Abstract
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%).
Highlights
In recent years, machine-learning techniques have been widely used in computer vision, image recognition, stock market analysis, medical diagnosis, natural language processing, voice/speech recognition, etc
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 Bag of Words (BOW) with support vector machines (SVM) (80.50%) and pyramid histogram of oriented gradients (PHOG)
Since the 1990s, multilayer perceptron (MLP) has is for training and optimizing the models, and the testing dataset is for evaluating the efficiency of encountered strong competition from the simpler support vector machines (SVM)
Summary
Machine-learning techniques have been widely used in computer vision, image recognition, stock market analysis, medical diagnosis, natural language processing, voice/speech recognition, etc. We use the diagnosis and analysis of hairy scalps as a case study for machine-learning techniques. We test whether machine learning technology can be applied to hairy scalp detection. We believe that machine learning-based AI image processing methods should be able to effectively solve the aforementioned hairy scalp detection problem. To the best of our knowledge, this is the first study to apply modern machine-learning techniques to the diagnosis and analysis of hairy scalp problems.
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