Enhancing Monkeypox Skin Lesion Detection: A Fusion Approach of VGG 16 and Xception Features with SVM Classifier

Debendra Muduli; Amballa Vijay Sai Charan Naidu; Kondepudi Venkata Durga; K. Rahul; Majji Jayanth Kumar; Santosh Kumar Sharma

doi:10.1109/AESPC59761.2023.10390185

Enhancing Monkeypox Skin Lesion Detection: A Fusion Approach of VGG 16 and Xception Features with SVM Classifier

Debendra Muduli
, Amballa Vijay Sai Charan Naidu
, Kondepudi Venkata Durga
, K. Rahul
, Majji Jayanth Kumar
, Santosh Kumar Sharma

C. V. Raman College of Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Monkeypox, a viral ailment resembling smallpox, can be identified through transfer learning, which involves utilizing pre-trained deep learning models to recognize patterns in medical images and facilitate early detection. This study assesses the efficacy of pre-trained convolutional neural network (CNN) models such as VGG 16, VGG 19, InceptionV3, and Xception as feature extractors. The study combines non-handcrafted features from these models, creating a final feature matrix that is inputted into various conventional machine learning classifiers. Testing was conducted on a publicly available dataset of monkeypox skin images, with the best performance achieved by VGG 16 + Xcepetion + SVM, exhibiting an accuracy of 97.14%, a sensitivity of 93.75%, and a specificity of 100%. This research highlights the potential of deep learning in medical image analysis and its potential to aid clinicians in the early detection of monkeypox.

Original language	English
Title of host publication	2023 IEEE 3rd International Conference on Applied Electromagnetics, Signal Processing, and Communication, AESPC 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798350358742
DOIs	https://doi.org/10.1109/AESPC59761.2023.10390185
Publication status	Published - 2023
Externally published	Yes
Event	3rd IEEE International Conference on Applied Electromagnetics, Signal Processing, and Communication, AESPC 2023 - Bhubaneswar, India Duration: 24 Nov 2023 → 26 Nov 2023

Publication series

Name	2023 IEEE 3rd International Conference on Applied Electromagnetics, Signal Processing, and Communication, AESPC 2023

Conference

Conference	3rd IEEE International Conference on Applied Electromagnetics, Signal Processing, and Communication, AESPC 2023
Country/Territory	India
City	Bhubaneswar
Period	24/11/23 → 26/11/23

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Deep learning
Inception V3
Magnetic resonance imaging (MRI)
Medical imaging
Monkeypox

Access to Document

10.1109/AESPC59761.2023.10390185

Cite this

Muduli, D., Naidu, A. V. S. C., Durga, K. V., Rahul, K., Kumar, M. J., & Sharma, S. K. (2023). Enhancing Monkeypox Skin Lesion Detection: A Fusion Approach of VGG 16 and Xception Features with SVM Classifier. In 2023 IEEE 3rd International Conference on Applied Electromagnetics, Signal Processing, and Communication, AESPC 2023 (2023 IEEE 3rd International Conference on Applied Electromagnetics, Signal Processing, and Communication, AESPC 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/AESPC59761.2023.10390185

Muduli, Debendra ; Naidu, Amballa Vijay Sai Charan ; Durga, Kondepudi Venkata et al. / Enhancing Monkeypox Skin Lesion Detection : A Fusion Approach of VGG 16 and Xception Features with SVM Classifier. 2023 IEEE 3rd International Conference on Applied Electromagnetics, Signal Processing, and Communication, AESPC 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (2023 IEEE 3rd International Conference on Applied Electromagnetics, Signal Processing, and Communication, AESPC 2023).

@inproceedings{94e7b45d1f4547068eb27635fb1f408c,

title = "Enhancing Monkeypox Skin Lesion Detection: A Fusion Approach of VGG 16 and Xception Features with SVM Classifier",

abstract = "Monkeypox, a viral ailment resembling smallpox, can be identified through transfer learning, which involves utilizing pre-trained deep learning models to recognize patterns in medical images and facilitate early detection. This study assesses the efficacy of pre-trained convolutional neural network (CNN) models such as VGG 16, VGG 19, InceptionV3, and Xception as feature extractors. The study combines non-handcrafted features from these models, creating a final feature matrix that is inputted into various conventional machine learning classifiers. Testing was conducted on a publicly available dataset of monkeypox skin images, with the best performance achieved by VGG 16 + Xcepetion + SVM, exhibiting an accuracy of 97.14\%, a sensitivity of 93.75\%, and a specificity of 100\%. This research highlights the potential of deep learning in medical image analysis and its potential to aid clinicians in the early detection of monkeypox.",

keywords = "Deep learning, Inception V3, Magnetic resonance imaging (MRI), Medical imaging, Monkeypox",

author = "Debendra Muduli and Naidu, \{Amballa Vijay Sai Charan\} and Durga, \{Kondepudi Venkata\} and K. Rahul and Kumar, \{Majji Jayanth\} and Sharma, \{Santosh Kumar\}",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 3rd IEEE International Conference on Applied Electromagnetics, Signal Processing, and Communication, AESPC 2023 ; Conference date: 24-11-2023 Through 26-11-2023",

year = "2023",

doi = "10.1109/AESPC59761.2023.10390185",

language = "English",

series = "2023 IEEE 3rd International Conference on Applied Electromagnetics, Signal Processing, and Communication, AESPC 2023",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2023 IEEE 3rd International Conference on Applied Electromagnetics, Signal Processing, and Communication, AESPC 2023",

}

Muduli, D, Naidu, AVSC, Durga, KV, Rahul, K, Kumar, MJ & Sharma, SK 2023, Enhancing Monkeypox Skin Lesion Detection: A Fusion Approach of VGG 16 and Xception Features with SVM Classifier. in 2023 IEEE 3rd International Conference on Applied Electromagnetics, Signal Processing, and Communication, AESPC 2023. 2023 IEEE 3rd International Conference on Applied Electromagnetics, Signal Processing, and Communication, AESPC 2023, Institute of Electrical and Electronics Engineers Inc., 3rd IEEE International Conference on Applied Electromagnetics, Signal Processing, and Communication, AESPC 2023, Bhubaneswar, India, 24/11/23. https://doi.org/10.1109/AESPC59761.2023.10390185

Enhancing Monkeypox Skin Lesion Detection: A Fusion Approach of VGG 16 and Xception Features with SVM Classifier. / Muduli, Debendra; Naidu, Amballa Vijay Sai Charan; Durga, Kondepudi Venkata et al.
2023 IEEE 3rd International Conference on Applied Electromagnetics, Signal Processing, and Communication, AESPC 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (2023 IEEE 3rd International Conference on Applied Electromagnetics, Signal Processing, and Communication, AESPC 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Enhancing Monkeypox Skin Lesion Detection

T2 - 3rd IEEE International Conference on Applied Electromagnetics, Signal Processing, and Communication, AESPC 2023

AU - Muduli, Debendra

AU - Naidu, Amballa Vijay Sai Charan

AU - Durga, Kondepudi Venkata

AU - Rahul, K.

AU - Kumar, Majji Jayanth

AU - Sharma, Santosh Kumar

PY - 2023

Y1 - 2023

N2 - Monkeypox, a viral ailment resembling smallpox, can be identified through transfer learning, which involves utilizing pre-trained deep learning models to recognize patterns in medical images and facilitate early detection. This study assesses the efficacy of pre-trained convolutional neural network (CNN) models such as VGG 16, VGG 19, InceptionV3, and Xception as feature extractors. The study combines non-handcrafted features from these models, creating a final feature matrix that is inputted into various conventional machine learning classifiers. Testing was conducted on a publicly available dataset of monkeypox skin images, with the best performance achieved by VGG 16 + Xcepetion + SVM, exhibiting an accuracy of 97.14%, a sensitivity of 93.75%, and a specificity of 100%. This research highlights the potential of deep learning in medical image analysis and its potential to aid clinicians in the early detection of monkeypox.

AB - Monkeypox, a viral ailment resembling smallpox, can be identified through transfer learning, which involves utilizing pre-trained deep learning models to recognize patterns in medical images and facilitate early detection. This study assesses the efficacy of pre-trained convolutional neural network (CNN) models such as VGG 16, VGG 19, InceptionV3, and Xception as feature extractors. The study combines non-handcrafted features from these models, creating a final feature matrix that is inputted into various conventional machine learning classifiers. Testing was conducted on a publicly available dataset of monkeypox skin images, with the best performance achieved by VGG 16 + Xcepetion + SVM, exhibiting an accuracy of 97.14%, a sensitivity of 93.75%, and a specificity of 100%. This research highlights the potential of deep learning in medical image analysis and its potential to aid clinicians in the early detection of monkeypox.

KW - Deep learning

KW - Inception V3

KW - Magnetic resonance imaging (MRI)

KW - Medical imaging

KW - Monkeypox

UR - https://www.scopus.com/pages/publications/85184848777

U2 - 10.1109/AESPC59761.2023.10390185

DO - 10.1109/AESPC59761.2023.10390185

M3 - Conference contribution

AN - SCOPUS:85184848777

T3 - 2023 IEEE 3rd International Conference on Applied Electromagnetics, Signal Processing, and Communication, AESPC 2023

BT - 2023 IEEE 3rd International Conference on Applied Electromagnetics, Signal Processing, and Communication, AESPC 2023

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 24 November 2023 through 26 November 2023

ER -

Muduli D, Naidu AVSC, Durga KV, Rahul K, Kumar MJ, Sharma SK. Enhancing Monkeypox Skin Lesion Detection: A Fusion Approach of VGG 16 and Xception Features with SVM Classifier. In 2023 IEEE 3rd International Conference on Applied Electromagnetics, Signal Processing, and Communication, AESPC 2023. Institute of Electrical and Electronics Engineers Inc. 2023. (2023 IEEE 3rd International Conference on Applied Electromagnetics, Signal Processing, and Communication, AESPC 2023). doi: 10.1109/AESPC59761.2023.10390185

Enhancing Monkeypox Skin Lesion Detection: A Fusion Approach of VGG 16 and Xception Features with SVM Classifier

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

Other files and links

Fingerprint

Cite this