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LiMS-Net: A Lightweight Multi-Scale CNN for COVID-19 Detection from Chest CT Scans
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2023 | ACM Transactions on Management Information Systems | Citations: 0
Authors: Joshi, Amogh Manoj; Nayak, Deepak Ranjan; Das, Dibyasundar; Zhang, Yudong

Abstract: Recent years have witnessed a rise in employing deep learning methods, especiall ... Expand

Abstract: Recent years have witnessed a rise in employing deep learning methods, especially convolutional neural networks (CNNs) for detection of COVID-19 cases using chest CT scans. Most of the state-of-the-art models demand a huge amount of parameters which often suffer from overfitting in the presence of limited training samples such as chest CT data and thereby, reducing the detection performance. To handle these issues, in this paper, a lightweight multi-scale CNN called LiMS-Net is proposed. The LiMS-Net contains two feature learning blocks where, in each block, filters of different sizes are applied in parallel to derive multi-scale features from the suspicious regions and an additional filter is subsequently employed to capture discriminant features. The model has only 2.53M parameters and therefore, requires low computational cost and memory space when compared to pretrained CNN architectures. Comprehensive experiments are carried out using a publicly available COVID-19 CT dataset and the results demonstrate that the proposed model achieves higher performance than many pretrained CNN models and state-of-the-art methods even in the presence of limited CT data. Our model achieves an accuracy of 92.11% and an F1-score of 92.59% for detection of COVID-19 from CT scans. Further, the results on a relatively larger CT dataset indicate the effectiveness of the proposed model. Collapse

Semantic filters: ImageNet covid 19

Topics: covid 19

Methods: computed tomography ImageNet ablation analysis experiment machine learning
Classification of COVID-19 Cases: The Customized Deep Convolutional Neural Network and Transfer Learning Approach

2022 | Americas Conference on Information Systems | Citations: 0
Authors: Vyas, Piyush; Ragothaman, Kaushik Nagarajan Muthusamy; Chauhan, Akhilesh; Rimal, Bhaskar P.

Abstract: The recent advancements under the umbrella of artificial intelligence (AI) open ... Expand

Abstract: The recent advancements under the umbrella of artificial intelligence (AI) open opportunities to tackle complex problems related to image analysis. Recently, the proliferation of COVID-19 brought multiple challenges to medical practitioners, such as precise analysis and classification of COVID-19 cases. Deep learning (DL) and transfer learning (TL) techniques appear to be attractive solutions. To provide the precise classification of COVID-19 cases, this article presents a customized Deep Convolutional Neural Network (DCNN) and pre-trained TL model approach. Our pipeline accommodated several popular pre-trained TL models, namely DenseNet121, ResNet50, InceptionV3, EfficientNetB0, and VGG16, to classify COVID-19 positive and negative cases. We evaluated and compared the performance of these models with a wide range of measures, including accuracy, precision, recall, and F1 score for classifying COVID-19 cases based on chest X-rays. The results demonstrate that our customized DCNN model performed well with randomly assigned weights, achieving 98.5% recall and 97.0% accuracy. KeywordsCovid19, transfer learning, deep convolutional neural network, medical imaging. Collapse

Semantic filters: ImageNet covid 19

Topics: Google Cloud Platform Google+ covid 19

Methods: machine learning ImageNet deep learning convolutional neural network literature study
COVID_SCREENET: COVID-19 Screening in Chest Radiography Images Using Deep Transfer Stacking
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2021 | Information Systems Frontiers | Citations: 0
Authors: Elakkiya, R.; Vijayakumar, Pandi; Karuppiah, Marimuthu

Abstract: Infectious diseases are highly contagious due to rapid transmission and very cha ... Expand

Abstract: Infectious diseases are highly contagious due to rapid transmission and very challenging to diagnose in the early stage. Artificial Intelligence and Machine Learning now become a strategic weapon in assisting infectious disease prevention, rapid-response in diagnosis, surveillance, and management. In this paper, a bifold COVID_SCREENET architecture is introduced for providing COVID-19 screening solutions using Chest Radiography (CR) images. Transfer learning using nine pre-trained ImageNet models to extract the features of Normal, Pneumonia, and COVID-19 images is adapted in the first fold and classified using baseline Convolutional Neural Network (CNN). A Modified Stacked Ensemble Learning (MSEL) is proposed in the second fold by stacking the top five pre-trained models, and then the predictions resulted. Experimentation is carried out in two folds: In first fold, open-source samples are considered and in second fold 2216 real-time samples collected from Tamilnadu Government Hospitals, India, and the screening results for COVID data is 100% accurate in both the cases. The proposed approach is also validated and blind reviewed with the help of two radiologists at Thanjavur Medical College & Hospitals by collecting 2216 chest X-ray images between the month of April and May. Based on the reports, the measures are calculated for COVID_SCREENET and it showed 100% accuracy in performing multi-class classification. Collapse

Semantic filters: ImageNet covid 19

Topics: covid 19 spreadsheet open source online learning system

Methods: ensemble learning ImageNet meta learning machine learning computational algorithm
Applications of Deep Learning Augmented Systems for Covid-19 Predictions- A Literature Review

2021 | Americas Conference on Information Systems | Citations: 0
Authors: Noteboom, Cherie; Zeng, David; Godasu, Rajesh; Sutrave, Kruttika

Abstract: Covid-19 Diagnosis needs new Information Systems technologies as Deep learning ... Expand

Abstract: Covid-19 Diagnosis needs new Information Systems technologies as Deep learning methods, especially in medical image screening. We aim to review the applications of deep learning augmented systems in Covid-19 predictions with the help of a large literature collection from four major databases IEEE explore, ACM, Web of Science, and PubMed. We have identified three major research themes from the current literature, Image Classification, Image segmentation, and evaluation methods for DL models. Among the DL techniques, Transfer Learning is identified as the most popular method for different tasks on Chest X-rays and CT scans. Pre-trained models such as ResNet, VGG, DenseNet, and UNet are widely used in the covid-19 diagnosis. While these models are pre-trained on natural images, a Chest X-ray image pre-trained model CheXnet is gaining popularity in Covid-19 image tasks helping in improving accuracies of classifications. Collapse

Semantic filters: ImageNet covid 19

Topics: pandemic artificial intelligence privacy database system decision making

Methods: natural language processing literature sample residual neural network meta analysis federated learning

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Found 4 articles

LiMS-Net: A Lightweight Multi-Scale CNN for COVID-19 Detection from Chest CT Scans

Classification of COVID-19 Cases: The Customized Deep Convolutional Neural Network and Transfer Learning Approach

COVID_SCREENET: COVID-19 Screening in Chest Radiography Images Using Deep Transfer Stacking

Applications of Deep Learning Augmented Systems for Covid-19 Predictions- A Literature Review