Açıklanabilir Yapay Zeka Destekli Evrişimsel Sinir Ağları Kullanılarak Maymun Çiçeği Deri Lezyonunun Sınıflandırılması

AKIN, Korhan Deniz; GURKAN, Caglar; Budak, Abdulkadir; Karataş, Hakan

doi:10.31590/ejosat.1171816

Cited by 9 publications

(6 citation statements)

References 3 publications

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“…Before starting this study, we conducted a comprehensive search on the Web of Science using the terms “monkeypox” and “deep learning” and found 13 relevant references. [ 16–28 ]…”

Section: Motivationmentioning

confidence: 99%

Ultrafast‐and‐Ultralight ConvNet‐Based Intelligent Monitoring System for Diagnosing Early‐Stage Mpox Anytime and Anywhere

Yue,

Shi,

Qin

et al. 2024

Advanced Intelligent Systems

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Due to the absence of more efficient diagnostic tools, the spread of mpox continues to be unchecked. Although related studies have demonstrated the high efficiency of deep learning models in diagnosing mpox, key aspects such as model inference speed and parameter size have always been overlooked. Herein, an ultrafast and ultralight network named Fast‐MpoxNet is proposed. Fast‐MpoxNet, with only 0.27 m parameters, can process input images at 68 frames per second (FPS) on the CPU. To detect subtle image differences and optimize model parameters better, Fast‐MpoxNet incorporates an attention‐based feature fusion module and a multiple auxiliary losses enhancement strategy. Experimental results indicate that Fast‐MpoxNet, utilizing transfer learning and data augmentation, produces 98.40% classification accuracy for four classes on the mpox dataset. Furthermore, its Recall for early‐stage mpox is 93.65%. Most importantly, an application system named Mpox‐AISM V2 is developed, suitable for both personal computers and smartphones. Mpox‐AISM V2 can rapidly and accurately diagnose mpox and can be easily deployed in various scenarios to offer the public real‐time mpox diagnosis services. This work has the potential to mitigate future mpox outbreaks and pave the way for developing real‐time diagnostic tools in the healthcare field.

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“…Before starting this study, we conducted a comprehensive search on the Web of Science using the terms “monkeypox” and “deep learning” and found 13 relevant references. [ 16–28 ]…”

Section: Motivationmentioning

confidence: 99%

Ultrafast‐and‐Ultralight ConvNet‐Based Intelligent Monitoring System for Diagnosing Early‐Stage Mpox Anytime and Anywhere

Yue,

Shi,

Qin

et al. 2024

Advanced Intelligent Systems

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“…True positive + True negative All cases (1) Precision is a metric that calculates the number of cases in the positive that were estimated precisely. Equation ( 2) is the precision formula which is showed below.…”

Section: Accuracy =mentioning

confidence: 99%

“…It is called monkeypox. Monkeypox is a disease that given rise to by the monkeypox virus [1]. Monkeypox can be transmitted both from animal to human and human to human [2].…”

Section: Introductionmentioning

confidence: 99%

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Utilizing the Ensemble of Deep Learning Approaches to Identify Monkeypox Disease

2023

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Recently, the monkeypox disease spreads to many countries rapidly and it becomes a serious health problem. In addition, this disease affects the quality of a person's life. Therefore, it is crucial to decrease the spread rate with the quick determination of the disease. In order to identify monkeypox rapidly, deep learning models are used. They are named EfficientNetB3, ResNet50, and InceptionV3 respectively. According to the results of the three models, ResNet50 is the best model when they compare aspects of performance. The accuracy of ResNet50 sets %94.00. There are four parameters that are used to evaluate the performance of the models. There are called precision, recall, f1-score, and support. These models demonstrate that monkeypox can be classified with high precision. Therefore these models can be used for the future of the work.

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“…Although monkeypox is more closely related to smallpox in terms of clinical manifestations, the rash and skin lesions can resemble those of cowpox and chickenpox [7]. Its early stages are also difficult to detect due to its striking resemblance to chickenpox and measles.…”

Section: Introductionmentioning

confidence: 99%

Monkeypox Detection using CSA Based K-Means Clustering with Swin Transformer Model

et al. 2024

JMC

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Despite the global COVID-19 pandemic, public health professionals are also concerned about a possible new monkeypox epidemic. Similar to vaccinia, cowpox, and variola, the orthopoxvirus that causes monkeypox has two strands that are double-stranded. Many people have propagated the current pandemic through sexual means, particularly those who identify as bisexual or gay. The speed with which monkeypox was detected is the most important element here. In order to catch monkeypox before it infects more people, machine learning could be a huge help in making a quick and accurate diagnosis. Finding a solution is the driving force behind this project, which aims to develop a model for detecting monkeypox using deep learning and image processing. For optimal cluster selection during photo segmentation, the Chameleon Swarm Algorithm (CSA) employs K-means clustering. Examining the accuracy with which the Swin Transformer model identified instances of monkeypox was the driving force for this study. The proposed techniques are evaluated on two datasets: Kaggle Monkeypox Skin Lesion Dataset (MSLD) besides the Monkeypox Skin Image Dataset (MSID). We assessed the outcomes of various deep learning models using sensitivity, specificity, and balanced accuracy. Positive results from the projected process raise the possibility of its widespread application in monkeypox detection. This ingenious and cheap method can be put to good use in economically deprived communities that may not have access to proper laboratory facilities.

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Açıklanabilir Yapay Zeka Destekli Evrişimsel Sinir Ağları Kullanılarak Maymun Çiçeği Deri Lezyonunun Sınıflandırılması

Cited by 9 publications

References 3 publications

Ultrafast‐and‐Ultralight ConvNet‐Based Intelligent Monitoring System for Diagnosing Early‐Stage Mpox Anytime and Anywhere

Ultrafast‐and‐Ultralight ConvNet‐Based Intelligent Monitoring System for Diagnosing Early‐Stage Mpox Anytime and Anywhere

Utilizing the Ensemble of Deep Learning Approaches to Identify Monkeypox Disease

Monkeypox Detection using CSA Based K-Means Clustering with Swin Transformer Model

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