SFS). As a result, the best accuracy (90.14% in the training set and 90.63% in the test set) was acquired using the k-NN for the triple combination, which included the standard deviation of LPC and the standard deviation and the mean of MFCC. Graphical abstract: [Figure not available: see fulltext.]
AtıfScopus15WoS9OpenAlex14İncele - MakaleAçık Erişim2021Scopus SJRQ1Seed-propagated summer apples: Great morphological and biochemical diversity
Karatas, Neva; Ercisli, Sezai; Bozhuyuk, Mehmet Ramazan; Cakir, Ozlem; Necas, Tomas; Ondrasek, Ivo
The northeastern Anatolia region of Turkey is one of the richest plant biodiversity areas in Turkey. This study describes some important tree and fruit characteristics of 20 seed-propagated summer apple genotypes from northeastern Anatolia using morphological and biochemical markers. The genotypes were evaluated morphologically for tree growth habit, ripening date, fruit weight, fruit firmness, fruit shape, fruit color, fruit taste, juiciness and aroma. The biochemical characteristics measured were organic acids, SSC (soluble solid content), titratable acidity, vitamin C, total phenolic content and antioxidant capacity. We found significant differences for most of the morphological and biochemical characteristics. The genotypes exhibited quite diverse harvest dates ranging from 4 July to 16 August. Summer apple genotypes had fruit weight between 101.9 g and 133.5 g. The genotypes had diverse fruit shapes, including round, conic and oblate, and over fruit colours such as pink, red, yellow and green. Genotypes GUM7 and GUM13 stood out for fruit weight, SSC, total phenolic and antioxidant capacity. GUM7 and GUM13 present dissimilarity from the other genotypes. While the first showed a lower average fruit weight, SSC, total phenolic content and antioxidant capacity, the second revealed higher average of fruit weight, total phenolic content, antioxidant capacity and higher SSC. Due to their favorable characteristics, GUM13 can be used for further crosses with genotypes of other groups, which also present favorable characteristics.
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