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All Journal Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Prosiding SNATIF JUMANJI (Jurnal Masyarakat Informatika Unjani) Jurnal Informasi dan Komputer Jurnal Cendikia Jurnal ICT : Information Communication & Technology Jurnal Informatika dan Rekayasa Elektronik Tematik : Jurnal Teknologi Informasi Komunikasi Jurnal Ilmiah Wahana Pendidikan Istech Journal (International Science And Technology Journal) Jurnal Nasional Teknik Elektro dan Teknologi Informasi Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Electrician : Jurnal Rekayasa dan Teknologi Elektro Journal Of Informatics And Busisnes JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Agus Komarudin
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Religion Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Mathematics Social Sciences Other

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Explainable Artificial Intelligence Model for Pneumonia Detection: A Hybrid CNN-ViT and Grad-CAM Atika Hendryani; Vita Nurdinawati; Agus Komarudin
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 14 No 4: November 2025
Publisher : This journal is published by the Department of Electrical and Information Engineering, Faculty of Engineering, Universitas Gadjah Mada.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jnteti.v14i4.19822

Abstract

Pneumonia detection through medical imaging presents a significant challenge, particularly in regions with limited access to healthcare professionals. This study presents an explainable artificial intelligence (XAI) model that integrates convolutional neural network (CNN) and vision transformer (ViT) to enhance the accuracy of pneumonia diagnosis using chest X-ray images. The proposed research aims to enhance diagnostic accuracy by providing explanations through gradient-weighted class activation mapping (Grad-CAM) visualization. The methodology includes image preprocessing, local feature extraction via CNN, and global spatial relationship modelling using ViT. The model was trained on a preprocessed chest X-ray dataset and evaluated using standard performance metrics such as accuracy, precision, recall, and F1 score. The proposed CNN-ViT model was assessed using chest X-ray datasets for pneumonia detection. The experimental results demonstrated that the model achieved an accuracy of 96.5%, precision of 96%, recall of 96%, and F1 score of 94%, These results indicate that the integration of CNN and ViT effectively enhances classification performance and provides a reliable tool for medical image analysis. Furthermore, Grad-CAM visualizations highlight the critical regions in the images that influence the model’s predictions, thereby enhancing interpretability. Compared to conventional models, this approach offers improved transparency in AI-driven diagnostics. Consequently, the proposed model represents a promising and reliable diagnostic tool, particularly beneficial in underserved or remote areas with limited medical infrastructure. Additionally, this research opens opportunities for the development of transparent and XAI-based diagnostic systems.
Co-Authors Aek Mustofa Aghnie Kurnia Alda Amorita Azza Atika Hendryani Bambang Suparapto Esmeralda C Djamal Esmeralda C. Djamal Ferry Kurniawan Galih Rakasiwi Hadiana, Asep Id Hafiz, Aliy Hartono, Rudi Ifo Wahyu Pratama Iman Muhdi Iskandar Iskandar Kuncoro Buwono Kurniawan Ikhsan Mia Narulita Anggraeni Mohamad Aris Hardianto Muhammad Dwi Laksono Nenza Nurfirmansyah Nuari Anisa Sivi Rasyid Ibnu Prasetyo Rezki Yuniarti Sri Hutamy Novianti Tacbir Hendro Pudjiantoro Vita Nurdinawati Wahidah, Nina Ikhwati Wina Witanti Yulison H. Crisnanto