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Journal : bit-Tech

YOLOv11n-Based Deep Learning Approach for Detecting Fractures in Pediatric X-Rays Ahmed Mohammed Mohammed Nasser Alghaili; Izzati Muhimmah
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3155

Abstract

Fracture detection in pediatric wrist radiographs is challenging due to incomplete skeletal ossification, small bone structures, and subtle hairline fractures that are frequently missed in clinical practice, while growth plate radiolucency often mimics fracture appearance. This study evaluates YOLOv11n, a lightweight deep learning architecture with Spatial Pyramid Feature Fusion (SPFF) modules optimized for small-object detection, for automated pediatric wrist fracture identification. The model was trained and validated on the GRAZPEDWRI-DX benchmark dataset comprising 20,327 pediatric wrist radiographs (14,269 training, 4,048 validation, 2,010 test images) using transfer learning and conservative augmentation strategies. YOLOv11n achieved mAP@50 of 0.936 on validation and 0.945 on test sets, with precision of 0.905–0.918 and recall of 0.869–0.871, demonstrating improved accuracy compared to previous YOLOv8 implementations (mAP@50 ≈ 0.92) with 40–60% faster inference. End-to-end processing averaged 3.8 ms per image on NVIDIA Tesla T4 hardware, supporting real-time clinical applications. The mAP@50-95 of approximately 0.56 indicates reduced localization accuracy under stricter IoU criteria, primarily for hairline fractures. Error analysis revealed that 62% of false negatives were non-displaced hairline fractures, while 58% of false positives occurred near growth plate regions. YOLOv11n provides favorable balance between diagnostic accuracy and computational efficiency for pediatric fracture detection. However, prospective multi-institutional validation, integration of multi-view fusion strategies, and incorporation of age-specific anatomical priors are necessary before clinical deployment to enhance detection of subtle fracture presentations and reduce growth plate misclassifications.
Co-Authors -, Indrayanti adelia sukma ardana Adeniar Yusnina Agung Purwo Wicaksono Agus Darmawan Ahmed Mohammed Mohammed Nasser Alghaili Ainan Nur Andhika Pratama Arif Sulaksana Putra Arrie Kurniawardhani Astrianty, Ledy Elsera Ause Labellapansa, Ause Azhari, M. Fauzan Aziz, Muhammad Thariq Dan Jeric Arcega Rustia Darmawan - Deny Rahmalianto Dhina Puspasari Wijaya, Dhina Puspasari Dhomas Hatta Fudholi Dimas Panji Eka Jalaputra Dimas Panji Eka Jalaputra Erika RE Denton Erlina Marfianti, Erlina Fajarwibowo, Dhimas Fajriyah, Rohmanul Franz, Annafi’ Galang Prihadi Mahardhika, Galang Prihadi Gracianna Devi, Micha Heksaputra, Dadang Helmi Roichatul Jannah Herlambang, Penggalih M Herman Yuliansyah Ika Fidianingsih Ika Firdianingsih Indrayanti, Indrayanti Indrayanti, Indrayanti Indri Dwi Febriani Irawan, Dudi Ivantoni, Redha Jamhari Jamhari July Arifianto Kariyam - Khairul Hafidh Komariyuli Anwariyah Kusumaningrum, Shinta Dewi Lailiyatus Sa'adah Lalu Mutawalli Lantip Rujito Latriwulansuci, Latriwulansuci Lestari, Tri Mukti Linda Rosita Lizda Iswari Meilita . Moh Reza Syaifur Rizal Mufti Syawaludin Muhammad Atnang Muhammad Khalifah Milano Nazarudin, Zohan Novian Mahardika Putra Novyan Lusiyana Nurastuti Wijareni Nurdana Ahmad Fadil Oktavianto, Hardian Penggalih M Herlambang Penggalih M Herlambang Prabowo, Mei Rahadian Kurniawan Raisha Amini Rakhmawati, Restu Ratri Agung Nugraheni Reyer Zwiggelaar Riyanto, Didik Rizki Surtiyan Surya Rizky Eka Listanto Rohmatul Fajriyah Rohmatul Fajriyah Rositasari, Annisa S, Andika Bayu Sahriani Sasmito, Dinda Eling K Septia Rani Silvia Nurul Fata Smulders, Marinus J.M. Sri Kusumadewi Sri Winiarti TAUFIQ HIDAYAT Tien Budi Febriani Tito Yuwono, Tito Ummi Athiyah Wayan Tunas Artama Wigatning, Lestari H Wilda, Anisa Nurul Yasmini Fitriyati Yasmini Fitriyati, Yasmini Yuliansyah, Herman Yulianti, Ana ZAINUL ARIFIN