Monkeypox Skin Lesion Classification Using Fine-Tune CNN Model

Monkeypox Skin Lesion Classification Using Fine-Tune CNN Model

A novel method for categorizing Monkeypox skin lesions is presented in this research work utilizing a finely adjusted Convolutional Neural Network (CNN) model. Monkeypox, a rare viral ailment that can lead to serious skin lesions, necessitates precise and prompt diagnosis for successful therapy. Thi...

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Bibliographic Details
Published in:2024 4th International Conference on Pervasive Computing and Social Networking (ICPCSN) pp. 37 - 41
Main Authors: Jagani, Dhwani, Degadwala, Sheshang
Format: Conference Proceeding
Language:English
Published: IEEE 03-05-2024
Subjects:
Accuracy
AlexNet
classification
Convolutional Neural Network (CNN)
fine-tuning
Lesions
Monkeypox
Pervasive computing
ResNet50
Sensitivity
Skin
skin lesions
Social networking (online)
Training
VGG16
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Summary:A novel method for categorizing Monkeypox skin lesions is presented in this research work utilizing a finely adjusted Convolutional Neural Network (CNN) model. Monkeypox, a rare viral ailment that can lead to serious skin lesions, necessitates precise and prompt diagnosis for successful therapy. This research work refines a pre-existing CNN model using a collection of Monkeypox skin lesion pictures to construct a classification system that can differentiate between various lesion types. The experimental findings showcase the efficacy of this proposed method, achieving remarkable accuracy and sensitivity in Monkeypox skin lesion classification. This study contributes significantly to the advancement of automated diagnostic solutions for infectious illnesses, supporting healthcare professionals in making quicker and more precise diagnoses.
DOI:10.1109/ICPCSN62568.2024.00014