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All Journal JUITA : Jurnal Informatika Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) RABIT: Jurnal Teknologi dan Sistem Informasi Univrab JURNAL INTEGRASI JURNAL REKAYASA TEKNOLOGI INFORMASI Jurnal Teknoinfo MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer ComTech: Computer, Mathematics and Engineering Applications Jurnal Teknologi Komputer dan Sistem Informasi METIK JURNAL Jurnal Ilmiah Ilmu Komputer Fakultas Ilmu Komputer Universitas Al Asyariah Mandar Jurnal Manajemen Informatika dan Sistem Informasi Jurnal Informatika dan Rekayasa Elektronik Journal of Applied Engineering and Technological Science (JAETS) Sains, Aplikasi, Komputasi dan Teknologi Informasi Jurnal Sistem Informasi dan Sistem Komputer JNANALOKA International Journal of Engineering, Science and Information Technology Jurnal J-PEMAS JAIA - Journal of Artificial Intelligence and Applications JTKSI (Jurnal Teknologi Komputer dan Sistem Informasi)
Muhammad Bambang Firdaus
Fakultas Ilmu Komputer Dan Teknologi Informasi, Universitas Mulawarman, Samarinda
Aerospace Engineering Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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Deep Learning Methods for Pneumonia Detection Using ConvNeXt Architecture Akhmad Irsyad; Muhammad Bambang Firdaus; Gubtha Mahendra Putra; Putut Pamilih Widagdo; Hario Jati Setiady; Muhammad Fawaz Saputra; Muhammad Abdillah Rahmat
International Journal of Engineering, Science and Information Technology Vol 6, No 1 (2026)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v6i1.1797

Abstract

Pneumonia remains a major respiratory infection with high mortality rates, especially in regions with limited access to medical specialists. Early and accurate diagnosis plays a critical role in reducing fatal outcomes and improving patient management. Chest X ray imaging is widely used as a primary diagnostic modality, yet interpretation relies heavily on experienced radiologists, whose availability is often insufficient to meet clinical demand. This condition motivates the development of automated pneumonia detection systems based on artificial intelligence. This study investigates the application of deep learning for pneumonia classification using chest X ray images, with a focus on the ConvNeXt architecture. ConvNeXt represents a modern neural network design that integrates structural advantages from Vision Transformers with the efficiency of traditional Convolutional Neural Networks, enabling strong feature extraction while maintaining computational efficiency. The research evaluates multiple ConvNeXt variants, including Tiny, Small, Base, and Large, to analyze the relationship between model complexity and classification performance. ResNet50 is employed as a baseline model to provide a fair comparative assessment against a widely used convolutional architecture. Model evaluation uses accuracy, precision, recall, and F measure to ensure balanced measurement across different classification outcomes and class distributions. Experimental results indicate that ConvNeXt Tiny achieves the highest overall performance, reaching an accuracy of 97.69 percent while using a relatively low number of parameters. This outcome highlights the efficiency of lightweight architectures for medical image analysis tasks. The findings demonstrate that ConvNeXt Tiny delivers strong discriminative capability with reduced computational requirements, making it suitable for deployment in resource constrained clinical environments. This study contributes evidence supporting the effectiveness of modern deep learning architectures for automated pneumonia detection and provides insight into model selection for practical medical imaging applications.
Co-Authors Ade Chrisvitandy Ade Riyanda Putra Agustin Ahmad Zamsuri Alameka, Faza Anam, M Khairul Anjas, Andi Anton Prafanto Anton Prafanto Arda Yunianta Arief Hidayat Awang Harsa Kridalaksana Basani, Yuniarta Budiman, Edy Bunga Nanti Pikir Damar Sanggara Habibie Eddy Kurniawan Pradana Efrizoni, Lusiana Erlin Fadli Suandi Fahrul Yamani Fajar Ramadhan Faza Alameka Fernando Elda Pati Gandhi Dwi Laksono Gubtha Mahendra Putra Hadi Asnal, Hadi Hairah, Ummul Hamdani Hamdani - Hamdani Hamdani Hanif Aulia Hario Jati Setiady Hasan J. Alyamani Hasyim Asyari Herman Santoso Pakpahan Ike Yunia Pasa Indah Mukhlis Tamara Irfan Putra Pratama Irsyad, Akhmad Irwanda Syahputra Junadhi Junadhi Kartina Diah K. W. Langgeng Ari Kusuma Lathifah Lathifah Lathifah Lathifah Latifah Lucky Lhaura Van FC, Lucky Lhaura M Syauqi Hafizh Medi Taruk Mohammad Yasin Muh Fathir Fahrezah Muhammad Abdillah Rahmat Muhammad Akbar Muhammad Budi Saputra Muhammad Fawaz Saputra Muhammad Fikri Anshori Muhammad Nopri Fauzi Muhammad Nur Ihwan Muhammad Wisdan Pratama Putra Nariza Wanti Wulan Sari Novi Yona Sidratul Munti Nurjayadi Nurjayadi Putut Pamilih Widagdo Rahmaddeni Rahmaddeni Rahmaddeni Rahmaddeni Renol Sulle Rosmasari Rosmasari Rozy Rivaldi Ruri Widi Priatna Sofiansyah Fadli Susi Erlinda Taruk, Medi Tejawati, Andi Tiopan Herny Manto Gultom Tri Putri Lestari Triyani Arita Fitri Ummul Hairah Wardhana, Reza Wati, Masna Widians, Joan Angelina Wirta Agustin Yaakub, Saleh Yuda Irawan Yudi Kurniawan Yumami, Eva Zainal Arifin