Auscultation of the lung is a conventional technique used for diagnosing chronic obstructive pulmonary diseases (COPDs) and lower respiratory infections and disorders in patients. In most of the earlier works, wavelet transforms or spectrograms have been used to analyze the lung sounds. However, an accurate prediction model for respiratory disorders has not been developed so far. In this paper, a pre-trained optimized Alexnet Convolutional Neural Network (CNN) architecture is proposed for predicting respiratory disorders. The proposed approach models the segmented respiratory sound signal into Bump and Morse scalograms from several intrinsic mode functions (IMFs) using empirical mode decomposition (EMD) method. From the extracted intrinsic mode functions, the percentage energy calculated for each wavelet coefficient in the form of scalograms are computed. Subsequently, these scalograms are given as input to the pre-trained optimized CNN model for training and testing. Stochastic gradient descent with momentum (SGDM) and adaptive data momentum (ADAM) optimization algorithms were examined to check the prediction accuracy on the dataset comprising of four classes of lung sounds, normal, crackles (coarse and fine), wheezes (monophonic & polyphonic) and low-pitched wheezes (Rhonchi). On comparison to the baseline method of standard Bump and Morse wavelet transform approach which produced 79.04 % and 81.27 % validation accuracy, an improved accuracy of 83.78 % is achieved by the virtue of scalogram representation of various IMFs of EMD. Hence, the proposed approach achieves significant performance improvement in accuracy compared to the existing state-of- the-art techniques in literature.

肺部听诊是用于诊断慢性阻塞性肺疾病（COPD）和患者下呼吸道感染和疾病的常规技术。在大多数早期工作中，小波变换或声谱图已用于分析肺部声音。但是，到目前为止尚未开发出呼吸系统疾病的准确预测模型。本文提出了一种经过预训练的优化Alexnet卷积神经网络（CNN）体系结构，用于预测呼吸系统疾病。所提出的方法使用经验模式分解（EMD）方法，将来自多个固有模式函数（IMF）的分段呼吸声信号建模为凹凸和莫尔斯比例图。从提取的固有模式函数中，计算以比例图形式为每个小波系数计算的能量百分比。随后，将这些比例图作为预训练的优化CNN模型的输入，以进行训练和测试。研究了具有动量的随机梯度下降（SGDM）和自适应数据动量（ADAM）优化算法，以检查数据集的预测准确性，该数据集包括四类肺音：正常，crack啪声（粗音和细音），喘息声（单音和复音）和低调的喘息声（隆奇）。与标准Bump和Morse小波变换方法的基线方法（产生79.04％和81.27％的验证准确度）相比，借助EMD的各种IMF的比例尺表示，可以提高83.78％的准确度。因此，