Determining features for discriminating PTB and normal lungs using phase congruency model

The appearance of the infected zone on the digital chest X-ray image for pulmonary tuberculosis (PTB) does not conform to standard shape, size or configuration. This study uses phase congruency (PC(x)) values to gather information from transition of adjacent pixel values that may be used as features to represent known disease type. The feature vector consisting of the average, variance, coefficient of variation and maximum PC(x)-values was found to be able to detect PTB with high accuracy

COVID-19: Symptoms Clustering and Severity Classification Using Machine Learning Approach

COVID-19 is an extremely contagious illness that causes illnesses varying from either the common cold to more chronic illnesses or even death. The constant mutation of a new variant of COVID-19 makes it important to identify the symptom of COVID-19 in order to contain the infection. The use of clustering and classification in machine learning is in mainstream use in different aspects of research, especially in recent years to generate useful knowledge on COVID-19 outbreak. Many researchers have shared their COVID-19 data on public database and a lot of studies have been carried out. However, the merit of the dataset is unknown and analysis need to be carried by the researchers to check on its reliability. The dataset that is used in this work was sourced from the Kaggle website. The data was obtained through a survey collected from participants of various gender and age who had been to at least ten countries. There are four levels of severity based on the COVID-19 symptom, which was developed in accordance to World Health Organization (WHO) and the Indian Ministry of Health and Family Welfare recommendations.  This paper presented an inquiry on the dataset utilising supervised and unsupervised machine learning approaches in order to better comprehend the dataset. In this study, the analysis of the severity group based on the COVID-19 symptoms using supervised learning techniques employed a total of seven classifiers, namely the K-NN, Linear SVM, Naive Bayes, Decision Tree (J48), Ada Boost, Bagging, and Stacking. For the unsupervised learning techniques, the clustering algorithm utilized in this work are Simple K-Means and Expectation-Maximization. From the result obtained from both supervised and unsupervised learning techniques, we observed that the result analysis yielded relatively poor classification and clustering results. The findings for the dataset analysed in this study do not appear to be providing the correct result for the symptoms categorized against the severity level which raises concerns about the validity and reliability of the dataset

The Knowledge and Use of Speech Therapy Mobile Applications: Speech-Language Pathologists’ Perspectives in Malaysia

Technology incorporation in speech therapy has been growing over the years. Mobile applications are among the adoptions that facilitate delivering speech therapy services. The situation in Malaysia is discouraging because there are not enough speech-language pathologists (SLPs) to serve the growing number of populations. Despite the abundance of available speech therapy mobile applications in the market, there is a lack of information focusing on the SLP’s knowledge and usage perspectives, especially in Malaysia. The objectives of this study are to describe the knowledge and usage perspectives of speech therapy mobile applications among SLPs in Malaysia and to analyze the instructional features and functional features relationships within the perspectives of SLPs. Surveys are established in three parts, with demographic questions in Part A, Likert scale responses for statements in Part B, and open-ended questions in Part C. This study is co-designed to relate to the results from an initial study that adopted PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) and features analysis. The data from the initial study includes a review of 161 apps out of 1797 that have been identified. Five instructional features and nine functional features are presented. There are 35 SLPs participating in the survey. Their responses demonstrate evidence of SLPs’ knowledge and usage of speech therapy mobile applications. We will propose a conceptual framework for the features of speech therapy mobile applications, using people with aphasia as a point of reference for users with speech and language disorders

COVID-19: Symptoms Clustering and Severity Classification Using Machine Learning Approach

COVID-19 is an extremely contagious illness that causes illnesses varying from either the common cold to more chronic illnesses or even death. The constant mutation of a new variant of COVID-19 makes it important to identify the symptom of COVID-19 in order to contain the infection. The use of clustering and classification in machine learning is in mainstream use in different aspects of research, especially in recent years to generate useful knowledge on COVID-19 outbreak. Many researchers have shared their COVID-19 data on public database and a lot of studies have been carried out. However, the merit of the dataset is unknown and analysis need to be carried by the researchers to check on its reliability. The dataset that is used in this work was sourced from the Kaggle website. The data was obtained through a survey collected from participants of various gender and age who had been to at least ten countries. There are four levels of severity based on the COVID-19 symptom, which was developed in accordance to World Health Organization (WHO) and the Indian Ministry of Health and Family Welfare recommendations.  This paper presented an inquiry on the dataset utilising supervised and unsupervised machine learning approaches in order to better comprehend the dataset. In this study, the analysis of the severity group based on the COVID-19 symptoms using supervised learning techniques employed a total of seven classifiers, namely the K-NN, Linear SVM, Naive Bayes, Decision Tree (J48), Ada Boost, Bagging, and Stacking. For the unsupervised learning techniques, the clustering algorithm utilized in this work are Simple K-Means and Expectation-Maximization. From the result obtained from both supervised and unsupervised learning techniques, we observed that the result analysis yielded relatively poor classification and clustering results. The findings for the dataset analysed in this study do not appear to be providing the correct result for the symptoms categorized against the severity level which raises concerns about the validity and reliability of the dataset

Calcification detection of coronary artery disease in intravascular ultrasound image: Deep feature learning approach

Coronary artery disease (CAD) is part of the non-communicable disease (NCD) in cardiovascular disease (CVD). The blood vessel area became narrow when the calcification with the plaque embedded in the coronary artery inner wall. The radiologists and medical practitioners used visual inspection to detect calcification on IVUS image. The presence of calcification will not be able to do the measurement to calculate the maximum diameter and the maximum area for the patient coronary artery either before treatment or after treatment. More than 100 frames per patient is needed to analyse the location of the calcification. In this study, our aim is to detect the presence and the absence of the calcification in the coronary artery using intravascular ultrasound (IVUS) images with catheter frequency of 20MHz. The IVUS images used were the original Cartesian coordinate image and the polar reconstructed coordinate image. In this study, three types of convolutional neural network (CNN) using Directed Acyclic Graph networks, were used together with five types of classifiers. The dataset used to demonstrate our framework is Dataset B from MICCAI Challenge 2011 that consists of 2175 coronary artery disease IVUS image where 530 are IVUS images with calcification and 1645 are IVUS images without calcification. The cross validation for testing and training, the k-fold value used was 2, 3, 5 and 10. The performance measures for the ResNet-50, the ResNet-101 and the Inception-V3 model shows an excellent result using support vector machine classifier and discriminant analysis for both types of images. A better improvement using polar reconstructed coordinate image when using decision tree classifier and Naïve Bayes classifier whilst ResNet-101 architecture shows an excellent performance measure when applying images polar reconstructed images when using k-nearest neighbor classifier. However, Naïve Bayes classifier has an excellent result when using Inception-V3 architecture

The Knowledge and Use of Speech Therapy Mobile Applications: Speech-Language Pathologists’ Perspectives in Malaysia

Technology incorporation in speech therapy has been growing over the years. Mobile applications are among the adoptions that facilitate delivering speech therapy services. The situation in Malaysia is discouraging because there are not enough speech-language pathologists (SLPs) to serve the growing number of populations. Despite the abundance of available speech therapy mobile applications in the market, there is a lack of information focusing on the SLP’s knowledge and usage perspectives, especially in Malaysia. The objectives of this study are to describe the knowledge and usage perspectives of speech therapy mobile applications among SLPs in Malaysia and to analyze the instructional features and functional features relationships within the perspectives of SLPs. Surveys are established in three parts, with demographic questions in Part A, Likert scale responses for statements in Part B, and open-ended questions in Part C. This study is co-designed to relate to the results from an initial study that adopted PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) and features analysis. The data from the initial study includes a review of 161 apps out of 1797 that have been identified. Five instructional features and nine functional features are presented. There are 35 SLPs participating in the survey. Their responses demonstrate evidence of SLPs’ knowledge and usage of speech therapy mobile applications. We will propose a conceptual framework for the features of speech therapy mobile applications, using people with aphasia as a point of reference for users with speech and language disorders

Breast Cancer Classification: Features Investigation using Machine Learning Approaches

Breast cancer is the second most common cancer after lung cancer and one of the main causes of death worldwide. Women have a higher risk of breast cancer as compared to men. Thus, one of the early diagnosis with an accurate and reliable system is critical in breast cancer treatment. Machine learning techniques are well known and popular among researchers, especially for classification and prediction. An investigation was conducted to evaluate the performance of breast cancer classification for malignant tumors and benign tumors using various machine learning techniques, namely k-Nearest Neighbors (k-NN), Random Forest, and Support Vector Machine (SVM) and ensemble techniques to compute the prediction of the breast cancer survival by implementing 10-fold cross validation. This study used a dataset obtained from Wisconsin Diagnostic Breast Cancer (WDBC) with 23 selected features measured from 569 patients, from which 212 patients have malignant tumors and 357 patients have benign tumors. The analysis was performed to investigate the feature of the tumors based on its mean, standard error, and worst. Each feature has ten properties which are radius, texture, perimeter, area, smoothness, compactness, concavity, concave, symmetry and fractal dimensions. The selection of features was considered a significant influence to the breast cancer. The analysis is compared and evaluated with thirty features to determine the features used for breast cancer classification. The result shown AdaBoost has obtained the highest accuracy for thirty features at 98.95%, ten features of mean at 98.07%, and ten features of worst at 98.77% with a lowest error rate. Additionally, the proposed methods are classified using 2-fold, 3-fold, and 5-fold cross validation to meet the best accuracy rate. Comparison results between all methods show that AdaBoost ensemble methods gave the highest accuracy at 98.77% for 10-fold cross validation, while 2-fold and 3-fold cross validation at 98.41% and 98.24%, respectively. Nevertheless, the result with 5-fold cross validation shows SVM produced the best accuracy rate at 98.60% with the lowest error rate

Malaysian women shoe sizing system using multivariate normal probability distribution

The Malaysian women population frequently face the problem of finding the best fitting shoes. This problem is created by the absence of a Malaysian women shoe sizing system. Standard statistical methods involving the Multivariate Normal distribution are used in a novel process of addressing issues related to the creation of a shoe sizing system, in particular, the problem of defining categories of shoe sizes. This study focused on the use of five-foot measurement namely, foot length (FL), foot breadth (FB), foot's ball girth (BG), instep length (IL), and fibulare instep length (FIL). Univariate hypothesis testing was performed taking advantage of the existence of normal probability distribution. For brevity, details for FL, FB, and BG are shown in this paper, followed by a comparison of performance results between FL, FB, BG and FL, FB, BG, IL, FIL. Our results were compared to a similar study showing almost the same aggregate loss and coverage percentage. The result shows that a modest sample size of 160 was sufficient to define categories of shoe sizes to help develop a prototype shoe sizing system using the proposed novel approach. The proposed prototype shoe sizing system provides information for the planning, design, and manufacturing of Malaysian women's footwear with implications for better fitness and comfort

Extreme learning machine based sub-key generation for cryptography system

The key generation process is the substantial step in any cryptosystem. Incorporating Artificial Neural Network (ANN) in the algorithmic work of cryptography achieves good performance in realizing high accuracy and security. In this paper, ANN based sub-key generation algorithm is presented. Extreme learning Machine (ELM) type is adopted for one hidden layer neural network. Initial key includes all needed information about ANN topology, activation function, and seeds for Pseudo-Random Number Generation (PRNG) in each round to initialize input-hidden layer weights and data. Sub-key in each round is generated from output layer weights. Evaluation measures have proved complete sensitivity and inevitability of this approach. In addition, it contributes in reducing the risks of breaking the symmetric key algorithms due to the generated independent sub-key in each round. Thus, it can be integrated in any cryptosystem for subkey generation

Lung disease classification using GLCM and deep features from different deep learning architectures with principal component analysis

Lung disease classification is an important stage in implementing a Computer Aided Diagnosis (CADx) system. CADx systems can aid doctors as a second rater to increase diagnostic accuracy for medical applications. It has also potential to reduce waiting time and increasing patient throughput when hospitals high workload. Conventional lung classification systems utilize textural features. However textural features may not be enough to describe properties of an image. Deep features are an emerging source of features that can combat the weaknesses of textural features. The goal of this study is to propose a lung disease classification framework using deep features from five different deep networks and comparing its results with the conventional Gray-level Co-occurrence Matrix (GLCM). This study used a dataset of 81 diseased and 15 normal patients with five levels of High Resolution Computed Tomography (HRCT) slices. A comparison of five different deep learning networks namely, Alexnet, VGG16, VGG19, Res50 and Res101, with textural features from Gray-level Co-occurrence Matrix (GLCM) was performed. This study used a K-fold validation protocol with K = 2, 3, 5 and 10. This study also compared using five classifiers; Decision Tree, Support Vector Machine, Linear Discriminant Analysis, Regression and k-nearest neighbor (k-NN) classifiers. The usage of PCA increased the classification accuracy from 92.01% to 97.40% when using k-NN classifier. This was achieved with only using 14 features instead of the initial 1000 features. Using SVM classifier, a maximum accuracy of 100% was achieved when using all five of the deep learning features. Thus deep features show a promising application for classifying diseased and normal lungs