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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal TIMES Sinkron : Jurnal dan Penelitian Teknik Informatika JURNAL MEDIA INFORMATIKA BUDIDARMA Building of Informatics, Technology and Science Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) EXPLORER Bulletin of Data Science Bulletin of Informatics and Data Science
Alkhairi, Putrama
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Journal : International Journal of Electrical and Computer Engineering

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Bone fracture classification using convolutional neural network architecture for high-accuracy image classification Solikhun, Solikhun; Windarto, Agus Perdana; Alkhairi, Putrama
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp6466-6477

Abstract

This research introduces an innovative method for fracture classification using convolutional neural networks (CNN) for high-accuracy image classification. The study addresses the need to improve the subjectivity and limited accuracy of traditional methods. By harnessing the capability of CNNs to autonomously extract hierarchical features from medical images, this research surpasses the limitations of manual interpretation and existing automated systems. The goal is to create a robust CNN-based methodology for precise and reliable fracture classification, potentially revolutionizing current diagnostic practices. The dataset for this research is sourced from Kaggle's public medical image repository, ensuring a diverse range of fracture images. This study highlights CNNs' potential to significantly enhance diagnostic precision, leading to more effective treatments and improved patient care in orthopedics. The novelty lies in the unique application of CNN architecture for fracture classification, an area not extensively explored before. Testing results show a significant improvement in classification accuracy, with the proposed model achieving an accuracy rate of 0.9922 compared to ResNet50's 0.9844. The research suggests that adopting CNN-based systems in medical practice can enhance diagnostic accuracy, optimize treatment plans, and improve patient outcomes.
Co-Authors Agus Perdana Windarto Ahmad Zamsuri, Ahmad Ariani, Dhini Asyifah, Hazha Hikmah Batubara, Ela Roza Efendi, Muhamad Masjun Heru Satria Tambunan, Heru Satria Ilmi R.H. Zer, P.P.P.A.N.W. Fikrul Indra Riyana Rahadjeng Indra Riyana Rahadjeng Irawan Irawan Muhammad Noor Hasan Siregar Muliadi Nasution, Alisa Putri Amanda Nuroini, Indi Pardede, Akim Manaor Hara Parinduri, Syawaluddin Kadafi Poningsih Poningsih, Poningsih Prihandoko, P Qurniawan, Hendry Raharjo, Mokhamad Ramdhani Rika Rosnelly, Rika Saragih, Farah Yusni Solikhun Solikhun, Solikhun Tambunan, Fazli Nugraha Wanayaumini, W Zakarias Situmorang