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Biometric authentication is the process that allows an individual to be identified based on a set of unique biological features data. In this study, we present different experiments to use the cardiac sound signals (phonocardiogram “PCG”) as a biometric authentication trait. We have applied different features extraction approaches and different classification techniques to use the PCG as a biometric trait. Through all experiments, data acquisition is based on collecting the cardiac sounds from HSCT‐11 and PASCAL CHSC2011 datasets, while preprocessing is concerned with de‐noising of cardiac sounds using multiresolution‐decomposition and multiresolution‐reconstruction (MDR‐MRR). The de‐noised signal is then segmented based on frame‐windowing and Shanon energy (SE) methods. For feature extraction, Cepstral (Cp) domain (based on mel‐frequency) and time‐scale (T‐S) domain (based on Wavelet Transform) features are extracted from the de‐noised signal after segmentation. The features, extracted from the Cp‐domain and the T‐S domain, are fed to four different classifiers: Artificial neural networks (ANN), support vector machine (SVM), random forest (RF) and K‐nearest neighbor (KNN). The performance of the classifications is assessed based on the k‐fold cross validation. The computation complexity of the feature extraction domains is expressed using the Big‐O measurements. The T‐S features are superior to PCG heart signals in terms of the classification accuracy. The experiments' results give the highest classification accuracy with lowest computation complexity for RF in the Cp domain and SVM and ANN in the T‐S domain.

This study proposes an image classification methodology that automatically classifies human brain magnetic resonance (MR) images. The proposed methods contain four main stages: Data acquisition, preprocessing, feature extraction, feature reduction and classification, followed by evaluation. First stage starts by collecting MRI images from Harvard and our constructed Egyptian database. Second stage starts with noise reduction in MR images. Third stage obtains the features related to MRI images, using stationary wavelet transformation. In the fourth stage, the features of MR images have been reduced using principles of component analysis and kernel linear discriminator analysis (KLDA) to the more essential features. In last stage, the classification stage, two classifiers have been developed to classify subjects as normal or abnormal MRI human images. The first classifier is based on K‐Nearest Neighbor (KNN) on Euclidean distance. The second classifier is based on Levenberg‐Marquardt (LM‐ANN). Classification accuracy of 100% for KNN and LM‐ANN classifiers has been obtained. The result shows that the proposed methodologies are robust and effective compared with other recent works.

One of the pandemics that have caused many deaths is the Coronavirus disease 2019 (COVID-19). It first appeared in late 2019, and many deaths are increasing day by day until now. Therefore, the early diagnosis of COVID-19 has become a salient issue. Additionally, the current diagnosis methods have several demerits, and a new investigation is required to enhance the diagnosis performance. In this paper, a set of phases are performed, such as collecting data, filtering and augmenting images, extracting features, and classifying ECG images. The data were obtained from two publicly available ECG image datasets, and one of them contained COVID ECG reports. A set of preprocessing methods are applied to the ECG images, and data augmentation is performed to balance the ECG images based on the classes. A deep learning approach based on a convolutional neural network (CNN) is performed for feature extraction. Four different pre-trained models are applied, such as Vgg16, Vgg19, ResNet-101, and Xception. Moreover, an ensemble of Xception and the temporary convolutional network (TCN), which is named ECGConvnet, is proposed. Finally, the results obtained from the former models are fed to four main classifiers. These classifiers are softmax, random forest (RF), multilayer perception (MLP), and support vector machine (SVM). The former classifiers are used to evaluate the diagnosis ability of the proposed methods. The classification scenario is based on fivefold cross-validation. Seven experiments are presented to evaluate the performance of the ECGConvnet. Three of them are multi-class, and the remaining are binary class diagnosing. Six out of seven experiments diagnose COVID-19 patients. The aforementioned experimental results indicated that ECGConvnet has the highest performance over other pre-trained models, and the SVM classifier showed higher accuracy in comparison with the other classifiers. The resulting accuracies from ECGConvnet based on SVM are (99.74%, 98.6%, 99.1% on the multi-class diagnosis tasks) and (99.8% on one of the binary-class diagnoses, while the remaining achieved 100%). It is possible to develop an automatic diagnosis system for COVID based on deep learning using ECG data.