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All Journal Emerging Science Journal
Ratanamahatana, Chotirat Ann
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Environmental Science

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An Explainable Deep Learning Approach for Classifying Monkeypox Disease by Leveraging Skin Lesion Image Data Maseleno, Andino; Huda, Miftachul; Ratanamahatana, Chotirat Ann
Emerging Science Journal Vol 8, No 5 (2024): October
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2024-08-05-013

Abstract

According to the World Health Organization's (WHO) external situation report on the multi-country outbreak of Monkeypox in 2023, from 11 countries in Southeast Asia Regions, Thailand recorded the highest reported cases, totaling 461. The ongoing Monkeypox outbreak has raised significant public health concerns due to its rapid spread across several nations. Early detection and diagnosis are imperative for effectively treating and controlling Monkeypox. Given this context, this study aimed to determine the most efficient model for detecting Monkeypox by employing interpretable deep learning techniques. This study utilizes deep learning techniques to diagnose Monkeypox based on images of skin lesions. We evaluate based on four models—convolutional neural network (CNN), gated recurrent unit (GRU), long short-term memory (LSTM), and bidirectional long short term memory (BiLSTM)—using a publicly available dataset. Additionally, we incorporate Local Interpretable Model-Agnostic Explanations (LIME) and techniques for explainable AI, facilitating visual interpretation of model predictions for healthcare practitioners. The CNN model's performance and LSTM model's performance have an accuracy of 100%, while the GRU model's performance and BiLSTM model's performance have an accuracy of 99.88% and 99.45%. Our findings demonstrate the effectiveness of deep learning models, including the suggested CNN model leveraging the pre-trained MobileNetV2 and LSTM. These models can play a pivotal role in combating the Monkeypox virus. Doi: 10.28991/ESJ-2024-08-05-013 Full Text: PDF
Metaheuristic Hyperparameter Optimization and Explainable Deep Learning for Baggage Threat Detection Maseleno, Andino; Huda, Miftachul; Fudholi, Ahmad; Ratanamahatana, Chotirat Ann
Emerging Science Journal Vol. 10 No. 1 (2026): February
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-01-06

Abstract

The American Statistical Association reports that Bangkok, the capital and largest city of Thailand, holds the top spot as the most visited city worldwide in 2023. X-ray imaging for security screening plays a crucial role in upholding transportation security by detecting a diverse range of threats or prohibited items carried by passengers. This study introduces an advanced deep learning model leveraging YOLOv8, renowned for its enhanced efficiency in automating baggage detection processes. To enhance the model's hyperparameters and adjust them finely during the training process using the baggage dataset, the system utilized a metaheuristic optimization algorithm known as Evolutionary Genetic Algorithm, which is based on evolutionary principles. Incorporating explainable artificial intelligence techniques such as Local Interpretable Model-Agnostic Explanations (LIME) and Gradient-weighted Class Activation Mapping (Grad-CAM) allows for visual interpretation of predictions, aiding operators in utilizing the model effectively. We trained and tested the baggage dataset, which included 8,312 images and five classes: gun, knife, pliers, scissors, and wrench. The YOLOv8 model achieved the following metrics for the detection of prohibited objects in baggage inspection: an overall precision of 90.5%, recall of 83.3%, mAP50 of 91.3%, and mAP50-95 of 67%. The proposed method can fully automate the recognition of prohibited objects during baggage inspection. This approach is beneficial for designing an integrated, automatic, and non-destructive X-ray image-based classification system.
Co-Authors Ahmad Fudholi Andino Maseleno Chatarina Umbul Wahyuni Miftachul Huda