Comparison of Convolutional Neural Networks in SARS-CoV-2 Identification DOI

Khokhoni Innocentia Mpho Ramaphosa,

Tranos Zuva, Temidayo Otunniyi

et al.

Published: March 15, 2024

Severe Acute Respiratory Syndrome SARS-CoV-2 is a global pandemic that has resulted in numerous fatalities and affected millions of people worldwide. The community been experiencing conditions resembling lockdowns due to the COVID-19 pandemic. This public health crisis posed significant challenge for scientists, researchers, healthcare professionals worldwide, extending from virus's detection its treatment. Healthcare would greatly benefit technological tool enables swift precise screening infections. Prompt recognition this specific virus can contribute easing burden on systems. X-rays have demonstrated their significance pinpointing ailments like Pneumonia. notable advancements achieved realm Machine Learning (ML) paved way development artificial intelligent systems proficient differentiating between cases those considered normal. latter contributed by Deep (DL) advancements. research utilizes advanced deep learning methods, particularly training CNN models using Python programming language. Its primary aim differentiate chest X-ray images patients which were used are VGG19, Xception VGG16. dataset incorporated was 400 normal 399 images. performance metric employed classification accuracy. Remarkably, VGG19 model outperformed others with highest accuracy 99%. VGG16 97%, while lowest at 96%. above results prove holds promising

Language: Английский

Automated detection and forecasting of COVID-19 using deep learning techniques: A review DOI
Afshin Shoeibi, Marjane Khodatars, Mahboobeh Jafari

et al.

Neurocomputing, Journal Year: 2024, Volume and Issue: 577, P. 127317 - 127317

Published: Jan. 26, 2024

Language: Английский

Citations

53
Fusion of transfer learning models with LSTM for detection of breast cancer using ultrasound images DOI
Madhusudan G. Lanjewar, Kamini G. Panchbhai, L. B. Patle

et al.

Computers in Biology and Medicine, Journal Year: 2024, Volume and Issue: 169, P. 107914 - 107914

Published: Jan. 4, 2024

Language: Английский

Citations

25
CNN and transfer learning methods with augmentation for citrus leaf diseases detection using PaaS cloud on mobile DOI
Madhusudan G. Lanjewar, Jivan S. Parab

Multimedia Tools and Applications, Journal Year: 2023, Volume and Issue: 83(11), P. 31733 - 31758

Published: Sept. 19, 2023

Language: Английский

Citations

20
Comparative Evaluation of Deep Learning Models for Diagnosis of COVID-19 Using X-ray Images and Computed Tomography DOI Creative Commons
Aroldo Ferraz, Roberto Cesar Betini

Journal of the Brazilian Computer Society, Journal Year: 2025, Volume and Issue: 31(1), P. 99 - 131

Published: Feb. 20, 2025

(1) Background: The COVID-19 pandemic is an unprecedented global challenge, having affected more than 776.79 million people, with over 7.07 deaths recorded since 2020. application of Deep Learning (DL) in diagnosing through chest X-rays and computed tomography (CXR CT) has proven promising. While CNNs have been effective, models such as the Vision Transformer Swin emerged promising solutions this field. (2) Methods: This study investigated performance like ResNet50, Transformer, Transformer. We utilized Bayesian Optimization (BO) diagnosis CXR CT based on four distinct datasets: COVID-QU-Ex, HCV-UFPR-COVID-19, HUST-19, SARS-COV-2 Ct-Scan Dataset. found that, although all tested achieved commendable metrics, stood out. Its unique architecture provided greater generalization power, especially cross-dataset evaluation (CDE) tasks, where it was trained one dataset another. (3) Results: Our approach aligns state-of-the-art (SOTA) methods, even complex tasks CDE. On some datasets, we exceptional AUC, Accuracy, Precision, Recall, F1-Score values 1. (4) Conclusion: Results obtained by go beyond what offered current SOTA methods indicate actual feasibility for medical diagnostic scenarios. robustness power demonstrated across different encourage future exploration adoption clinical settings.

Language: Английский

Citations

0
Small size CNN-Based COVID-19 Disease Prediction System using CT scan images on PaaS cloud DOI
Madhusudan G. Lanjewar, Kamini G. Panchbhai, Charanarur Panem

et al.

Multimedia Tools and Applications, Journal Year: 2024, Volume and Issue: 83(21), P. 60655 - 60687

Published: Jan. 3, 2024

Language: Английский

Citations

3
Hybrid methods for detection of starch in adulterated turmeric from colour images DOI
Madhusudan G. Lanjewar,

Satyam S. Asolkar,

Jivan S. Parab

et al.

Multimedia Tools and Applications, Journal Year: 2024, Volume and Issue: 83(25), P. 65789 - 65814

Published: Jan. 19, 2024

Language: Английский

Citations

3
Cloud-based COVID-19 disease prediction system from X-Ray images using convolutional neural network on smartphone DOI Open Access
Madhusudan G. Lanjewar,

Arman Yusuf Shaikh,

Jivan S. Parab

et al.

Multimedia Tools and Applications, Journal Year: 2022, Volume and Issue: 82(19), P. 29883 - 29912

Published: Nov. 24, 2022

Language: Английский

Citations

9
Advancing differential diagnosis: a comprehensive review of deep learning approaches for differentiating tuberculosis, pneumonia, and COVID-19 DOI
Kajal Kansal, Tej Bahadur Chandra, Akansha Singh

et al.

Multimedia Tools and Applications, Journal Year: 2024, Volume and Issue: unknown

Published: May 27, 2024

Language: Английский

Citations

0
Comparison of Convolutional Neural Networks in SARS-CoV-2 Identification DOI

Khokhoni Innocentia Mpho Ramaphosa,

Tranos Zuva, Temidayo Otunniyi

et al.

Published: March 15, 2024

Severe Acute Respiratory Syndrome SARS-CoV-2 is a global pandemic that has resulted in numerous fatalities and affected millions of people worldwide. The community been experiencing conditions resembling lockdowns due to the COVID-19 pandemic. This public health crisis posed significant challenge for scientists, researchers, healthcare professionals worldwide, extending from virus's detection its treatment. Healthcare would greatly benefit technological tool enables swift precise screening infections. Prompt recognition this specific virus can contribute easing burden on systems. X-rays have demonstrated their significance pinpointing ailments like Pneumonia. notable advancements achieved realm Machine Learning (ML) paved way development artificial intelligent systems proficient differentiating between cases those considered normal. latter contributed by Deep (DL) advancements. research utilizes advanced deep learning methods, particularly training CNN models using Python programming language. Its primary aim differentiate chest X-ray images patients which were used are VGG19, Xception VGG16. dataset incorporated was 400 normal 399 images. performance metric employed classification accuracy. Remarkably, VGG19 model outperformed others with highest accuracy 99%. VGG16 97%, while lowest at 96%. above results prove holds promising

Language: Английский

Citations

0