Usman Akram

Bio: Usman Akram is an academic researcher from University of the Sciences. The author has contributed to research in topics: Support vector machine & Fundus (eye). The author has an hindex of 8, co-authored 24 publications receiving 187 citations. Previous affiliations of Usman Akram include National University of Sciences and Technology & National University of Science and Technology.

Glaucoma detection through optic disc and cup segmentation using K-mean clustering

Abstract: One of the primary cause of blindness is Glaucoma. Although the disease is incurable but its symptoms can be minimized therefore early detection of the disease is essential. Elevated intraocular pressure, gradual vision loss which is a step towards blindness, structural damage to the retina are the marked symptoms of Glaucoma. Manually. It is diagnosed by examination of size, structure, shape of optic disc and optic cup. In patient of glaucoma Cup size increases while disc area remains the same hence cup to disc ratio (CDR) increases in glaucoma patient. CDR is the ratio of optic cup area to the optic disc area, which provides basis for the diagnosis of glaucoma. This article focuses on automated detection of glaucoma from fundus images using CDR. Region of interest (ROI) extraction through intensity weighted centroid method which is followed by preprocessing and recursively applied k-mean clustering segmentation for the detection of Optic cup (OC) and optic disc (OD). Ellipse fitting is implied for boundary smoothening of OC and OD. Performance of the proposed technique is assessed on 100 fundus images collected locally. Proposed approach gives an accuracy of 92% for glaucoma and Mean square error of 0.002 for CDR.

Localization and classification of heart beats in phonocardiography signals —a comprehensive review

Abstract: Phonocardiogram (PCG) signal represents recording of sounds and murmurs resulting from heart auscultation. Analysis of these PCG signals is critical in diagnosis of different heart diseases. Over the years, a variety of methods have been proposed for automatic analysis of PCG signals in time, frequency, and time-frequency domains. This paper presents a comprehensive survey of different methods proposed for automatic analysis of PCG signals with the objective to evaluate the current state-of-the-art and to determine the potential domains of effective analysis. An important aspect of our contribution is that the review is carried out as a function of domains of analysis rather than simply discussing different methods. Our method further splits analysis into pre-processing, localization, and classification, and details are presented in terms of techniques and classifiers used during these phases. Finally, results are summarized for normal heart beat, noisy heart beat, and different pathologies using metrices like accuracy and detection rate. In addition to time and frequency domain, time-frequency based methods including wavelet, empirical mode decomposition (EMD) and time-frequency representation (TFR) are selected for detailed analysis. The review concludes that the time-frequency representations like EMD and wavelets represent areas of exploration in future along with perspective of using different time-frequency techniques together.

Heart rate tracking in photoplethysmography signals affected by motion artifacts: a review

Abstract: Non-invasive photoplethysmography (PPG) technology was developed to track heart rate during motion. Automated analysis of PPG has made it useful in both clinical and non-clinical applications. However, PPG-based heart rate tracking is a challenging problem due to motion artifacts (MAs) which are main contributors towards signal degradation as they mask the location of heart rate peak in the spectra. A practical analysis system must have good performance in MA removal as well as in tracking. In this article, we have presented state-of-art techniques in both areas of the automated analysis, i.e., MA removal and heart rate tracking, and have concluded that adaptive filtering and multi-resolution decomposition techniques are better for MA removal and machine learning-based approaches are future perspective of heart rate tracking. Hence, future systems will be composed of machine learning-based trackers fed with either empirically decomposed signal or from output of adaptive filter.

Wireless Sensor Network for Distributed Event Detection Based on Machine Learning

Abstract: Pipelines are one of the most widely used means for oil/gas and water transportation worldwide. These pipelines are often subject to failures like erosion, sabotage and theft, causing high financial, environmental and health risks. Therefore, detecting leakages, estimating its size and location is very important. Current pipeline monitoring systems needs to be more automated, efficient and accurate methods for continuous inspection/reporting about faults. For this purpose, several pattern recognition and data mining techniques have been brought into the research community. In light of the issues of low efficiency and high false alarm rates in traditional pipeline condition monitoring, in this paper, we have used negative pressure wave (NPW) coupled with intelligent machine learning techniques integrated in distributed wireless sensor network (WSN) to identify specific events beased on raw data gathered by individual sensor nodes. This collaborative approach reduces communication overhead to minimum by processing raw data on sensor nodes directly and reporting the detected events only. We apply the methods of support vector machine (SVM), K-nearest neighbor (KNN) and Gaussian mixture model (GMM) in multi-dimensional feature space. The suggested technique is validated using a serial publication of experimentation on a field deployed test bed, with regard to performance of detection of leakages in pipelines.

Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: A review

Abstract: Background and objective During the recent global urgency, scientists, clinicians, and healthcare experts around the globe keep on searching for a new technology to support in tackling the Covid-19 pandemic The evidence of Machine Learning (ML) and Artificial Intelligence (AI) application on the previous epidemic encourage researchers by giving a new angle to fight against the novel Coronavirus outbreak This paper aims to comprehensively review the role of AI and ML as one significant method in the arena of screening, predicting, forecasting, contact tracing, and drug development for SARS-CoV-2 and its related epidemic Method A selective assessment of information on the research article was executed on the databases related to the application of ML and AI technology on Covid-19 Rapid and critical analysis of the three crucial parameters, ie, abstract, methodology, and the conclusion was done to relate to the model's possibilities for tackling the SARS-CoV-2 epidemic Result This paper addresses on recent studies that apply ML and AI technology towards augmenting the researchers on multiple angles It also addresses a few errors and challenges while using such algorithms in real-world problems The paper also discusses suggestions conveying researchers on model design, medical experts, and policymakers in the current situation while tackling the Covid-19 pandemic and ahead Conclusion The ongoing development in AI and ML has significantly improved treatment, medication, screening, prediction, forecasting, contact tracing, and drug/vaccine development process for the Covid-19 pandemic and reduce the human intervention in medical practice However, most of the models are not deployed enough to show their real-world operation, but they are still up to the mark to tackle the SARS-CoV-2 epidemic

Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda

Abstract: Artificial intelligence can assist providers in a variety of patient care and intelligent health systems. Artificial intelligence techniques ranging from machine learning to deep learning are prevalent in healthcare for disease diagnosis, drug discovery, and patient risk identification. Numerous medical data sources are required to perfectly diagnose diseases using artificial intelligence techniques, such as ultrasound, magnetic resonance imaging, mammography, genomics, computed tomography scan, etc. Furthermore, artificial intelligence primarily enhanced the infirmary experience and sped up preparing patients to continue their rehabilitation at home. This article covers the comprehensive survey based on artificial intelligence techniques to diagnose numerous diseases such as Alzheimer, cancer, diabetes, chronic heart disease, tuberculosis, stroke and cerebrovascular, hypertension, skin, and liver disease. We conducted an extensive survey including the used medical imaging dataset and their feature extraction and classification process for predictions. Preferred reporting items for systematic reviews and Meta-Analysis guidelines are used to select the articles published up to October 2020 on the Web of Science, Scopus, Google Scholar, PubMed, Excerpta Medical Database, and Psychology Information for early prediction of distinct kinds of diseases using artificial intelligence-based techniques. Based on the study of different articles on disease diagnosis, the results are also compared using various quality parameters such as prediction rate, accuracy, sensitivity, specificity, the area under curve precision, recall, and F1-score.