## 4 Related Work  
The current state-of-the-art in the application of supervised learning for pulmonary diseases is reported in this section. The authors in [28] classify respiratory sounds as normal and pulmonary emphysema by analysing a dataset composed of 168 subjects. They obtain an accuracy score ranging from 87.4% to 88.7%. The authors in [29] reached a detection rate of 0.92 in the discrimination between healthy and pathological crackles by exploiting supervised machine learning. A support vector machine model is discussed by researchers in [30] to discriminate pneumonia and congestive heart failure. In total, 257 patients are analysed by the authors, reaching a detection rate between 0.82 and 0.87. A detection rate equal to 0.9 is obtained by the authors in [31]. They propose the adoption of the support vector machine algorithm with the aim to distinguish between healthy lung sounds and non-healthy ones. Researchers in [32] exploited Empirical Mode Decomposition (EMD), which is a time domain, and computed the Instantaneous Frequency (IF) for the detection of disease starting from lung sounds. Other research papers presented Short-Time Fourier Transform (STFT) results, from which signal features can be extracted, such as peak frequency [33], local maxima, peak coexistence, discontinuity [34], mean, amplitude deviation, local maximum, discontinuity criteria [35], mean and median frequency, spectral crest factor, entropy, relative power factor, and high-order frequency moment. Another approach used is to change the STFT as an image and then to perform processing such as image processing [33; 35]. The advantages of STFT are that it is computationally simple and allows the easy observation of the frequency of the signal each time. The drawbacks of this method are the relatively low resolution and the uncertainty of the time when the frequency occurs because the frequencies are calculated at specified intervals. Another TF domain method used is Wigner-Ville Distribution, exploited by several researchers, i.e., [36; 37], to show the differences between normal lung sounds and pathological lung sounds. Another approach is to identify wheeze sounds in pulmonary audio, as discussed by researchers in [38], obtaining a detection ratio equal to 0.95 in the detection of lung disease-affected patients. Neural networks (NNs)  
Figure 6: Roc analysis. are exploited by researchers in [39] in lung disease detection, obtaining an accuracy score equal to 71.81% using a deep neural network trained with respiratory sounds based on Mel spectrogram features. The authors in [40] explore whether the application of a convolutional neural network in the deep learning context can assist medical experts by providing a detailed and rigorous analysis of the medical respiratory audio data for chronic obstructive pulmonary disease detection. They exploit features such as MFCC, Mel spectrogram, Chroma, and Chroma CENS. The proposed method is able to predict the severity of the disease identified, such as mild, moderate, or acute, obtaining an accuracy score equal to 93%. Researchers in [41] propose a method proposed aimed to transform the characteristic vectors from reconstructed signals into reconstructed signal energy for lung disease detection. They consider linear discriminant analysis, which aimed to reduce the dimension of characteristic vectors. They consider a neural network to carry out lung sound recognition, where comparatively high-dimensional characteristic vectors and low-dimensional vectors are set as input and lung sound categories as output, with an accuracy score ranging between 82.5% and 92.5%. Table 5 shows a comparison of the state of the art in automatic lung disease detection, in terms of features extracted and performance obtained. As shown from the comparison in Table 5, the proposed method (last row in Table 5) obtains detection performance equal to 98%.