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Open AccessArticle

Development and Experimental Evaluation of Machine-Learning Techniques for an Intelligent Hairy Scalp Detection System

1
Science and Engineering Faculty, Queensland University of Technology, Brisbane 4000, Australia
2
Department of Electronic Engineering, Southern Taiwan University of Science and Technology, Tainan 71005, Taiwan
*
Authors to whom correspondence should be addressed.
Appl. Sci. 2018, 8(6), 853; https://doi.org/10.3390/app8060853
Received: 14 May 2018 / Revised: 20 May 2018 / Accepted: 21 May 2018 / Published: 23 May 2018
(This article belongs to the Special Issue Advanced Intelligent Imaging Technology)
Deep learning has become the most popular research subject in the fields of artificial intelligence (AI) and machine learning. In October 2013, MIT Technology Review commented that deep learning was a breakthrough technology. Deep learning has made progress in voice and image recognition, image classification, and natural language processing. Prior to deep learning, decision tree, linear discriminant analysis (LDA), support vector machines (SVM), k-nearest neighbors algorithm (K-NN), and ensemble learning were popular in solving classification problems. In this paper, we applied the previously mentioned and deep learning techniques to hairy scalp images. Hairy scalp problems are usually diagnosed by non-professionals in hair salons, and people with such problems may be advised by these non-professionals. Additionally, several common scalp problems are similar; therefore, non-experts may provide incorrect diagnoses. Hence, scalp problems have worsened. In this work, we implemented and compared the deep-learning method, the ImageNet-VGG-f model Bag of Words (BOW), with machine-learning classifiers, and histogram of oriented gradients (HOG)/pyramid histogram of oriented gradients (PHOG) with machine-learning classifiers. The tools from the classification learner apps were used for hairy scalp image classification. The results indicated that deep learning can achieve an accuracy of 89.77% when the learning rate is 1 × 10−4, and this accuracy is far higher than those achieved by BOW with SVM (80.50%) and PHOG with SVM (53.0%). View Full-Text
Keywords: deep learning; machine learning; support vector machine (SVM); images classification; images recognition; hairy scalp diagnosis and analysis deep learning; machine learning; support vector machine (SVM); images classification; images recognition; hairy scalp diagnosis and analysis
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Wang, W.-C.; Chen, L.-B.; Chang, W.-J. Development and Experimental Evaluation of Machine-Learning Techniques for an Intelligent Hairy Scalp Detection System. Appl. Sci. 2018, 8, 853.

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