scholarly journals Deep Learning in the Detection and Diagnosis of COVID-19 Using Radiology Modalities: A Systematic Review

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Mustafa Ghaderzadeh ◽  
Farkhondeh Asadi
Keyword(s):  
Systematic Review ◽  
Deep Learning ◽  
Diagnostic Methods ◽  
Efficient System ◽  
Current State ◽  
Radiological Images ◽  
Research Population ◽  
Radiologic Image ◽  
Detection And Diagnosis ◽  
Early Detection And Diagnosis

Introduction. The early detection and diagnosis of COVID-19 and the accurate separation of non-COVID-19 cases at the lowest cost and in the early stages of the disease are among the main challenges in the current COVID-19 pandemic. Concerning the novelty of the disease, diagnostic methods based on radiological images suffer from shortcomings despite their many applications in diagnostic centers. Accordingly, medical and computer researchers tend to use machine-learning models to analyze radiology images. Material and Methods. The present systematic review was conducted by searching the three databases of PubMed, Scopus, and Web of Science from November 1, 2019, to July 20, 2020, based on a search strategy. A total of 168 articles were extracted and, by applying the inclusion and exclusion criteria, 37 articles were selected as the research population. Result. This review study provides an overview of the current state of all models for the detection and diagnosis of COVID-19 through radiology modalities and their processing based on deep learning. According to the findings, deep learning-based models have an extraordinary capacity to offer an accurate and efficient system for the detection and diagnosis of COVID-19, the use of which in the processing of modalities would lead to a significant increase in sensitivity and specificity values. Conclusion. The application of deep learning in the field of COVID-19 radiologic image processing reduces false-positive and negative errors in the detection and diagnosis of this disease and offers a unique opportunity to provide fast, cheap, and safe diagnostic services to patients.

Early detection and diagnosis using deep learning

2021 ◽  
pp. 191-217
Author(s):  
Deepak Kumar Sharma ◽  
Saakshi Bhargava ◽  
Aashna Jha ◽  
Pawan Singh
Keyword(s):  
Deep Learning ◽  
Early Detection ◽  
Detection And Diagnosis ◽  
Early Detection And Diagnosis

scholarly journals A Novel System for the Identification of Diabetic Retinopathy using Computer Based Statistical Classification

2021 ◽  
Vol 12 (3) ◽  
pp. 4044-4059
Author(s):  
Sunil S S Et.al
Keyword(s):  
Deep Learning ◽  
Learning Strategy ◽  
Early Stage ◽  
Diabetic Patients ◽  
Statistical Classification ◽  
Computer Based ◽  
Detection And Diagnosis ◽  
Different Characteristics ◽  
Early Detection And Diagnosis

Diabetes Retinopathy (DR) is an eye disorder that affects the human retina due to increased insulin levels in the blood. Early detection and diagnosis of DR is essential in the optimal treatment of diabetic patients. The current research is to develop controls for identifying different characteristics and differences in colour  retina and using different classifications. This therapeutic approach describes diabetes recovery from data collected from multiple fields including DRIDB0, DRIDB1, MESSIDOR, STARE and HRF. Here  machine learning, neural networks and deep learning algorithms issues are addressed with related topics such as Sensitivity, Precision, Accuracy, Error,   Specificity and F1-score, Mathews Correlation Coefficient (MCC) and coefficient of kappa are compared. Finally due to the deep learning strategy the results were more effective compared to other methods. The system can help ophthalmologists, to identify the symptoms of diabetes at an early stage, for better treatment and to improve the quality of life biology.

scholarly journals Efficient Deep Learning Model for COVID-19 Detection in large CT images datasets: A cross-dataset analysis

2020 ◽  
Author(s):  
Pedro Silva ◽  
Eduardo Luz ◽  
Guilherme Silva ◽  
Gladston Moreira ◽  
Rodrigo Silva ◽  
...  
Keyword(s):  
Deep Learning ◽  
Data Augmentation ◽  
State Of The Art ◽  
Ct Images ◽  
Critical Factors ◽  
Specific Data ◽  
Dataset Analysis ◽  
Detection And Diagnosis ◽  
Deep Learning Model ◽  
Early Detection And Diagnosis

Abstract Early detection and diagnosis are critical factors to control the COVID-19 spreading. A number of deep learning-based methodologies have been recently proposed for COVID-19 screening in CT scans as a tool to automate and help with the diagnosis. To achieve these goals, in this work, we propose a slice voting-based approach extending the EfficientNet Family of deep artificial neural networks.We also design a specific data augmentation process and transfer learning for such task.Moreover, a cross-dataset study is performed into the two largest datasets to date. The proposed method presents comparable results to the state-of-the-art methods and the highest accuracy to date on both datasets (accuracy of 87.60\% for the COVID-CT dataset and accuracy of 98.99% for the SARS-CoV-2 CT-scan dataset). The cross-dataset analysis showed that the generalization power of deep learning models is far from acceptable for the task since accuracy drops from 87.68% to 56.16% on the best evaluation scenario.These results highlighted that the methods that aim at COVID-19 detection in CT-images have to improve significantly to be considered as a clinical option and larger and more diverse datasets are needed to evaluate the methods in a realistic scenario.

scholarly journals Machine Learning in Detection and Classification of Leukemia Using Smear Blood Images: A Systematic Review

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Mustafa Ghaderzadeh ◽  
Farkhondeh Asadi ◽  
Azamossadat Hosseini ◽  
Davood Bashash ◽  
Hassan Abolghasemi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Lymphoblastic Leukemia ◽  
Peripheral Blood Smear ◽  
Disease Diagnosis ◽  
Diagnostic Methods ◽  
Study Results ◽  
The Status ◽  
Detection And Diagnosis ◽  
Learning Machine

Introduction. The early detection and diagnosis of leukemia, i.e., the precise differentiation of malignant leukocytes with minimum costs in the early stages of the disease, is a major problem in the domain of disease diagnosis. Despite the high prevalence of leukemia, there is a shortage of flow cytometry equipment, and the methods available at laboratory diagnostic centers are time-consuming. Motivated by the capabilities of machine learning (machine learning (ML)) in disease diagnosis, the present systematic review was conducted to review the studies aiming to discover and classify leukemia by using machine learning. Methods. A systematic search in four databases (PubMed, Scopus, Web of Science, and ScienceDirect) and Google Scholar was performed via a search strategy using Machine Learning (ML), leukemia, peripheral blood smear (PBS) image, detection, diagnosis, and classification as the keywords. Initially, 116 articles were retrieved. After applying the inclusion and exclusion criteria, 16 articles remained as the population of the study. Results. This review study presents a comprehensive and systematic view of the status of all published ML-based leukemia detection and classification models that process PBS images. The average accuracy of the ML methods applied in PBS image analysis to detect leukemia was >97%, indicating that the use of ML could lead to extraordinary outcomes in leukemia detection from PBS images. Among all ML techniques, deep learning (DL) achieved higher precision and sensitivity in detecting different cases of leukemia, compared to its precedents. ML has many applications in analyzing different types of leukemia images, but the use of ML algorithms to detect acute lymphoblastic leukemia (ALL) has attracted the greatest attention in the fields of hematology and artificial intelligence. Conclusion. Using the ML method to process leukemia smear images can improve accuracy, reduce diagnosis time, and provide faster, cheaper, and safer diagnostic services. In addition to the current diagnostic methods, clinical and laboratory experts can also adopt ML methods in laboratory applications and tools.

scholarly journals Early-Stage Detection of Cancer in Breast Using Artificial Intelligence

2021 ◽  
Vol 11 (2) ◽  
pp. 2016-2028
Author(s):  
M.N. Vimal Kumar ◽  
S. Aakash Ram ◽  
C. Shobana Nageswari ◽  
C. Raveena ◽  
S. Rajan
Keyword(s):  
Breast Cancer ◽  
Artificial Intelligence ◽  
Deep Learning ◽  
Learning Algorithm ◽  
Early Stage ◽  
Deep Learning Algorithm ◽  
Common Cancer ◽  
Abnormal Cells ◽  
Detection And Diagnosis ◽  
Early Detection And Diagnosis

One of the deadly diseases among humans is Cancer, which occurs almost anywhere in the human body. Cancer is caused by the cells that spread into the surrounding tissues by dividing itself uncontrollably. Breast Cancer is the most common cancer among women. Early detection and diagnosis of breast cancer are treatable and curable. Many women have no symptoms for this cancer at an early stage. The abnormal cells in the breast will risk for the development of breast cancer. So, it is important for women to regularly examine their breast. Technologies can be utilized in a smarter way with Artificial Intelligence techniques to assist the women during their examination of the breast at their living place to avoid the risk of breast cancer. The main aim is to develop a lowcost self-examining device for the detection of breast cancer and abnormality in the breast using an efficient optical method, Deep-learning algorithm and Internet of Things.

Artificial Intelligence in Healthcare

2021 ◽  
Vol 9 (VIII) ◽  
pp. 309-311
Author(s):  
Shrey Bhagat
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Medical Specialties ◽  
Current State ◽  
Wide Range ◽  
Care Information ◽  
Structured Knowledge ◽  
Detection And Diagnosis ◽  
Early Detection And Diagnosis

Artificial intelligence aspires to imitate the psychological functions of humans. It ushers in a perfect change in care, fueled by the rising accessibility of care information and the rapid advancement of analytics approaches. We prefer to assess the current state of AI applications in healthcare and speculate on their future. AI is being used to apply a wide range of care expertise. Machine learning algorithms for structured knowledge, such as the traditional support vector machine and neural network, and therefore the popular deep learning, as well as the tongue process for unstructured knowledge, are typical AI approaches. Cancer, neurology, medical specialties, and strokes are all major disease areas that employ AI technologies. We therefore go over AI applications in stroke in more depth, focusing on the three key areas of early detection and diagnosis, as well as outcome prediction and prognosis analysis.

Detection of Leukemia Using Image Processing

2018 ◽  
Vol 8 (4) ◽  
pp. 61
Author(s):  
A. Sivasangari ◽  
G. Sasikumar
Keyword(s):  
Image Processing ◽  
Statistical Analysis ◽  
Early Detection ◽  
Visual Inspection ◽  
Causes Of Death ◽  
Cure Rate ◽  
Blood Disorders ◽  
Detection And Diagnosis ◽  
The Moment ◽  
Early Detection And Diagnosis

Leukemia   disease   is one   of    the   leading   causes   of death   among   human. Its  cure  rate and  prognosis   depends   mainly   on  the  early  detection   and  diagnosis  of   the  disease. At  the  moment, identification  of  blood  disorders  is  through   visual  inspection  of  microscopic  images  by  examining  changes  like  texture, geometry, colour  and   statistical  analysis  of  images . This  project  aims  to  preliminary  of  developing  a  detection  of  leukemia  types  using   microscopic  blood  sample using MATLAB. Images  are  used  as  they  are  cheap  and  do  not  expensive  for testing  and  lab  equipment.

Intersection of smoking, e-cigarette use, obesity, and metabolic and bariatric surgery: a systematic review of the current state of evidence

2021 ◽  
pp. 1-19
Author(s):  
Nimisha Srikanth ◽  
Luyu Xie ◽  
Elisa Morales-Marroquin ◽  
Ashley Ofori ◽  
Nestor de la Cruz-Muñoz ◽  
...  
Keyword(s):  
Systematic Review ◽  
Bariatric Surgery ◽  
Cigarette Use ◽  
Current State ◽  
Metabolic And Bariatric Surgery
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