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Juwanda, Farikhin
Departemen Matematika FSM UNDIP
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Journal : Scientific Journal of Informatics

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A Hybrid YOLOv8-ResNet50 Architecture for Enhanced Cardiomegaly Prediction from Chest X-rays Faudin, Arif Nur; Farikhin, Farikhin; Syafei, Wahyul Amien
Scientific Journal of Informatics Vol. 12 No. 4: November 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i4.35225

Abstract

Abstract. Objective: This study aims to develop a reliable deep learning architecture for predicting cardiomegaly by integrating the ResNet-50 backbone into the YOLOv8 object detection framework, overcoming the challenges of detecting subtle anatomical variations and low-contrast features often found in chest radiographs. Methods: This study used a publicly available chest X-ray dataset, with rigorous data annotation to establish ground truth for the heart and thoracic cavity regions. Preprocessing included resizing input images to 640×640 pixels, automatic orientation correction, and an 80:20 data split between training and testing. Real-time data augmentation was applied to the training set. The ResNet-YOLOv8 hybrid model was trained for 150 epochs with optimized hyperparameters (learning rate, momentum, weight decay, loss weight), and performance was evaluated using metrics such as mAP, precision, recall, and confusion matrix results. Results: The experimental results show that the proposed architecture achieves high accuracy in detecting cardiomegaly, with mAP50-95 of 0.7578, precision of 0.9955, recall of 0.9962, F1 score of 0.9959, and inference latency of only 4.5 ms/img. This model is more optimal than the standard YOLOv8 variant in both accuracy and computational efficiency. Innovation: The integration of ResNet-50 into YOLOv8 significantly improves feature extraction capabilities for chest X-ray images, enabling the recognition of fine anatomical details with high precision. This innovative hybrid approach advances automated cardiomegaly detection, offering potential for large-scale, real-time implementation in clinical settings and contributing to the development of advanced AI-powered diagnostic tools.
Co-Authors A. Haris A. Rusgiyono Acep Irham Gufroni Adi Ariyo Munandar Adi Suliantoro Ahmad Abdul Chamid Ahmad Lubis Ghozali Aprilia, Maita Aris Sugiharto Arnelli Arnelli B. Raharjo Bambang Irawanto Bambang Irawanto Bambang Subeno Bayu Surarso Bayu Surarso Beta Noranita Bibit Waluyo Aji Budi Warsito Carolin Carolin Catur Edi Widodo D. Ispriyanti Didik Setiyo Widodo Dinar Mutiara Kusumo Nugraheni Djuwandi Djuwandi DONNY IRAWAN MUSTABA Dwinta Rahmallah Pulukadang, Dwinta Rahmallah E. Setiawati Erikha Feriyanto Erlin Dwi Endarwati, Erlin Dwi Esti Wijayanti, Esti F. Ariyanto Faozi, Safik Faudin, Arif Nur Fauzi, Irza Nur Feriyanto, Erikha Ferry Jie, Ferry Fitika Andraini H. Sutanto Heny Maslahah, Heny I. Marhaendrajaya Iswahyudi Joko Suprayitno J. E. Suseno Kartono . Keszya Wabang Kusworo Kusworo Laily Rahmania, Laily LM Fajar Israwan, LM Fajar M. Izzati M. Nur Madani, Faiq Mansur Mansur Meryta Febrilian Fatimah, Meryta Febrilian Mustafid Mustafid Neza Zhevira Septiani Nikken Prima Puspita Nikken Prima Puspita Nur Khasanah Oky Dwi Nurhayati Pangestika, Vidya Dwi Pradana, Fadli Dony Prantiastio Prastio, Wahyu Tedi Priyono Priyono Purwanto Purwanto R. Hariyati R. Hastuti Rachmat Gernowo Ratri Wulandari Retno Kusumaningrum Rezki Kurniati, Rezki Rineka Brylian Akbar Satriani Rinta Kridalukmana Robertus Heri Sulistyo Utomo S. Tana Safik Faozi, Safik Satriani, Rineka Brylian Akbar Siti Khabibah Siti Khabibah Sri Wahyuni Sugito Sugito Suhartono Suhartono Sunarsih . Suparti Suparti T. Windarti Titi Udjiani SRRM Toni Prahasto Udjiani , Titi Udjiani S.R.R.M, Titi Usman, Carissa Devina Uswatun Khasanah W. H. Rahmanto Wahyul Amien Syafei Wardani, Novita Koes Wardianto, Wardianto Warsito , Budi Wicaksono, Mahad Wyne Mumtaazah Putri Yosza Dasril Yully Estiningsih Z. Muhlisin