Detection of Covid From Chest X-Rays Using Gan

Unnikrishnan, M; Lakshman, Gontu Hemant; KhwairakpamBidyanandaSingh,; Pande, Sagar

doi:10.36713/epra10226

Cited by 7 publications

(3 citation statements)

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“…Skin lesions are a common occurrence in clinical practice, and it is crucial to accurately detect and diagnose them for proper patient management. In recent years, the emergence of artificial intelligence (AI) and deep learning techniques has shown great potential in aiding clinical decision making for skin lesion recognition [ 19 , 20 , 21 ]. This literature review provides an overview of relevant studies related to skin problems, including various skin lesions and conditions, as well as studies specifically focused on detecting monkeypox skin lesions.…”

Section: Related Workmentioning

confidence: 99%

A Transfer Learning Approach for Clinical Detection Support of Monkeypox Skin Lesions

et al. 2023

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Monkeypox (MPX) is a disease caused by monkeypox virus (MPXV). It is a contagious disease and has associated symptoms of skin lesions, rashes, fever, and respiratory distress lymph swelling along with numerous neurological distresses. This can be a deadly disease, and the latest outbreak of it has shown its spread to Europe, Australia, the United States, and Africa. Typically, diagnosis of MPX is performed through PCR, by taking a sample of the skin lesion. This procedure is risky for medical staff, as during sample collection, transmission and testing, they can be exposed to MPXV, and this infectious disease can be transferred to medical staff. In the current era, cutting-edge technologies such as IoT and artificial intelligence (AI) have made the diagnostics process smart and secure. IoT devices such as wearables and sensors permit seamless data collection while AI techniques utilize the data in disease diagnosis. Keeping in view the importance of these cutting-edge technologies, this paper presents a non-invasive, non-contact, computer-vision-based method for diagnosis of MPX by analyzing skin lesion images that are more smart and secure compared to traditional methods of diagnosis. The proposed methodology employs deep learning techniques to classify skin lesions as MPXV positive or not. Two datasets, the Kaggle Monkeypox Skin Lesion Dataset (MSLD) and the Monkeypox Skin Image Dataset (MSID), are used for evaluating the proposed methodology. The results on multiple deep learning models were evaluated using sensitivity, specificity and balanced accuracy. The proposed method has yielded highly promising results, demonstrating its potential for wide-scale deployment in detecting monkeypox. This smart and cost-effective solution can be effectively utilized in underprivileged areas where laboratory infrastructure may be lacking.

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Section: Related Workmentioning

confidence: 99%

A Transfer Learning Approach for Clinical Detection Support of Monkeypox Skin Lesions

et al. 2023

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“…Artificial intelligence (AI)-based techniques through virus image processing and analysis could help in diagnosis. With emerging and more reliable AI models in various domains, such models were proposed for medical image analysis to detect virus-related diseases. − Skin diseases like Psoriasis, Vitiligo, Melanoma, Chicken Pox, Ringworm, Lupus, Acne, and Herpes were investigated using deep-learning models . Low-cost image analysis for Herpes Zoster Virus (HZV) detection was proposed using the CNN model with 89.6% accuracy .…”

Section: Review Of the Related Workmentioning

confidence: 99%

Deep and Transfer Learning Approaches for Automated Early Detection of Monkeypox (Mpox) Alongside Other Similar Skin Lesions and Their Classification

Pal,

Mahal,

Mohapatra

et al. 2023

ACS Omega

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The world faces multiple public health emergencies simultaneously, such as . mpox, from being a neglected disease, has emerged as a global threat that has spread to more than 100 nonendemic countries, even as COVID-19 has been spreading for more than 3 years now. The general mpox symptoms are similar to chickenpox and measles, thus leading to a possible misdiagnosis. This study aimed at facilitating a rapid and high-brevity mpox diagnosis. Reportedly, mpox circulates among particular groups, such as sexually promiscuous gay and bisexuals. Hence, selectively vaccinating, isolating, and treating them seems difficult due to the associated social stigma. Deep learning (DL) has great promise in image-based diagnosis and could help in error-free bulk diagnosis. The novelty proposed, the system adopted, and the methods and approaches are discussed in the article. The present work proposes the use of DL models for automated early mpox diagnosis. The performances of the proposed algorithms were evaluated using the data set available in public domain. The data set adopted for the study was meant for both training and testing, the details of which are elaborated. The performances of CNN, VGG19, ResNet 50, Inception v3, and Autoencoder algorithms were compared. It was concluded that CNN, VGG19, and Inception v3 could help in early detection of mpox skin lesions, and Inception v3 returned the best (96.56%) classification accuracy.

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“…With the emerging growth of AI models in various domains such as chest x-ray images [30], fruit image analysis [25], and sentiment analysis [29,24], the AI models for medical image analysis have been proposed for various virusrelated disease detection [33,14]. For instance, Madhavan et al [14] developed a deep learning model (Res-COvNet) based on transfer learning approach for COVID-19 virus detection.…”

Section: Introductionmentioning

confidence: 99%

Monkeypox virus detection using pre-trained deep learning-based approaches

Sitaula¹,

Shahi²

2022

Preprint

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Monkeypox virus is emerging slowly with the decline of COVID-19 virus infections around the world. People are afraid of it, thinking that it would appear as a pandemic like COVID-19. As such, it is crucial to detect them earlier before widespread community transmission. AI-based detection could help identify them at the early stage. In this paper, we aim to compare 13 different pre-trained deep learning (DL) models for the Monkeypox virus detection. For this, we initially fine-tune them with the addition of universal custom layers for all of them and analyse the results using four well-established measures: Precision, Recall, F1-score, and Accuracy. After the identification of the best-performing DL models, we ensemble them to improve the overall performance using a majority voting over the probabilistic outputs obtained from them. We perform our experiments on a publicly available dataset, which results in average Precision, Recall, F1-score, and Accuracy of 85.44%, 85.47%, 85.40%, and 87.13%, respectively with the help of our proposed ensemble approach. These encouraging results, which outperform the state-of-the-art methods, suggest that the proposed approach is applicable to health practitioners for mass screening.

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Detection of Covid From Chest X-Rays Using Gan

Cited by 7 publications

References 0 publications

A Transfer Learning Approach for Clinical Detection Support of Monkeypox Skin Lesions

A Transfer Learning Approach for Clinical Detection Support of Monkeypox Skin Lesions

Deep and Transfer Learning Approaches for Automated Early Detection of Monkeypox (Mpox) Alongside Other Similar Skin Lesions and Their Classification

Monkeypox virus detection using pre-trained deep learning-based approaches

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