Abstract
One facial skin problem is redness. On site examination currently relies on examination through direct observations conducted by doctors and the patient's medical history. However, some patients are reluctant to consult with a doctor because of shame or prohibitive costs. This study attempts to utilize digital image processing algorithms to analyze the patient's facial skin condition automatically, especially redness detection in the face image. The method used for detecting red objects on face skin for this research is Redness method. The output of the Redness method will be optimized by feature selection based on area, mean intensity of the RGB color space, and mean intensity of the Hue Intensity. The dataset used in this research consists of 35 facial images. The sensitivity, specificity, and accuracy are used to measure the detection performance. The performance achieved 54%, 99.1%, and 96.2% for sensitivity, specificity, and accuracy, respectively, according to dermatologists. Meanwhile, according to PT. AVO personnel, the performance achieved 67.4%, 99.1%, and 97.7%, for sensitivity, specificity, and accuracy, respectively. Based on the result, the system is good enough to detect redness in facial images.
ABSTRAK: Salah satu masalah kulit wajah adalah kemerahan muka. Pemeriksaan di lokasi kini bergantung pada pemeriksaan melalui pemerhatian langsung yang dilakukan oleh doktor dan sejarah perubatan pesakit. Namun, sebilangan pesakit enggan berunding dengan doktor kerana rasa malu atau kos yang terhad. Kajian ini cuba membuat sistem pengesanan kemerahan wajah yang dapat menganalisis keadaan wajah, terutama kemerahan, melalui gambar kulit wajah. Kaedah yang digunakan untuk mengesan objek merah pada kulit wajah bagi penyelidikan ini adalah kaedah Kemerahan. Keluaran kaedah Kemerahan akan dioptimumkan dengan pemilihan ciri berdasarkan luas, intensiti min RGB, dan intensiti min Hue Intensity. Set data yang digunakan dalam penyelidikan ini terdiri daripada 35 gambar wajah. Nilai pengesahan yang digunakan adalah kepekaan, kekhususan, dan ketepatan. Hasil yang diperoleh berdasarkan pakar dermatologi masing-masing adalah 54%, 99.1%, dan 96.2% untuk kepekaan, kekhususan, dan ketepatan. Sementara itu, PT. Selain itu, menurut kakitangan AVO 67.4%, 99.1%, dan 97.7%, bagi kepekaan, kekhususan, dan ketepatan, masing-masing. Berdasarkan dapatan kajian ini, sistem ini cukup baik bagi mengesan kemerahan pada gambar wajah.
Subject
Applied Mathematics,General Engineering,General Chemical Engineering,General Computer Science
Cited by
1 articles.
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1. Multiple Skin-Disease Classification Based on Machine Vision Using Transfer Learning Approach;2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT);2023-07-06