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All Journal Nusantara Science and Technology Proceedings
Andrew Jonathan Brahms Simangunsong
Universitas Indonesia, Depok 16911, Indonesia
Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Economics, Econometrics & Finance Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Medicine & Pharmacology

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Leprosy Early Detection Through Binary Segmentation Using ResU-Net Andrew Jonathan Brahms Simangunsong
Nusantara Science and Technology Proceedings Multi-Conference Proceeding Series E
Publisher : Future Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11594/nstp.2023.3721

Abstract

Leprosy is a chronic infectious disease caused by Mycobacterium leprae that can lead to physical deformity if left untreated. Indonesia currently ranks third in the world for leprosy prevalence, with the highest concentration of cases found in the provinces of West Papua, North Maluku, and Papua. These provinces, located in the eastern region of Indonesia, face numerous challenges in terms of healthcare accessibility for early leprosy detection due to various factors and novel, more accessible method to detect leprosy is urgently needed. In this study, we introduce an innovative approach to early leprosy detection by leveraging the ResU-Net model. The ResU-Net, a hybrid architecture, combines the robust U-Net framework, renowned for its efficacy in medical image segmentation, with the powerful ResNet-50 and ResNet-101 backbones. The incorporation of ResNet-50 and ResNet-101 enhances the model's capability to extract intricate features from the target image, allowing for a more comprehensive analysis and ultimately, a more accurate and early detection of leprosy. To train and validate our model, we employ the CO2Wounds Leprosy dataset, a comprehensive collection of medical images showcasing images of leprosy taken using smartphone. The research results demonstrate the promising potential of ResU-Net in accurately identifying leprosy-affected areas within these images with highest IoU scores of around 80% with the ResNet101 backbone and around 79% with the ResNet50 backbone. This method holds great potential for improving the management of leprosy in regions with high prevalence by enabling accessible and timely interventions.
Feature Reduction of Lung Cancer Microarray Data Using Mutual Information Selection and PyCaret-Supported Recursive Feature Elimination Andrew Jonathan Brahms Simangunsong; Valha Tsabita Hidayat
Nusantara Science and Technology Proceedings Multi-Conference Proceeding Series E
Publisher : Future Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11594/nstp.2023.3701

Abstract

Lung cancer remains a leading cause of cancer-related mortality worldwide, and Indonesia's ever-increasing amount of pollution signals an urgency for improvement in lung cancer early detection. One of the methods to detect lung cancer is molecular diagnosis using DNA microarray, which has been proven to be effective. However, the complexity of microarray data with a vast number of features hinders the timely and accurate detection of lung cancer. This study seeks to optimize the features of the data to improve classification performance. Our approach combines Mutual Information Feature Selection with Recursive Feature Elimination, leveraging the PyCaret library to train and evaluate machine learning models. The process involves initial feature reduction using Mutual Information to enhance computational efficiency, followed by training machine learning models with PyCaret. The two best-performing models for each dataset are used to perform recursive feature elimination to search for the most optimal feature. A support vector machine is also used for comparison. The final output will be three subsets of features and another subset that consists of combined features of the rest of other subsets. Finally, PyCaret will be utilized again to train machine learning models with all feature subsets. The study shows that other models can select fewer features compared to the Support Vector Machine and still maintain a powerful predictive power with high accuracy (95% - 98%). In conclusion, our research offers a new approach to selecting optimal features for microarray analysis, with implications for more effective and timely cancer diagnosis.
Co-Authors Valha Tsabita Hidayat