Acne detection by ensemble neural networks
Acne detection, utilizing prior knowledge to diagnose acne severity, number or position through facial images, plays a very important role in medical diagnoses and treatment for patients with skin problems. Recently, deep learning algorithms were introduced in acne detection to improve detection pre...
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sg-ntu-dr.10356-1670232023-07-14T15:47:24Z Acne detection by ensemble neural networks Zhang, Hang Ma, Tianyi School of Materials Science and Engineering Engineering::Materials Acne Detection Ensemble Model Acne detection, utilizing prior knowledge to diagnose acne severity, number or position through facial images, plays a very important role in medical diagnoses and treatment for patients with skin problems. Recently, deep learning algorithms were introduced in acne detection to improve detection precision. However, it remains challenging to diagnose acne based on the facial images of patients due to the complex context and special application scenarios. Here, we provide an ensemble neural network composed of two modules: (1) a classification module aiming to calculate the acne severity and number; (2) a localization module aiming to calculate the detection boxes. This ensemble model could precisely predict the acne severity, number, and position simultaneously, and could be an effective tool to help the patient self-test and assist the doctor in the diagnosis. Published version 2023-05-10T01:45:43Z 2023-05-10T01:45:43Z 2022 Journal Article Zhang, H. & Ma, T. (2022). Acne detection by ensemble neural networks. Sensors, 22(18), 6828-. https://dx.doi.org/10.3390/s22186828 1424-8220 https://hdl.handle.net/10356/167023 10.3390/s22186828 36146177 2-s2.0-85138373346 18 22 6828 en Sensors © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). application/pdf |
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Singapore Singapore |
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Engineering::Materials Acne Detection Ensemble Model |
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Engineering::Materials Acne Detection Ensemble Model Zhang, Hang Ma, Tianyi Acne detection by ensemble neural networks |
| description |
Acne detection, utilizing prior knowledge to diagnose acne severity, number or position through facial images, plays a very important role in medical diagnoses and treatment for patients with skin problems. Recently, deep learning algorithms were introduced in acne detection to improve detection precision. However, it remains challenging to diagnose acne based on the facial images of patients due to the complex context and special application scenarios. Here, we provide an ensemble neural network composed of two modules: (1) a classification module aiming to calculate the acne severity and number; (2) a localization module aiming to calculate the detection boxes. This ensemble model could precisely predict the acne severity, number, and position simultaneously, and could be an effective tool to help the patient self-test and assist the doctor in the diagnosis. |
| author2 |
School of Materials Science and Engineering |
| author_facet |
School of Materials Science and Engineering Zhang, Hang Ma, Tianyi |
| format |
Article |
| author |
Zhang, Hang Ma, Tianyi |
| author_sort |
Zhang, Hang |
| title |
Acne detection by ensemble neural networks |
| title_short |
Acne detection by ensemble neural networks |
| title_full |
Acne detection by ensemble neural networks |
| title_fullStr |
Acne detection by ensemble neural networks |
| title_full_unstemmed |
Acne detection by ensemble neural networks |
| title_sort |
acne detection by ensemble neural networks |
| publishDate |
2023 |
| url |
https://hdl.handle.net/10356/167023 |
| _version_ |
1772825787497447424 |