Monkeypox Disease Detection using Deep Learning Techniques

Mitra, Arpita (2023) Monkeypox Disease Detection using Deep Learning Techniques. Masters thesis, Dublin, National College of Ireland.

Preview	PDF (Master of Science) Download (2MB) | Preview
Preview	PDF (Configuration manual) Download (2MB) | Preview

Abstract

Following a pandemic like Covid-19, from which the world is still trying to recover, another unexpected epidemic of a new contagious infection called Monkeypox has captivated everybody’s attention. It is the first example of a more wide population spreading of Monkeypox disease. The number of infected cases is still increasing even though it is less contagious and dangerous unlike Covid-19. Hence, developing a precautionary strategy is crucial for a brighter future. This resembles with other skin diseases in certain ways, but due to a lack of proper identification of the disease, it can be fatal in some situations. AI-based identification would be beneficial in this scenario as spotting Monkeypox at a very early stage will become easier with its help. Convolutional Neural Network (CNN) technology and an approach to use VGG-19 models with model’s bottleneck features have been introduced in this study with fine-tuning the model by changing its custom layers to check and improve the performance and accuracy by lowering the complexities of the image classification procedure. A publicly accessible dataset has been employed for the research. In accordance with the model’s acquired Accuracy, F1 Score, Precision, and Recall Value, the findings that were acquired using the recommended approach were confirmed and evaluated on various images of Monkeypox and non- Monkeypox to determine their correctness and efficacy. The achieved accuracy of 91.87% from VGG-19 model clearly states that this model will help in detecting Monkeypox at a wider range.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Agarwal, Bharat UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science R Medicine > RL Dermatology Q Science > QH Natural history > QH301 Biology > Methods of research. Technique. Experimental biology > Data processing. Bioinformatics > Artificial intelligence Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Artificial intelligence Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Tamara Malone
Date Deposited:	22 May 2023 10:54
Last Modified:	22 May 2023 10:54
URI:	https://norma.ncirl.ie/id/eprint/6620

Actions (login required)

View Item