AI EMPOWERED DIAGNOSIS OF PEMPHIGUS: A MACHINE LEARNING APPROACH FOR AUTOMATED SKIN LESION DETECTION
Mamun Ahmed
Bangladesh Army International University of Science and Technology, Department of Computer Science and Technology (Bangladesh)
https://orcid.org/0000-0002-3980-3981
Salma Binta Islam
Bangladesh Army International University of Science and Technology, Department of Computer Science and Technology (Bangladesh)
https://orcid.org/0009-0004-9975-4861
Aftab Uddin Alif
alifbaiust@gmail.comBangladesh Army International University of Science and Technology, Department of Computer Science and Technology (Bangladesh)
https://orcid.org/0009-0001-8461-1129
Mirajul Islam
Bangladesh Army International University of Science and Technology, Department of Computer Science and Technology (Bangladesh)
https://orcid.org/0009-0006-1215-1422
Sabrina Motin Saima
Bangladesh Army International University of Science and Technology, Department of Computer Science and Technology (Bangladesh)
https://orcid.org/0009-0005-7319-7259
Abstract
Pemphigus is a skin disease that can cause a serious damage to human skin. Pemphigus can result in other issues including painful patches and infected blisters, which can result in sepsis, weight loss, and starvation, all of which can be life-threatening, tooth decay and gum disease. Early prediction of Pemphigus may save us from fatal disease. Machine learning has the potential to offer a highly efficient approach for decision-making and precise forecasting. The healthcare sector is experiencing remarkable advancements through the utilization of machine learning techniques. Therefore, to identify Pemphigus using images, we suggested machine learning-based techniques. This proposed system uses a large dataset collected from various web sources to detect Pemphigus. Augmentation has been applied on our dataset using techniques such as zoom, flip, brightness, distortion, magnitude, height, width to enhance the breadth and variety of the dataset and improve model’s performance. Five popular machine learning algorithms has been employed to train and evaluate model, these are K-Nearest Neighbor (referred to as KNN), Decision Tree (DT), Logistic Regression (LR), Random Forest (RF), and Convolutional Neural Network (CNN). Our outcome indicate that the CNN based model outperformed the other algorithms by achieving accuracy of 93% whereas LR, KNN, RF and DT achieved accuracies of 78%, 70%, 85% and 75% respectively.
Keywords:
pemphigus, blisters, augmentation, CNNReferences
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Authors
Mamun AhmedBangladesh Army International University of Science and Technology, Department of Computer Science and Technology Bangladesh
https://orcid.org/0000-0002-3980-3981
Authors
Salma Binta IslamBangladesh Army International University of Science and Technology, Department of Computer Science and Technology Bangladesh
https://orcid.org/0009-0004-9975-4861
Authors
Aftab Uddin Alifalifbaiust@gmail.com
Bangladesh Army International University of Science and Technology, Department of Computer Science and Technology Bangladesh
https://orcid.org/0009-0001-8461-1129
Authors
Mirajul IslamBangladesh Army International University of Science and Technology, Department of Computer Science and Technology Bangladesh
https://orcid.org/0009-0006-1215-1422
Authors
Sabrina Motin SaimaBangladesh Army International University of Science and Technology, Department of Computer Science and Technology Bangladesh
https://orcid.org/0009-0005-7319-7259
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