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All Journal Journal of Electronics, Electromedical Engineering, and Medical Informatics Prosiding SeNTIK STI&K
Pamungkas, Kiki Adi
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media

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Comparative Analysis of Feature Extraction Techniques for Facial Paralysis Classification Himawan, Salamet Nur; Suheryadi, Adi; Cahyanto, Kurnia Adi; Sitanggang, Filemon; Pamungkas, Kiki Adi
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 2 (2025): April
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i2.645

Abstract

Facial paralysis significantly affects a person's ability to communicate and perform essential functions. Facial paralysis classification plays a vital role in the diagnosis and monitoring of facial disorders. Traditional diagnostic methods often rely on subjective evaluations, leading to inconsistent outcomes. The aim of this study is to evaluate and compare various feature extraction techniques to enhance the accuracy and efficiency of facial paralysis classification. The primary contribution of this research lies in its comprehensive analysis of texture-based (Local Binary Patterns, Histogram of Oriented Gradients, Gabor filters) and geometric feature extraction methods, providing insights into their respective strengths and limitations for facial paralysis detection. This study utilizes the YouTube Facial Palsy (YFP) dataset, comprising annotated images of paralyzed and non-paralyzed faces. Preprocessing included resizing images to 128x128 pixels to standardize inputs. Feature extraction methods were applied to the dataset, and the extracted features were classified using machine learning algorithms, including Support Vector Machines (SVM), Random Forest (RF), and k-Nearest Neighbors (KNN). Model performance was evaluated using accuracy, precision, recall, and F1-score metrics. The best-performing method achieved an accuracy of 85% using HOG features combined with KNN. The findings highlight that texture-based methods, particularly HOG, excel in capturing subtle asymmetries, while geometric features offer computational efficiency and interpretability with fewer extracted features. This study underscores the importance of selecting suitable feature extraction methods based on task requirements, and emphasizes the potential of hybrid approaches to leverage the strengths of different methods. Future research should explore advanced geometric descriptors and integrate hybrid models to enhance clinical applicability
DETEKSI KELUMPUHAN WAJAH MENGGUNAKAN YOLO DENGAN IMPLEMENTASI WEB Pamungkas, Kiki Adi; Himawan, Salamet Nur; Suheryadi, Adi; Cahyanto, Kurnia Adi; Sitanggang, Filemon
Prosiding Seminar SeNTIK Vol. 8 No. 1 (2024): Prosiding SeNTIK 2024
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat

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Abstract

Kelumpuhan wajah merupakan ketidakmampuan seseorang untuk menggerakkan otot-otot pada wajah.. Deteksi awal kelumpuhan wajah sangat penting untuk memberikan intervensi medis yang cepat dan mencegah perburukan kondisi pasien. Dalam penelitian ini, kami mengembangkan sebuah sistem berbasis deep learning yang menggunakan model YOLO (You Only Look Once) untuk mendeteksi paralisis secara otomatis. Sistem ini diintegrasikan dengan sebuah aplikasi web, yang memungkinkan pengguna untuk mengunggah citra untuk dilakukan deteksi secara real-time. Pengujian terhadap sistem ini menunjukkan akurasi yang tinggi dalam mendeteksi kelumpuhan wajah pada citra. Hasil penelitian menunjukkan bahwa model YOLO dapat membedakan wajah yang lumpuh dengan baik, terlihat pada precision dan recall yang mencapai nilai 0.91 dan 0.97
Co-Authors Himawan, Salamet Nur Kurnia Adi Cahyanto Sitanggang, Filemon Suheryadi, Adi