Categorization of human monkey-pox from skin lesion images based on transformer and ensemble learning using GRADCAM

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023.

Bibliografiset tiedot
Päätekijät:	Hassan, Ibne, Tahsin, Raida Mobashshira, Toma, Sadia Shara, Utchhash, Tausif Tazwar Quadria
Muut tekijät:	Hossain, Muhammad Iqbal
Aineistotyyppi:	Opinnäyte
Kieli:	English
Julkaistu:	Brac University 2023
Aiheet:	Covid-19 Pathogens Symptoms Monkeypox CLAHE GRADCAM Ensemble learning Transformer based models Pre-tained models Cognitive learning theory Pattern recognition systems Machine learning
Linkit:	http://hdl.handle.net/10361/22041

id	10361-22041
record_format	dspace
spelling	10361-220412023-12-31T21:02:34Z Categorization of human monkey-pox from skin lesion images based on transformer and ensemble learning using GRADCAM Hassan, Ibne Tahsin, Raida Mobashshira Toma, Sadia Shara Utchhash, Tausif Tazwar Quadria Hossain, Muhammad Iqbal Rahman, Rafeed Department of Computer Science and Engineering, Brac University Covid-19 Pathogens Symptoms Monkeypox CLAHE GRADCAM Ensemble learning Transformer based models Pre-tained models Cognitive learning theory Pattern recognition systems Machine learning This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023. Cataloged from PDF version of thesis. Includes bibliographical references (pages 67-70). As the world keeps healing from the worldwide outbreak of COVID-19, the MPOX virus poses a new risk. The Mpox virus is not as deadly or contagious as COVID-19, but new patient cases are recorded every day from a wide variety of nations.Therefore, it will not come as a surprise if another global pandemic occurs due to a lack of pre- cautionary measures. Therefore, it is essential to detect them before they spread throughout the community. The World Health Organisation (WHO) has issued nu- merous precautionary warnings regarding MPOX. If MPOX spreads swiftly, it poses a significant threat to public health. The result is a significant increase in hospital wait times. This means that hospitals require additional supplementary or auxiliary systems.Recent advances in machine learning have demonstrated immense promise for diagnosis based on image data, including detection of cancer, identification of tu- mour cell, and identification of COVID-19 patients. Therefore, identical technology could possibly be used to detect the human skin infection known as MPOX. After acquiring an image it can be utilised for further diagnosis and early identification of MPOX.In this research, we leverage 13 recently developed deep learning (DL) mod- els to suggest a new strategy for enhancing the accuracy of MPOX image detection and classification. Eight of the suggested models are based on transformer, while the remaining five are CNN-based models that have been pre-trained.The publicly avail- able Monkeypox Skin Images Dataset (MSID) is utilized to evaluate the suggested method. Four standard metrics—precision, accuracy, F1-score and recall —were applied to the outcomes after the models were fine-tuned. We ultimately utilised an ensemble approach to enhance overall performance and obtain more precise results. We achieved the best results possible with our approach, which included a 99% F1 score, 99% accuracy, 100% precision, and 99% recall. Based on these promising out- comes, which surpass those of existing methods, we propose applying the suggested method for widespread testing by health practitioners. This model can be used as a supplementary diagnostic system for the early detection of MPOX skin lesions. Ibne Hassan Raida Mobashshira Tahsin Sadia Shara Toma Tausif Tazwar Quadria Utchhash B.Sc. in Computer Science 2023-12-31T05:17:15Z 2023-12-31T05:17:15Z 2023 2023-01 Thesis ID 22241121 ID 19101272 ID 19101016 ID 19101022 http://hdl.handle.net/10361/22041 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 70 pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	English
topic	Covid-19 Pathogens Symptoms Monkeypox CLAHE GRADCAM Ensemble learning Transformer based models Pre-tained models Cognitive learning theory Pattern recognition systems Machine learning
spellingShingle	Covid-19 Pathogens Symptoms Monkeypox CLAHE GRADCAM Ensemble learning Transformer based models Pre-tained models Cognitive learning theory Pattern recognition systems Machine learning Hassan, Ibne Tahsin, Raida Mobashshira Toma, Sadia Shara Utchhash, Tausif Tazwar Quadria Categorization of human monkey-pox from skin lesion images based on transformer and ensemble learning using GRADCAM
description	This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023.
author2	Hossain, Muhammad Iqbal
author_facet	Hossain, Muhammad Iqbal Hassan, Ibne Tahsin, Raida Mobashshira Toma, Sadia Shara Utchhash, Tausif Tazwar Quadria
format	Thesis
author	Hassan, Ibne Tahsin, Raida Mobashshira Toma, Sadia Shara Utchhash, Tausif Tazwar Quadria
author_sort	Hassan, Ibne
title	Categorization of human monkey-pox from skin lesion images based on transformer and ensemble learning using GRADCAM
title_short	Categorization of human monkey-pox from skin lesion images based on transformer and ensemble learning using GRADCAM
title_full	Categorization of human monkey-pox from skin lesion images based on transformer and ensemble learning using GRADCAM
title_fullStr	Categorization of human monkey-pox from skin lesion images based on transformer and ensemble learning using GRADCAM
title_full_unstemmed	Categorization of human monkey-pox from skin lesion images based on transformer and ensemble learning using GRADCAM
title_sort	categorization of human monkey-pox from skin lesion images based on transformer and ensemble learning using gradcam
publisher	Brac University
publishDate	2023
url	http://hdl.handle.net/10361/22041
work_keys_str_mv	AT hassanibne categorizationofhumanmonkeypoxfromskinlesionimagesbasedontransformerandensemblelearningusinggradcam AT tahsinraidamobashshira categorizationofhumanmonkeypoxfromskinlesionimagesbasedontransformerandensemblelearningusinggradcam AT tomasadiashara categorizationofhumanmonkeypoxfromskinlesionimagesbasedontransformerandensemblelearningusinggradcam AT utchhashtausiftazwarquadria categorizationofhumanmonkeypoxfromskinlesionimagesbasedontransformerandensemblelearningusinggradcam
_version_	1814308772034641920

Categorization of human monkey-pox from skin lesion images based on transformer and ensemble learning using GRADCAM

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