A comparative study of different feature extraction techniques for identifying COVID-19 patients using chest X-rays images

Vyas, Shrasti; Seal, Ayan

doi:10.1109/dasa51403.2020.9317299

Cited by 9 publications

(2 citation statements)

References 28 publications

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“…The literature review highlights the advancements achieved in liver disease detection via the use of ML and DL approaches. Researchers have verified the efficacy of these methods in several facets of liver illness, encompassing fibrosis staging, liver cancer categorization, and the diagnosis of non-alcoholic liver disease [27][28][29], among others. Nevertheless, it is crucial to acknowledge and resolve certain constraints, such as the variability of diseases and the accessibility of standardized information, to enhance the precision and applicability of these models.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Method for Exploring Deep Learning Techniques for Subtyping and Prediction of Liver Disease

Hendi,

Hossain,

Majrashi

et al. 2024

Applied Sciences

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The term “Liver disease” refers to a broad category of disorders affecting the liver. There are a variety of common liver ailments, such as hepatitis, cirrhosis, and liver cancer. Accurate and early diagnosis is an emergent demand for the prediction and diagnosis of liver disease. Conventional diagnostic techniques, such as radiological, CT scan, and liver function tests, are often time-consuming and prone to inaccuracies in several cases. An application of machine learning (ML) and deep learning (DL) techniques is an efficient approach to diagnosing diseases in a wide range of medical fields. This type of machine-related learning can handle various tasks, such as image recognition, analysis, and classification, because it helps train large datasets and learns to identify patterns that might not be perceived by humans. This paper is presented here with an evaluation of the performance of various DL models on the estimation and subtyping of liver ailment and prognosis. In this manuscript, we propose a novel approach, termed CNN+LSTM, which is an integration of convolutional neural network (CNN) and long short-term memory (LSTM) networks. The results of the study prove that ML and DL can be used to improve the diagnosis and prognosis of liver disease. The CNN+LSTM model achieves a better accuracy of 98.73% compared to other models such as CNN, Recurrent Neural Network (RNN), and LSTM. The incorporation of the proposed CNN+LSTM model has better results in terms of accuracy (98.73%), precision (99%), recall (98%), F1 score (98%), and AUC (Area Under the Curve)-ROC (Receiver Operating Characteristic) (99%), respectively. The use of the CNN+LSTM model shows robustness in predicting the liver ailment with an accurate diagnosis and prognosis.

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Section: Related Workmentioning

confidence: 99%

Adaptive Method for Exploring Deep Learning Techniques for Subtyping and Prediction of Liver Disease

Hendi,

Hossain,

Majrashi

et al. 2024

Applied Sciences

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“…Because COVID-19 medical images were still limited in 2021, prior studies often focused on demonstrating the feasibility of deep learning models in distinguishing COVID-19 cases from normal subjects or patients with other respiratory diseases, such as pneumonia [14][15][16]. These studies either used chest X-ray images or CT scans [17][18][19][20][21][22] and were designed as either binary or multi-class classifications [23,24]. In general, the model classification accuracy was higher using chest X-ray images (90%+) than using CT (~85%) and was higher (1-5%) in binary than multi-class classifications [10,25,26].…”

Section: Introductionmentioning

confidence: 99%

Multi-Level Training and Testing of CNN Models in Diagnosing Multi-Center COVID-19 and Pneumonia X-ray Images

Talaat,

Si,

2023

Applied Sciences

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This study aimed to address three questions in AI-assisted COVID-19 diagnostic systems: (1) How does a CNN model trained on one dataset perform on test datasets from disparate medical centers? (2) What accuracy gains can be achieved by enriching the training dataset with new images? (3) How can learned features elucidate classification results, and how do they vary among different models? To achieve these aims, four CNN models—AlexNet, ResNet-50, MobileNet, and VGG-19—were trained in five rounds by incrementally adding new images to a baseline training set comprising 11,538 chest X-ray images. In each round, the models were tested on four datasets with decreasing levels of image similarity. Notably, all models showed performance drops when tested on datasets containing outlier images or sourced from other clinics. In Round 1, 95.2~99.2% accuracy was achieved for the Level 1 testing dataset (i.e., from the same clinic but set apart for testing only), and 94.7~98.3% for Level 2 (i.e., from an external clinic but similar). However, model performance drastically decreased for Level 3 (i.e., outlier images with rotation or deformation), with the mean sensitivity plummeting from 99% to 36%. For the Level 4 testing dataset (i.e., from another clinic), accuracy decreased from 97% to 86%, and sensitivity from 99% to 67%. In Rounds 2 and 3, adding 25% and 50% of the outlier images to the training dataset improved the average Level-3 accuracy by 15% and 23% (i.e., from 56% to 71% to 83%). In Rounds 4 and 5, adding 25% and 50% of the external images increased the average Level-4 accuracy from 81% to 92% and 95%, respectively. Among the models, ResNet-50 demonstrated the most robust performance across the five-round training/testing phases, while VGG-19 persistently underperformed. Heatmaps and intermediate activation features showed visual correlations to COVID-19 and pneumonia X-ray manifestations but were insufficient to explicitly explain the classification. However, heatmaps and activation features at different rounds shed light on the progression of the models’ learning behavior.

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A Deep Convolution Neural Networks Framework for Analyzing Electroencephalography Signals in Neuromarketing

Vyas

Seal

2022

Lecture Notes in Networks and Systems

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A comparative study of different feature extraction techniques for identifying COVID-19 patients using chest X-rays images

Cited by 9 publications

References 28 publications

Adaptive Method for Exploring Deep Learning Techniques for Subtyping and Prediction of Liver Disease

Adaptive Method for Exploring Deep Learning Techniques for Subtyping and Prediction of Liver Disease

Multi-Level Training and Testing of CNN Models in Diagnosing Multi-Center COVID-19 and Pneumonia X-ray Images

A Deep Convolution Neural Networks Framework for Analyzing Electroencephalography Signals in Neuromarketing

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