A Shuffling Building Block and Augmentation Parameter Tuning Techniques to Handle Small Medical Dataset

The recent COVID-19 outbreak showed us the importance of faster disease diagnosis using medical image processing as it is considered the most reliable and accurate diagnostic tool. In a CNN architecture, performance improves with the increasing number of trainable parameters at the cost of processing time. We have proposed an innovative approach of combining efficient novel architectures like Inception, ResNet, and ResNet-Xt and created a new CNN architecture that benefits Extreme Cardinal dimensions. We have also created four variations of the same base architecture by varying the position of each building block and used X-Ray, Microscopic, MRI, and pathMNIST datasets to train our architecture. For learning curve optimization, we have applied learning rate changing techniques, tuned image augmentation parameters, and chose the best random states value. For a specific dataset, we reduced the validation loss from 0.22 to 0.18 by interchanging the architecture's building block position. Our results indicate that image augmentation parameters can help to decrease the validation loss. We have also shown rearrangement of the building blocks reduces the number of parameters, in our case, from 5,689,008 to 3,876,528. © 2023 IEEE.

N/MEMS Biosensors: An Introduction

In the twenty-first century, biosensors have gathered much wider attention than ever before, irrespective of the technology that promises to bring them forward. With the recent COVID-19 outbreak, the concern and efforts to restore global health and well-being are rising at an unprecedented rate. A requirement to develop precise, fast, point-of-care, reliable, easily disposable/reproducible and low-cost diagnostic tools has ascended. Biosensors form a primary element of hand-held medical kits, tools, products, and/or instruments. They have a very wide range of applications such as nearby environmental checks, detecting the onset of a disease, food quality, drug discovery, medicine dose control, and many more. This chapter explains how Nano/Micro-Electro-Mechanical Systems (N/MEMS) can be enabling technology toward a sustainable, scalable, ultra-miniaturized, easy-to-use, energy-efficient, and integrated bio/chemical sensing system. This study provides a deeper insight into the fundamentals, recent advances, and potential end applications of N/MEMS sensors and integrated systems to detect and measure the concentration of biological and/or chemical analytes. Transduction principle/s, materials, efficient designs, including readout technique, and sensor performance are explained. This is followed by a discussion on how N/MEMS biosensors continue to evolve. The challenges and possible opportunities are also discussed. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Diagnosis and Possible Use of Traditional Medicinal Plant to combat COVID-19

The novel Coronavirus disease 2019 has turned into a serious public health concern around the globe. Due to its high adaptability in every environment, this novel virus has outspread like fire compared to SARS and MERS, but the fatality rate is lower. This outbreak has caused illness to many people worldwide. Especially, people with lung problems and other chronic diseases are at high risk. Although convincing results have shown the use of chemically synthesized drugs, these drugs have various limitations. Therefore, a medicinal plant might provide a solution for the novel virus along with the recent advancement in computational methods that have paved a new path to operate complex molecules, which will ultimately result in discovering new and advanced drugs. In this review, we have summarized and analyze plant-based natural product which can be used to boost the immune system or act as a remedy for patients suffering from a novel virus. This review also focuses on the structure of COVID-19, various diagnostics tools, preventive measures, and data analysis of the novel Coronavirus of India.Copyright © 2021 Bentham Science Publishers.

AI-Based and Digital Mental Health Apps: Balancing Need and Risk

Mental health and well-being are increasingly important topics in discussions on public health [1]. The COVID-19 pandemic further revealed critical gaps in existing mental health services as factors such as job losses and corresponding financial issues, prolonged physical illness and death, and physical isolation led to a sharp rise in mental health conditions [2]. As such, there is increasing interest in the viability and desirability of digital mental health applications. While these dedicated applications vary widely, from platforms that connect users with healthcare professionals to diagnostic tools to self-assessments, this article specifically explores the implications of digital mental health applications in the form of chatbots [3]. Chatbots can be text based or voice enabled and may be rule based (i.e., linguistics based) or based on machine learning (ML). They can utilize the power of conversational agents well-suited to task-oriented interactions, like Apple's Siri, Amazon's Alexa, or Google Assistant. But increasingly, chatbot developers are leveraging conversational artificial intelligence (AI), which is the suite of tools and techniques that allow a computer program to seemingly carry out a conversational experience with a person or a group. © 1982-2012 IEEE.

Comparative Analysis of Deep Learning Models for Covid-19 Detection from Chest X-rays

Lungs are the organs which play key role in human respiratory system. The severity of infections caused to the lungs might vary from mild to moderate. Chest X-Ray is a principal diagnostic tool used in detecting various types of lung diseases. The whole world is struggling due to a pandemic arised in 2019, known as Coronavirus disease or Covid-19, a severe respiratory infection. The medical industry demanded the use of computer aided techniques for analysing extremity of the disease. This work aims to examine the effectiveness of pretrained deep learning models in classifying chest X-rays as Covid, Viral pneumonia and Healthy cases. We have used largest publicly accessible Covid dataset, QaTa Cov-19 for conducting experiments. Out of six fine tuned deep learning pretrained network models, Densenet 201 outperformed with highest accuracy of 98.6% and AUC of 0.9996. © 2022 IEEE.

A joint segmentation and classification framework for COVID-19 infection segmentation and detection from chest CT images

In COVID19 management, CT images are used as noninvasive diagnostic tools for screening and disease monitoring. Segmentation of infections provides valuable visual interpretations in the process of prognosis and decision making. Segmentation of COVID19 infection from chest CT images is challenging due to the presence of multiple infection types and complex morphological patterns. This paper presents a novel multi task learning framework for COVID19 infection segmentation and detection. The proposed model called DB-YNet, is built on YNet architecture with a dense bottleneck and attention based UNet backbone. This model is trained and tested with standard datasets, demonstrating superior segmentation and classification metrics. It achieves a Dice score of 0.9923 for segmentation, and classification accuracies of 0. 0.9875 and 0.9961 under binary and multi class classifications respectively. In COVID19 triage, DB-YNet is a promising tool to assist physicians in the early identification of COVID19 infected patients for quick clinical interventions. © 2023 Wiley Periodicals LLC.

Viral Pneumonia and Covid Screening on Lung Ultrasound

The rise of Covid-19 pandemic has exaggerated the necessity for safe, quick and sensitive diagnostic tools to confirm the protection of tending employees and patients. Although ML has shown success in medical imaging, existing studies concentrate on Covid-19 medicine victimization using Deep Learning (DL) with X-ray and computed axial Tomography (CT) scans. During this study we tend to aim to implement CNN model on Lung Ultrasound (LUS), to assist doctors with the designation of Covid-19 patients. We selected LUS since it's quicker, cheaper and additional out there in rural areas compared to CT and X- ray. We have used the biggest public dataset containing LUS pictures and videos of Covid, Pneumonia and healthy patients that has been collected from totally different resources. We tried out frame level approach that extracted 5 frames per patient video. We'll use this dataset to experiment with a CNN model that has hyper parameter calibration. We conjointly enclosed explainable AI using Grad-CAM that uses gradients of a selected target that flows through the convolutional network to localize and highlight regions of the target within the image. Moreover, we'll experiment with completely different data preprocessing techniques that may aid with pattern recognition and increasing the DL model's accuracy like histogram equalization, standardization, Principle Component Analysis (PCA) and Synthetic Minority Oversampling Technique (SMOTE). Lastly, we tend to create a straightforward application that diagnoses LUS videos with our CNN model, and shows the frame results with visual illustration of why the model has taken certain prediction with the help of Gradient-Weighted category Activation Mapping (Grad-CAM). © 2022 IEEE.

Improved Method of X-Ray Image Classification Using Deep Learning for Covid-19 Detection

COVID-2019, which popped up in December 2019 in Wuhan, China. It quickly spread around the world and turned into a pandemic.It has wreaked havoc on people's daily lives and public health. It has wreaked havoc on people's everyday lives, health, and the economic growth. Positive cases should be found as early as possible in order to control the disease outbreak and treat those who have been infected as fast as possible. Because there are no precise toolkits available, the demand for additional diagnostic tools has increased significantly. Current findings from radiology imaging techniques suggest that such images can reveal a lot about the COVID virus. The use of modern AI technologies (Artificial intelligence) algorithms in conjunction with imaging techniques can help to identify this disease accurately. This paper presents a new model for automatically detecting COVID-19 from X-ray pictures. The suggested model was created to deliver precise diagnostics for three classes of categorization. © 2022 IEEE.

Detection of SARS-CoV-2 by RT-LAMP assay in Human COVID-19 patients

Background: The novel COVID-19 outbreak has infected human population all around the world. Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) diagnosis in a rapid manner remains challenging for health care professionals. Currently, RT-qPCR technique is extensively practiced in SARS-CoV-2 diagnosis and is considered as gold standard. The constraints of RT-qPCR, high cost and need for trained technician, longer detection time, highlighted the need for alternate healthcare diagnostic approaches. They follow the WHO assured standard and offer the health-care sector optimism. One of them is the Loop Mediated isothermal amplification system (LAMP). There is no need for costly equipment like thermal cycler since LAMP assay is performed at a fixed temperature. It can also be implemented as a point of care testing device. RT-LAMP is one of the extensively used isothermal amplification system in pathogen diagnostics.Aims: The current study aims to validate and standardize RT-LAMP assay for rapid diagnosis of SARS-CoV-2 in both lab and field conditions. The reactions can be carried out using a heating vessel including the use of a water bath and end-point detection by colorimetry. A rising middle ground of tiny, more portable technology, that provides most of the capability at less cost and time.Methods and Results: 20 Samples were taken from COVID-19 positive patients. RNA extraction from COVID-19 samples was followed up by one-step reverse transcription and loop-mediated isothermal amplification (LAMP). LAMP primers were designed to amplify the conserved regions of SARS-COV-2 specific genes. The target regions for primer design were selected after genome-wide sequence alignment of SARS-CoV-2 strains isolated in various regions of the world i.e., Europe, Africa, Asia, and North America. RT-LAMP assays were performed at the specific incubation temperature (60°C) for 50 minutes. Assay was optimized as per consumable compatibility, COVID template integrity, primer concentration, template concentration, primer ratio, testing time etc. Sensitivity and specificity of the assay was elucidated. Finally, different end-point analysis i.e., Agarose Gel Electrophoresis and Colorimetry have been used to interpret the results.Conclusion: RT-LAMP assay has shown to be a quick and accurate diagnostic method that can be put to use for SARS-CoV-2 detection in laboratories and Point-of- Care settings. © 2022 IEEE.

The Optimization of ECG-Leads Based Cardiovascular Abnormal Detection Scheme

An electrocardiogram, often known as an ECG, is a diagnostic tool that measures the electrical activity of the heart in order to identify potential heart abnormalities. Although the normal 12-lead ECG is the dominant approach in cardiac diagnostics, it is still challenging to identify distinct heart illnesses using a single lead or a reduced number of leads. Automatic diagnosis of cardiac abnormalities via the ECG with a reduced lead system (less than the typical 12-lead system) may give a helpful diagnostic alternative to traditional 12-lead ECG equipment that is both simple to use and less expensive. This alternative uses fewer leads than the standard system. This study considers the use of Recurrent Neural Networks Long Short-Term Memory (RNN- LSTM) to identify the ability to use less standard ECG leads to detect cardiac abnormalities using various lead combinations, including 6, 4, 3, 2, 1, and 12 lead ECG data. The results of this investigation are presented in this article. Data pre-processing, model design, and hyperparameter tuning are all essential for RNN-LSTM multi-label classification. The initial step was to pre-process the ECG readings to eliminate the base-line wander noise for ECG signals;the next stage is lead combination selection and clipped to have an equal duration of 10 seconds at various used leads. The gathered results show a possibility of using a single lead instead of multiple leads for preliminary cardiovascular diseases (CVDs) identification. It is a critical issue, especially during emergencies such as the COVID- 19 pandemic or in crowded hospitals when medical resources are limited and online (internet-based) monitoring technologies are vital. © 2022 IEEE.

Capsule Networks with Chest X-Ray Enhancement for Detection of COVID-19

Corona virus was declared a global pandemic that has affected people worldwide. It is critical to diagnose corona virus-infected individuals to restrict the virus's transmission. Recent research indicates that radiological methods provide valuable information in identifying infection using deep learning algorithms. Deep learning has contributed to large-scale medical data research, providing new ways and chances for diagnostic tools. This research attempted to investigate how the Capsule Networks leverage chest X-ray scans to identify the infected person. We suggest Capsule Networks identify the illness using chest X-ray data. The proposed approach is rapid and robust, classifying scans into COVID-19, No Findings, or any other issue in the lungs. The study can be used as a preliminary diagnosis by medical practitioners, and the study focuses on the COVID-19 class, a minority class in all public data sets accessible, and ensures that no COVID-19 infected individual is identified as Normal. Even with a small dataset, the model provides 96.37% accuracy for COVID-19 and for the non-COVID-19, and on multi-class classification, it provides an accuracy of 95.12%. © 2022 IEEE.

Transfer Learning for COVID-19 and Pneumonia Detection using Chest X-Rays

Chest X-rays (CXR) images are a useful noninvasive diagnostic tool for assessing various lung diseases. In this paper, we propose transfer learning with a fine-tuning-based model to detect and classify COVID-19 and pneumonia using CXR images to assist the radiologist with diagnosis. One of the difficulties with the medical imaging classification is the limited number of available datasets, and hence training a deep Convolutional Neural Network (CNN) model for medical image classification on a small dataset is challenging. We address this issue by exploiting transfer learning via fine-tuning. In this paper, we use a pre-trained deep CNN model and then fine-tune the layers of the neural network to perform multi-class classification using CXR images. The model is trained to perform multi-class classification, such as two-class (COVID-19 vs normal), three-class (COVID-19 vs Bacterial Pneumonia vs normal), four-class (COVID-19 vs Bacterial Pneumonia vs lung opacity vs normal), and five-class (COVID-19 vs Bacterial Pneumonia vs Viral Pneumonia vs lung opacity vs normal) classification. The performance of the model is evaluated in terms of accuracy, precision, recall, and F1-score. © 2022 IEEE.

Mixup Data Augmentation for COVID-19 Infection Percentage Estimation

The outbreak of the COVID-19 pandemic considerably increased the workload in hospitals. In this context, the availability of proper diagnostic tools is very important in the fight against this virus. Scientific research is constantly making its contribution in this direction. Actually, there are many scientific initiatives including challenges that require to develop deep algorithms that analyse X-ray or Computer Tomography (CT) images of lungs. One of these concerns a challenge whose topic is the prediction of the percentage of COVID-19 infection in chest CT images. In this paper, we present our contribution to the COVID-19 Infection Percentage Estimation Competition organised in conjunction with the ICIAP 2021 Conference. The proposed method employs algorithms for classification problems such as Inception-v3 and the technique of data augmentation mixup on COVID-19 images. Moreover, the mixup methodology is applied for the first time in radiological images of lungs affected by COVID-19 infection, with the aim to infer the infection degree with slice-level precision. Our approach achieved promising results despite the specific constrains defined by the rules of the challenge, in which our solution entered in the final ranking. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Fully autonomous domino capillaric circuit for instrument-free, quantitative detection of SARS-CoV-2 in saliva

Rapid, sensitive, quantitative and patient-friendly diagnostic tools have yet to be developed for COVID-19 continued monitoring at the point-of-care. Here, we present an instrument-free capillary microfluidic chip coupled to a lateral flow module that is compatible with a smartphone application for quantitative detection of SARS-CoV-2 from saliva samples. The microfluidic chip is fully autonomous, and performs aliquoting, sample metering, and sequential delivery of reagents. The limit of detection is 0.07 ng/mL for recombinant nucleocapsid protein in saliva. This rapid antigen test provides results in less than 1 hour, without sacrificing analytical sensitivity. © 2021 MicroTAS 2021 - 25th International Conference on Miniaturized Systems for Chemistry and Life Sciences. All rights reserved.

Characterizing Internet Access and Quality Inequities in California M-Lab Measurements

It is well documented that, in the United States (U.S.), the availability of Internet access is related to several demographic attributes. Data collected through end user network diagnostic tools, such as the one provided by the Measurement Lab (M-Lab) Speed Test, allows the extension of prior work by exploring the relationship between the quality, as opposed to only the availability, of Internet access and demographic attributes of users of the platform. In this study, we use network measurements collected from the users of Speed Test by M-Lab and demographic data to characterize the relationship between the quality-of-service (QoS) metric download speed, and various critical demographic attributes, such as income, education level, and poverty. For brevity, we limit our focus to the state of California. For users of the M-Lab Speed Test, our study has the following key takeaways: (1) geographic type (urban/rural) and income level in an area have the most significant relationship to download speed;(2) average download speed in rural areas is 2.5 times lower than urban areas;(3) the COVID-19 pandemic had a varied impact on download speeds for different demographic attributes;and (4) the U.S. Federal Communication Commission's (FCC's) broadband speed data significantly over-represents the download speed for rural and low-income communities compared to what is recorded through Speed Test. © 2022 Owner/Author.

The Use of an Incremental Learning Algorithm for Diagnosing COVID-19 from Chest X-ray Images

The new Coronavirus or simply Covid-19 causes an acute deadly disease. It has spread rapidly across the world, which has caused serious consequences for health professionals and researchers. This is due to many reasons including the lack of vaccine, shortage of testing kits and resources. Therefore, the main purpose of this study is to present an inexpensive alternative diagnostic tool for the detection of Covid-19 infection by using chest radiographs and Deep Convolutional Neural Network (DCNN) technique. In this paper, we have proposed a reliable and economical solution to detect COVID-19. This will be achieved by using X-rays of patients and an Incremental-DCNN (I-DCNN) based on ResNet-101 architec-ture. The datasets used in this study were collected from publicly available chest radiographs on medical repositories. The proposed I-DCNN method will help in diagnosing the positive Covid-19 patient by utilising three chest X-ray imagery groups, these will be: Covid-19, viral pneumonia, and healthy cases. Furthermore, the main contribution of this paper resides on the use of incremental learning in order to accommodate the detection system. This has high computational energy requirements, time consuming challenges, while working with large-scale and reg-ularly evolving images. The incremental learning process will allow the recognition system to learn new datasets, while keeping the convolutional layers learned pre-viously. The overall Covid-19 detection rate obtained using the proposed I-DCNN was of 98.70% which undeniably can contribute effectively to the detection of COVID-19 infection. © 2022, Brno University of Technology. All rights reserved.

On Data Bias and the Usability of Deep Learning Algorithms in Classifying COVID-19 based on Chest X-ray

SARS-COV-2 is a new strain of virus that was first detected in China. It quickly spread across the world affecting millions of people. For this reason, early detection of the virus is mandatory in order to limit the spread of the virus. Real-time reverse transcription polymerase chain reaction (RT-PCR) and the antibody test are the main tests used to detect the virus. Chest X-rays (CXRs) and computerized tomography (CT) scans are also used to detect the virus although the American college of Radiology does not recommend using medical imaging as a diagnostic tool. Like other medical imaging, convolutional neural networks are used to classify the images. We believe that developing a model to detect COVID-19 has no clinical value regardless of the accuracy achieved since 58% of CXRs seem to be normal. During literature review, several papers with suspicious accuracy of 90% and higher were found. We believe that the dataset used to train and validate the network is biased and is not appropriate for deep learning as any model we train using the same dataset has achieved high accuracy. Our experiments on Cohen's Covid dataset, augmented with Wang dataset, shows that any model trained on Cohen dataset can easily achieve high accuracy. This was further validated with two experienced radiologists who participated in this study were only able to classify 60% as being Covid. Our study highlight the importance of addressing bias in data and developing trustworthy and explainable ML models based on well curated data. © 2021 IEEE.