![]() An observation (e.g., an image) can be represented in many ways including a vector of intensity values per pixel, or in a more abstract way as a set of edges, regions of a particular shape, and various other features. ĭeep learning is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data by using model architectures, with complex structures, composed of multiple non-linear transformations. Some of the representations are inspired by advances in neuroscience and are loosely based on interpretation of information processing and communication patterns in a nervous system, such as neural coding which attempts to define a relationship between the stimulus and the neuronal responses and the relationship among the electrical activities of the neurons in the brain. Research in this area attempts to make better representations and create models to learn these representations from large-scale unlabeled data. Especially in computer vision and speech recognition, machine learning is reaching human levels of detection. Over the years, various successful algorithms were developed and now with the deep learning algorithms, error rate became close to negligible. The latest technology that is attempting to solve this problem is machine learning. However, for critical systems, the error rate for the decision was too high. In the past decade, various technologies, such as expert systems, have been used to attempt to solve this problem. Data collected from the environment is usually non-linear, so we cannot use traditional methods to find patterns or create mathematical models. But most of the time, it is very hard to spot these patterns, especially if the data is very large. Accuracy results of the experiments were (1) CNN 86%, SVM 86%, (2) CNN 76%, SVM 75%, (3) CNN 80%, SVM 80%, and (4) CNN 62%, SVM 62%, respectively.Īs a result, we found out that spectrogram image classification with CNN algorithm works as well as the SVM algorithm, and given the large amount of data, CNN and SVM machine learning algorithms can accurately classify and pre-diagnose respiratory audio.ĭiagnosis or classification requires recognizing patterns. We prepared four data sets for each CNN and SVM algorithm to classify respiratory audio: (1) healthy versus pathological classification (2) rale, rhonchus, and normal sound classification (3) singular respiratory sound type classification and (4) audio type classification with all sound types. Since using MFCC features with a SVM algorithm is a generally accepted classification method for audio, we utilized its results to benchmark the CNN algorithm. ![]() We employed two types of machine learning algorithms mel frequency cepstral coefficient (MFCC) features in a support vector machine (SVM) and spectrogram images in the convolutional neural network (CNN). Using this device, we recorded 17,930 lung sounds from 1630 subjects. ![]() In order to store respiratory sounds on a computer, we developed a cost-effective and easy-to-use electronic stethoscope that can be used with any device. In this study, our aim is to develop a non-invasive method of classifying respiratory sounds that are recorded by an electronic stethoscope and the audio recording software that uses various machine learning algorithms. In the field of medicine, with the introduction of computer systems that can collect and analyze massive amounts of data, many non-invasive diagnostic methods are being developed for a variety of conditions.
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