# Voice Pathology Detection and Classification Using Convolutional Neural Network Model
## 2 Related Work  
The utilization of machine learning (ML) can be useful in many applications such as medical diagnosis [23], cancer detection [24], smart building applications [25], and others [11; 11; 26]. Machine learning methods are valuable for discriminatory detection and classification tasks [27; 28]. These methods have been used in diverse speech identification uses, where one of these uses is pathological voice investigation [29]. The identification and the classification of voice pathology techniques are still one of the difficult domains within the investigation of speech detection. Besides, these basic techniques are expensive and need more time and many sorts of gear [30]. Many researchers focus on the Saarbrucken voice database (SVD) in their studies. The researchers that utilized SVD extracted different features from voice records prior to pathology identification. The features that are frequently extracted are entropy, energy, time, contained Mel-frequency cepstral coefficients (MFCC), cepstral domains, frequency, harmonics-to-noise ratio, short-term cepstral parameters, normalized noise energy, and others [2; 31; 32; 33]. After this stage the classification task will begin. Many binary and multi-classification methods have been used such K-means clustering, Support Vector Machine, and so on. To our best knowledge, our study is the first study to present voice pathology detection and classification using a convolutional neural network (CNN). The outcomes of the published studies vary greatly because of the variances among the datasets used in the experimental results. According to [PERSON] et al. [34], the accuracy achieved utilizing 200 records of sustained vowel /a/ represent a high value and it's very close to our study. Other studies utilized the combination of vowels /a/, /i/ and /u/ to get high accuracy and do not focus on the pathology causes. In the studies by [PERSON] et al. in [35] they achieved high accuracy of 87.82% utilizing subset involving four kinds of voice pathologies that include 71 types. Also, [PERSON] et al. [16; 36] achieved an accuracy of 99.68% due to their use of a subset involving a few of the pathologies to conduct a test on information that was moreover displayed in other accessible datasets, such as Arabic Voice Pathology Database (AVPD), and Massachusetts Eye and Ear Infirmary Database (MEEI). Another study conducted by [PERSON] et al. [13] utilized a subset involving three kinds of voice pathologies that achieved an accuracy of 93.20%. In addition, they utilized a combination of voice records as an electroglottograph signal to increase the accuracy to 99.98%. However, in another study conducted by [PERSON] et al. [37] they achieved a high accuracy of 100% in the detection issue by their method to separate male and female speakers. The study of [PERSON] et al. [38] assessed the execution of the proposed high order statistic feature highlights extracted from wavelet space to segregate between normal voices and pathological ones. Traditional features such as Cruel Wavelet Esteem, Cruel Wavelet Vitality and Cruel Wavelet Entropy were used in the experiments. These highlights, combined with a SVM classifier, reach the most elevated correctness of 99.26% within the location step and 100% when classifying the information. In order to include concrete logical included values a clinical evaluation was performed on information collected from subjects from a healing center in Tunez. The results were acceptable and the precisions were 94.82% and 94.44% for the location and classification, respectively. [PERSON] et al. [39] worked on the discovery of co-existent laryngeal issues for which the major phonic side effect is the same, creating features with noteworthy inter-class coverage. Based on the combination of SE, ZCR and SH, all utilized for extraction, related with DPM, particularly received for classification, the proposed approach was effectively concluded, productively dealing within definitions and inconsistencies with an estimated precision of 95%.