Chandigarh University

About: Chandigarh University is a education organization based out in Mohali, India. It is known for research contribution in the topics: Computer science & Chemistry. The organization has 1358 authors who have published 2104 publications receiving 10050 citations.

IOT Big Data Analytics in Healthcare: Benefits and Challenges

Abstract: Medical data is being generated from a wide variety of sources today, including smart phones, wearable sensors, patient records, clinical reports, researchers, healthcare professionals, and organizations. Healthcare data has the potential to support rapid decision making processes in detection of critical diseases and pandemics. The widespread acceptance of the Internet of Things (IOT) has brought challenges for analytical systems, because heterogeneous, complex and un-structured data has to be processed and stored in real time. The incorporation of big data analytics (BDA) of the Internet of Things is vital to reducing healthcare costs and identifying risks. The design, however, has a staggering complexity that requires the use of sophisticated technologies and techniques. Using Deep Learning techniques, Machines can be trained with Artificial Intelligence to support the medical industry in various ways. This paper describes how the IOT Analytics are beneficial in the healthcare domain and discusses the challenges in it.

A novel machine learning-based analytical framework for automatic detection of COVID-19 using chest X-ray images

Abstract: Considering the prevailing scenario of COVID‐19 pandemic, early detection of the disease is an important and crucial step in disease management. Early detection and correct treatment may limit disease progression to severe levels and prevent deaths. In addition, early isolation of infected patients will lead to control transmission rate and will possibly reduce the stress on the present healthcare system. Currently, the most common and reliable testing method available for COVID‐19 diagnosis is real‐time reverse transcription‐polymerase chain reaction (rRT‐PCR) test. However, the chest radiological (X‐ray) imaging can be used as an alternate method to rRT‐PCR test, and early COVID‐19 symptoms can be investigated by critical examination of patient's chest scans. In the present work, a novel machine learning (ML)‐based analytical framework is developed for automatic detection of COVID‐19 using chest X‐ray (CXR) images of plausible patients. The framework is designed, trained, and validated to identify four classes of CXR images namely, healthy, bacterial pneumonia, viral pneumonia, and COVID‐19. The experimental results pose the proposed framework as a potential candidate for COVID‐19 disease diagnosis using CXR images, with training, validation, and testing accuracy of 92.4%, 88.24%, and 87.13%, respectively, in four‐class classification. The comparative analysis demonstrates the better capabilities of the proposed framework COVID‐19 detection along with other types of pneumonia.

Predicting risk of Cervical Cancer : A case study of machine learning

Abstract: Human Papillomavirus (HPV) is the cause for 90% of cases of Cervical Cancer which can only be cured if diagnosed in early stage. Years of clinical experience in testing the tissue slide and examina...

Review of Various Image Fusion Algorithms and Image Fusion Performance Metric

Abstract: Image fusion is the process in which substantial information taken through different sensors, different exposure values and at different focus points is integrated together to generate a composite image. In various applications, different types of data sets are captured with the help of different sensors like infrared (IR) region and visible region, Computed Tomography (CT) and Positron Emission Tomograph (PET) scan, multifocus images with different focal points and images taken by a static camera at different exposure values. A most promising area of image processing nowadays is image fusion. The picture fusion method seeks to incorporate two or more pictures into one picture that contains better data than each source picture without adding any artifacts. It plays an essential role in distinct applications like biomedical diagnostics, photography, object identification, surveillance, defense, and remote sensing satellite imaging. Three elements are taken into consideration in this review article that includes spatial domain fusion methodology, different transformation domain techniques, and image fusion performance evaluation metrics.