Convolutional Neural Network for Monkeypox Detection

Alcalá-Rmz, Vanessa; Villagrana-Bañuelos, Karen E.; Celaya-Padilla, José M.; Galván-Tejada, Jorge I.; Gamboa-Rosales, Hamurabi; Galván-Tejada, Carlos E.

doi:10.1007/978-3-031-21333-5_9

Cited by 12 publications

(10 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This is obvious as MonDiaL-CAD attained higher accuracy than most previous CADs. This is because they employed individual CNNs like, 3 , 36 , 42 , 45 , 52 did not perform feature selection, 12 , 42 , 45 , 52 or did not employ ensemble learning. 3 , 30 , 36 , 42 , 45 , 52 However, MonDiaL-CAD uses deep features from three CNNs.…”

Section: Discussionmentioning

confidence: 99%

“…This is because they employed individual CNNs like, 3 , 36 , 42 , 45 , 52 did not perform feature selection, 12 , 42 , 45 , 52 or did not employ ensemble learning. 3 , 30 , 36 , 42 , 45 , 52 However, MonDiaL-CAD uses deep features from three CNNs. It also employs DWT to fuse features and reduce their dimension.…”

Section: Discussionmentioning

confidence: 99%

“…The same dataset (MSLD) was used in 44 to train the ResNet-18 CNN model with TL. Similarly, the studies 45 , 46 employed the MSLD dataset to identify monkeypox using a new version of GoogLeNet called MiniGoogLeNet. In contrast, the study 30 utilized the MSLD dataset to train three CNNs involving Inception, Xception, and DenseNet-169.…”

Section: Literature Reviewmentioning

confidence: 99%

See 2 more Smart Citations

MonDiaL-CAD: Monkeypox diagnosis via selected hybrid CNNs unified with feature selection and ensemble learning

Attallah

2023

DIGITAL HEALTH

View full text Add to dashboard Cite

Objective Recently, monkeypox virus is slowly evolving and there are fears it will spread as COVID-19. Computer-aided diagnosis (CAD) based on deep learning approaches especially convolutional neural network (CNN) can assist in the rapid determination of reported incidents. The current CADs were mostly based on an individual CNN. Few CADs employed multiple CNNs but did not investigate which combination of CNNs has a greater impact on the performance. Furthermore, they relied on only spatial information of deep features to train their models. This study aims to construct a CAD tool named “Monkey-CAD” that can address the previous limitations and automatically diagnose monkeypox rapidly and accurately. Methods Monkey-CAD extracts features from eight CNNs and then examines the best possible combination of deep features that influence classification. It employs discrete wavelet transform (DWT) to merge features which diminishes fused features' size and provides a time-frequency demonstration. These deep features’ sizes are then further reduced via an entropy-based feature selection approach. These reduced fused features are finally used to deliver a better representation of the input features and feed three ensemble classifiers. Results Two freely accessible datasets called Monkeypox skin image (MSID) and Monkeypox skin lesion (MSLD) are employed in this study. Monkey-CAD could discriminate among cases with and without Monkeypox achieving an accuracy of 97.1% for MSID and 98.7% for MSLD datasets respectively. Conclusions Such promising results demonstrate that the Monkey-CAD can be employed to assist health practitioners. They also verify that fusing deep features from selected CNNs can boost performance.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

MonDiaL-CAD: Monkeypox diagnosis via selected hybrid CNNs unified with feature selection and ensemble learning

Attallah

2023

DIGITAL HEALTH

View full text Add to dashboard Cite

show abstract

“…The highest performance is achieved using MobileNetV2, with 91.37% accuracy, 90.5% precision, 86.75% recall and 88.25% F1 score. Another approach to classify Monkeypox based on Mini-GoogleNet architecture is proposed in [36] using "Monkeypox Skin Lesion Dataset (MSLD)" [28] and the highest accuracy achieved is 97.08%.…”

Section: Related Workmentioning

confidence: 99%

“…• The research study and experimentation are carried out on a small and limited dataset or on imbalanced dataset [27][28][29][30][34][35][36]. • Comparative analysis with the existing literature based on the pre-trained DL models may not be discussed in detail [28][29][30].…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Diagnosis of Monkeypox Disease using Deep Learning and a Novel Attention Model SENet on Diversified Dataset

Surati

Trivedi

Shrimali

2023

Preprint

View full text Add to dashboard Cite

With the widespread of Monkeypox and increase in the weekly reported number of cases, it is observed that this outbreak continues to put the human beings in risk. The early detection and reporting of this disease will help monitoring and controlling the spread of it and hence, supporting international coordination for the same. For this purpose, the aim of this paper is to classify three diseases viz. Monkeypox, Chikenpox and Measles based on provided image dataset using trained standalone DL models (InceptionV3, EfficientNet, VGG16) and Squeeze and Excitation Network (SENet) Attention model. The first step to implement this approach is to search, collect and aggregate (if require) verified existing dataset(s). To the best of our knowledge, this is the first paper which has proposed the use of SENet based attention models in the classification task of Monkeypox and also targets to aggregate two different datasets from distinct sources in order to improve the performance parameters. The unexplored SENet attention architecture is incorporated with the trunk branch of InceptionV3 (SENet+InceptionV3), EfficientNet (SENet+EfficientNet) and VGG16 (SENet+VGG16) and these architectures improve the accuracy of the Monkeypox classification task significantly. Comprehensive experiments on three datasets depict that the proposed work achieves considerably high results with regard to accuracy, precision, recall and F1-score and hence, improving the overall performance of classification. Thus, the proposed research work is advantageous in enhanced diagnosis and classification of Monkeypox that can be utilized further by healthcare experts and researchers to confront its outspread.

show abstract

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

View full text Add to dashboard Cite

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.

show abstract

Convolutional Neural Network for Monkeypox Detection

Cited by 12 publications

References 18 publications

MonDiaL-CAD: Monkeypox diagnosis via selected hybrid CNNs unified with feature selection and ensemble learning

MonDiaL-CAD: Monkeypox diagnosis via selected hybrid CNNs unified with feature selection and ensemble learning

An Enhanced Diagnosis of Monkeypox Disease using Deep Learning and a Novel Attention Model SENet on Diversified Dataset

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

Contact Info

Product

Resources

About