International Journal of Advances in Applied Sciences (IJAAS) Vol. 13, No. 1, March 2024, pp. 46~53 ISSN: 2252-8814, DOI: 10.11591/ijaas.v13.i1.pp46-53  46 Journal homepage: http://ijaas.iaescore.com Enrichment of microscopic photographs by utilizing CNN regarding soil-transmitted helminths identification Rio Andika Malik 1 , Marta Riri Frimadani 2 , Dwipa Junika Putra 1 1 Department of Digital Business, Faculty of Economic Business and Social Sciences, University of Perintis Indonesia, Padang, Indonesia 2 Integrated Natural Resources Management, Postgraduate Program, Andalas University, Padang, Indonesia Article Info ABSTRACT Article history: Received Aug 13, 2023 Revised Nov 28, 2023 Accepted Dec 9, 2023 Soil-transmitted helminth (STH) infection remains a significant global health challenge, affecting millions of people, particularly in developing countries. A convolutional neural network (CNN) approach to optimize the detection of STH infections in microscopic images. The study aims to assess the effectiveness of the CNN model in identifying and classifying STH worm eggs accurately. The research employs MATLAB as the primary tool for conducting experiments and validation tests. By implementing image preprocessing techniques to enhance image quality and applying precise segmentation methods, the CNN model is trained on a dataset of microscopic images to learn and classify STH infections effectively. The validation test results demonstrate that the CNN model achieved a high accuracy rate of 92.31% in classifying STH infections. This accuracy surpasses traditional methods, which are time-consuming and susceptible to human errors. This study underscores the importance of integrating artificial intelligence, particularly CNN, into the healthcare domain to support detecting and diagnosing diseases requiring specialized expertise, such as STH infections. The findings of this research can serve as a valuable reference for researchers, medical practitioners, and data scientists in leveraging artificial intelligence to enhance the quality of healthcare services, leading to positive impacts on society worldwide. Keywords: Classification Convolutional neural network Deep learning Image processing Soil-transmitted helminths This is an open access article under the CC BY-SA license. Corresponding Author: Rio Andika Malik Department of Digital Business, Faculty of Economic Business and Social Sciences, University of Perintis Indonesia Adinegoro Road KM15, Lubuk Buaya, Padang, West Sumatera, 25173, Indonesia Email: rioandikamalik@upertis.ac.id 1. INTRODUCTION Soil-transmitted helminth (STH) infections are significant and pose a substantial global health problem, especially in impoverished nations with subpar sanitation and limited access to healthcare facilities [1]–[7]. STH is a group of parasitic worms commonly found in the soil that can infect humans through direct contact with contaminated soil or by consuming food or water contaminated with worm eggs [8]–[12]. Infections induced by STH are among the most widespread infections in the world, with an estimated 1.5 billion infected people or 24% of the world's population [2], [3], [12]–[16]. In Indonesia, STH infections remain a major concern due to close links with socio-economic conditions, personal hygiene, and environmental factors [12], [14], [17]–[20]. The prevalence of worm infections among children aged 1-12 years in several provinces is relatively high, ranging from 30% to 90% [13], [14], [21]. The primary challenge that this research attempts to solve is how inadequate the present approaches are for identifying infections caused by STH, especially in areas with poor resources and restricted access to