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
N1 - Publisher Copyright:
© 2023 IEEE.
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 - http://www.scopus.com/inward/record.url?scp=85184848777&partnerID=8YFLogxK
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 -
