PoxNet22: A Fine-Tuned Model for the Classification of Monkeypox Disease Using Transfer Learning

Yasmin, Farhana; Hassan, Md. Mehedi; Hasan, Mahade; Zaman, Sadika; Kaushal, Chetna; El‐Shafai, Walid; Soliman, Naglaa F.

doi:10.1109/access.2023.3253868

Cited by 27 publications

(17 citation statements)

References 39 publications

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“…Yasmin et al used the data augmentation method to avoid any model "overfitting" and classified monkeypox skin lesion using the poxnet22 DL model with 100% accuracy. 21 Ali et al detected and classified mpox skin lesion from a data set that contained the images of measles, chickenpox, and mpox skin lesions. 22 27 and tested the model on ten different CNN models.…”

Section: Review Of the Related Workmentioning

confidence: 99%

“…The model correctly identified mpox with 93.19 and 98.91% accuracies, using original and augmented data sets, respectively. Yasmin et al used the data augmentation method to avoid any model “overfitting” and classified monkeypox skin lesion using the poxnet22 DL model with 100% accuracy . Ali et al detected and classified mpox skin lesion from a data set that contained the images of measles, chickenpox, and mpox skin lesions .…”

Section: Review Of the Related Workmentioning

confidence: 99%

“…Yasmin et al used the data augmentation method to avoid any model “overfitting” and classified monkeypox skin lesion using the poxnet22 DL model with 100% accuracy. 21 Ali et al detected and classified mpox skin lesion from a data set that contained the images of measles, chickenpox, and mpox skin lesions. 22 They implemented three pretrained DL models ResNet50, VGG-16, and Inception v3 to classify mpox skin lesion from the other two and obtained a maximal accuracy of 82.96% with the ResNet50 model.…”

Section: Review Of the Related Workmentioning

confidence: 99%

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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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Section: Review Of the Related Workmentioning

confidence: 99%

Section: Review Of the Related Workmentioning

confidence: 99%

Section: Review Of the Related Workmentioning

confidence: 99%

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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 fastest way to identify monkeypox infection is via skin lesions. A PoxNet22 model has been fine-tuned to classify monkeypox from 3192 images with a precision rate of 100% [ 30 ]. Another work has been done where authors used a mobile application interface to detect monkeypox by simply capturing photos of skin lesion [ 31 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Monkeypox genome mutation analysis using a timeseries model based on long short-term memory

Pathan,

Uddin,

Paul

et al. 2023

PLoS ONE

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Monkeypox is a double-stranded DNA virus with an envelope and is a member of the Poxviridae family’s Orthopoxvirus genus. This virus can transmit from human to human through direct contact with respiratory secretions, infected animals and humans, or contaminated objects and causing mutations in the human body. In May 2022, several monkeypox affected cases were found in many countries. Because of its transmitting characteristics, on July 23, 2022, a nationwide public health emergency was proclaimed by WHO due to the monkeypox virus. This study analyzed the gene mutation rate that is collected from the most recent NCBI monkeypox dataset. The collected data is prepared to independently identify the nucleotide and codon mutation. Additionally, depending on the size and availability of the gene dataset, the computed mutation rate is split into three categories: Canada, Germany, and the rest of the world. In this study, the genome mutation rate of the monkeypox virus is predicted using a deep learning-based Long Short-Term Memory (LSTM) model and compared with Gated Recurrent Unit (GRU) model. The LSTM model shows “Root Mean Square Error” (RMSE) values of 0.09 and 0.08 for testing and training, respectively. Using this time series analysis method, the prospective mutation rate of the 50th patient has been predicted. Note that this is a new report on the monkeypox gene mutation. It is found that the nucleotide mutation rates are decreasing, and the balance between bi-directional rates are maintained.

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“…These objectives offer direction in determining various image features, data providers, and interchangeable patterns with these options. Valan, Zimjonov, and Maçal (2023) report that the efficiency of machine learning is achieved via the application of radiomics features, which become a part of our methodology context [7]. The investigation of a range of dimensions, the pixel spacing of the radiography scans, and the number of series forming the basis of research are planned to shed light on conditions that will help in the diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Advancing Prostate Cancer Diagnostics with Image Masking Techniques in Medical Image Analysis

Rao,

RaviSankar

2024

IJACSA

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Prostate cancer is a prevalent health concern characterized by the abnormal and uncontrolled growth of cells within the prostate gland in men. This research paper outlines a standardized methodology for integrating medical slide images into machine learning algorithms, specifically emphasizing advancing healthcare diagnostics. The methodology involves thorough data collection, exploration, and image analysis, establishing a foundation for future progress in medical image analysis. The study investigates the relationships among image characteristics, data providers, and target variables to reveal patterns conducive to diagnosing medical conditions. Novel background prediction techniques are introduced, highlighting the importance of meticulous data preparation for improved diagnostic accuracy. The results of our research offer insights into dataset characteristics and image dimensions, facilitating the development of machine-learning models for healthcare diagnosis. Through deep learning and statistical analysis, we contribute to the evolving field of prostate cancer detection, showcasing the potential of advanced imaging modalities. This research promises to revolutionize healthcare diagnostics and shape the trajectory of medical image analysis, providing a robust framework for applying machine learning algorithms in the field. The standardized approach presented in this paper aims to enhance the reproducibility and comparability of studies in medical image analysis, fostering advancements in healthcare technology.

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PoxNet22: A Fine-Tuned Model for the Classification of Monkeypox Disease Using Transfer Learning

Cited by 27 publications

References 39 publications

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

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

Monkeypox genome mutation analysis using a timeseries model based on long short-term memory

Advancing Prostate Cancer Diagnostics with Image Masking Techniques in Medical Image Analysis

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