Acne Detection by Ensemble Neural Networks.
Hang ZhangTianyi MaPublished in: Sensors (Basel, Switzerland) (2022)
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.
Keyphrases
- neural network
- deep learning
- convolutional neural network
- hidradenitis suppurativa
- loop mediated isothermal amplification
- real time pcr
- machine learning
- label free
- healthcare
- end stage renal disease
- chronic kidney disease
- artificial intelligence
- ejection fraction
- climate change
- soft tissue
- case report
- newly diagnosed