MPox-DenseConvNet: A Transfer Learning Based Convolutional Neural Network for Monkeypox Detection and Assessment using Color Models
Monkeypox, a zoonotic orthopoxvirus, unintentionally produces smallpox-like sickness in people, though with a far lower death rate. Despite the fact that Deep Networks have been extensively used for visual inspection of such diesases, the majority of works have frequently relied their analysis on th...
محفوظ في:
| المؤلف الرئيسي: | |
|---|---|
| مؤلفون آخرون: | |
| منشور في: |
2023
|
| الموضوعات: | |
| الوصول للمادة أونلاين: | https://bspace.buid.ac.ae/handle/1234/3098 https://doi.org/10.1109/ICCIKE58312.2023.10131756. |
| الوسوم: |
إضافة وسم
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!
|
| _version_ | 1862980615441219584 |
|---|---|
| author | Tiwari, Shamik |
| author2 | Maheshwari, Piyush |
| author2_role | author |
| author_facet | Tiwari, Shamik Maheshwari, Piyush |
| author_role | author |
| dc.creator.none.fl_str_mv | Tiwari, Shamik Maheshwari, Piyush |
| dc.date.none.fl_str_mv | 2023 2025-05-22T12:46:45Z 2025-05-22T12:46:45Z |
| dc.identifier.none.fl_str_mv | Tiwari, S., Maheshwari, P. and 2023 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE) Dubai, United Arab Emirates 2023 March 9 - 2023 March 10 (2023) “MPox-DenseConvNet: A Transfer Learning Based Convolutional Neural Network for Monkeypox Detection and Assessment using Color Models,” in 2023 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE), pp. 175–180. https://bspace.buid.ac.ae/handle/1234/3098 https://doi.org/10.1109/ICCIKE58312.2023.10131756. |
| dc.language.none.fl_str_mv | en |
| dc.publisher.none.fl_str_mv | IEEE |
| dc.relation.none.fl_str_mv | 2023 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE)175-180 |
| dc.subject.none.fl_str_mv | Monkeypox, Convolutional Neural Network, DenseNet, RGB, HSV, YCbCr |
| dc.title.none.fl_str_mv | MPox-DenseConvNet: A Transfer Learning Based Convolutional Neural Network for Monkeypox Detection and Assessment using Color Models |
| dc.type.none.fl_str_mv | Article |
| description | Monkeypox, a zoonotic orthopoxvirus, unintentionally produces smallpox-like sickness in people, though with a far lower death rate. Despite the fact that Deep Networks have been extensively used for visual inspection of such diesases, the majority of works have frequently relied their analysis on the results produced by a particular network without taking the response of the colour channels to classification findings into account. Deep learning has recently shown to have enormous potential for image based diagnosis, including the detection of skin cancer, the identification of tumour cells, and the COVID-19 patient diagnosis through chest radiography. As a result, a similar application may be used to identify the sickness associated with monkeypox as it impacted human skin. This image can then be obtained and employed to identify the illness. This work focused on investing the prominent color channel for Convolution Neural Network (ConvNet) based monkeypox classification using skin images. For this purpose, a transfer lerning based classification architecture named MPox-DenseConvNet with fine tuning is designed. Three colour channels namely RGB, HSV and YCbCr are analyzed using proposed MPox-DenseConvNet. The outcomes demonstrated that the colour channel employed had an impact on the performance of the classification. The results also confirmed that the HSV color channel has outperformed of all the colour channels taken into consideration. |
| id | budr_6a42bf88b4f9a59e4b7c2c15f8da52ec |
| identifier_str_mv | Tiwari, S., Maheshwari, P. and 2023 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE) Dubai, United Arab Emirates 2023 March 9 - 2023 March 10 (2023) “MPox-DenseConvNet: A Transfer Learning Based Convolutional Neural Network for Monkeypox Detection and Assessment using Color Models,” in 2023 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE), pp. 175–180. |
| language_invalid_str_mv | en |
| network_acronym_str | budr |
| network_name_str | The British University in Dubai repository |
| oai_identifier_str | oai:bspace.buid.ac.ae:1234/3098 |
| publishDate | 2023 |
| publisher.none.fl_str_mv | IEEE |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | MPox-DenseConvNet: A Transfer Learning Based Convolutional Neural Network for Monkeypox Detection and Assessment using Color ModelsTiwari, ShamikMaheshwari, PiyushMonkeypox, Convolutional Neural Network, DenseNet, RGB, HSV, YCbCrMonkeypox, a zoonotic orthopoxvirus, unintentionally produces smallpox-like sickness in people, though with a far lower death rate. Despite the fact that Deep Networks have been extensively used for visual inspection of such diesases, the majority of works have frequently relied their analysis on the results produced by a particular network without taking the response of the colour channels to classification findings into account. Deep learning has recently shown to have enormous potential for image based diagnosis, including the detection of skin cancer, the identification of tumour cells, and the COVID-19 patient diagnosis through chest radiography. As a result, a similar application may be used to identify the sickness associated with monkeypox as it impacted human skin. This image can then be obtained and employed to identify the illness. This work focused on investing the prominent color channel for Convolution Neural Network (ConvNet) based monkeypox classification using skin images. For this purpose, a transfer lerning based classification architecture named MPox-DenseConvNet with fine tuning is designed. Three colour channels namely RGB, HSV and YCbCr are analyzed using proposed MPox-DenseConvNet. The outcomes demonstrated that the colour channel employed had an impact on the performance of the classification. The results also confirmed that the HSV color channel has outperformed of all the colour channels taken into consideration.IEEE2025-05-22T12:46:45Z2025-05-22T12:46:45Z2023ArticleTiwari, S., Maheshwari, P. and 2023 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE) Dubai, United Arab Emirates 2023 March 9 - 2023 March 10 (2023) “MPox-DenseConvNet: A Transfer Learning Based Convolutional Neural Network for Monkeypox Detection and Assessment using Color Models,” in 2023 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE), pp. 175–180.https://bspace.buid.ac.ae/handle/1234/3098https://doi.org/10.1109/ICCIKE58312.2023.10131756.en2023 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE)175-180oai:bspace.buid.ac.ae:1234/30982025-05-22T12:57:59Z |
| spellingShingle | MPox-DenseConvNet: A Transfer Learning Based Convolutional Neural Network for Monkeypox Detection and Assessment using Color Models Tiwari, Shamik Monkeypox, Convolutional Neural Network, DenseNet, RGB, HSV, YCbCr |
| title | MPox-DenseConvNet: A Transfer Learning Based Convolutional Neural Network for Monkeypox Detection and Assessment using Color Models |
| title_full | MPox-DenseConvNet: A Transfer Learning Based Convolutional Neural Network for Monkeypox Detection and Assessment using Color Models |
| title_fullStr | MPox-DenseConvNet: A Transfer Learning Based Convolutional Neural Network for Monkeypox Detection and Assessment using Color Models |
| title_full_unstemmed | MPox-DenseConvNet: A Transfer Learning Based Convolutional Neural Network for Monkeypox Detection and Assessment using Color Models |
| title_short | MPox-DenseConvNet: A Transfer Learning Based Convolutional Neural Network for Monkeypox Detection and Assessment using Color Models |
| title_sort | MPox-DenseConvNet: A Transfer Learning Based Convolutional Neural Network for Monkeypox Detection and Assessment using Color Models |
| topic | Monkeypox, Convolutional Neural Network, DenseNet, RGB, HSV, YCbCr |
| url | https://bspace.buid.ac.ae/handle/1234/3098 https://doi.org/10.1109/ICCIKE58312.2023.10131756. |