DKPNet41: Directed knight pattern network-based cough sound classification model for automatic disease diagnosis.

PROBLEM: Cough-based disease detection is a hot research topic for machine learning, and much research has been published on the automatic detection of Covid-19. However, these studies are useful for the diagnosis of different diseases. AIM: In this work, we collected a new and large (n=642 subjects) cough sound dataset comprising four diagnostic categories: 'Covid-19', 'heart failure', 'acute asthma', and 'healthy', and used it to train, validate, and test a novel model designed for automatic detection. METHOD: The model consists of four main components: novel feature generation based on a specifically directed knight pattern (DKP), signal decomposition using four pooling methods, feature selection using iterative neighborhood analysis (INCA), and classification using the k-nearest neighbor (kNN) classifier with ten-fold cross-validation. Multilevel multiple pooling decomposition combined with DKP yielded 41 feature vectors (40 extracted plus one original cough sound). From these, the ten best feature vectors were selected. Based on each vector's misclassification rate, redundant feature vectors were eliminated and then merged. The merged vector's most informative features automatically selected using INCA were input to a standard kNN classifier. RESULTS: The model, called DKPNet41, attained a high accuracy of 99.39% for cough sound-based multiclass classification of the four categories. CONCLUSIONS: The results obtained in the study showed that the DKPNet41 model automatically and efficiently classifies cough sounds for disease diagnosis.

Multi-classification deep CNN model for diagnosing COVID-19 using iterative neighborhood component analysis and iterative ReliefF feature selection techniques with X-ray images.

Background: The acute respiratory syndrome coronavirus 2 (SARS-CoV-2) disease seriously affected worldwide health. It remains an important worldwide concern as the number of patients infected with this virus and the death rate is increasing rapidly. Early diagnosis is very important to hinder the spread of the coronavirus. Therefore, this article is intended to facilitate radiologists automatically determine COVID-19 early on X-ray images. Iterative Neighborhood Component Analysis (INCA) and Iterative ReliefF (IRF) feature selection methods are applied to increase the accuracy of the performance criteria of trained deep Convolutional Neural Networks (CNN). Materials and methods: The COVID-19 dataset consists of a total of 15153 X-ray images for 4961 patient cases. The work includes thirteen different deep CNN model architectures. Normalized data of lung X-ray image for each deep CNN mesh model are analyzed to classify disease status in the category of Normal, Viral Pneumonia and COVID-19. The performance criteria are improved by applying the INCA and IRF feature selection methods to the trained CNN in order to improve the analysis, forecasting results, make a faster and more accurate decision. Results: Thirteen different deep CNN experiments and evaluations are successfully performed based on 80-20% of lung X-ray images for training and testing, respectively. The highest predictive values are seen in the analysis using INCA feature selection in the VGG16 network. The means of performance criteria obtained using the accuracy, sensitivity, F-score, precision, MCC, dice, Jaccard, and specificity are 99.14%, 97.98%, 99.58%, 98.80%, 97.81%, 98.83%, 97.68%, and 99.56%, respectively. This proposed study is indicated the useful application of deep CNN models to classify COVID-19 in X-ray images.

Automated COVID-19 and Heart Failure Detection Using DNA Pattern Technique with Cough Sounds.

COVID-19 and heart failure (HF) are common disorders and although they share some similar symptoms, they require different treatments. Accurate diagnosis of these disorders is crucial for disease management, including patient isolation to curb infection spread of COVID-19. In this work, we aim to develop a computer-aided diagnostic system that can accurately differentiate these three classes (normal, COVID-19 and HF) using cough sounds. A novel handcrafted model was used to classify COVID-19 vs. healthy (Case 1), HF vs. healthy (Case 2) and COVID-19 vs. HF vs. healthy (Case 3) automatically using deoxyribonucleic acid (DNA) patterns. The model was developed using the cough sounds collected from 241 COVID-19 patients, 244 HF patients, and 247 healthy subjects using a hand phone. To the best our knowledge, this is the first work to automatically classify healthy subjects, HF and COVID-19 patients using cough sounds signals. Our proposed model comprises a graph-based local feature generator (DNA pattern), an iterative maximum relevance minimum redundancy (ImRMR) iterative feature selector, with classification using the k-nearest neighbor classifier. Our proposed model attained an accuracy of 100.0%, 99.38%, and 99.49% for Case 1, Case 2, and Case 3, respectively. The developed system is completely automated and economical, and can be utilized to accurately detect COVID-19 versus HF using cough sounds.