The Possibility of Deep Learning-Based, Computer-Aided Skin Tumor Classifiers

Abstract

The incidence of skin tumors has steadily increased. Although most are benign and do not affect survival, some of the more malignant skin tumors present a lethal threat if a delay in diagnosis permits them to become advanced. Ideally, an inspection by an expert dermatologist would accurately detect malignant skin tumors in the early stage; however, it is not practical for every single patient to receive intensive screening by dermatologists. To overcome this issue, many studies are ongoing to develop dermatologist-level, computer-aided diagnostics. Whereas, many systems that can classify dermoscopic images at this dermatologist-equivalent level have been reported, a much fewer number of systems that can classify conventional clinical images have been reported thus far. Recently, the introduction of deep-learning technology, a method that automatically extracts a set of representative features for further classification has dramatically improved classification efficacy. This new technology has the potential to improve the computer classification accuracy of conventional clinical images to the level of skilled dermatologists. In this review, this new technology and present development of computer-aided skin tumor classifiers will be summarized.

Classifying Melanoma Skin Lesions Using Convolutional Spiking Neural Networks With Unsupervised STDP Learning Rule

“…The prior knowledge and complicated image preprocessing which are very necessary in the image classification using traditional ML algorithms, are no longer greatly demanded. Some classifiers based on deep learning methods have shown to classify images of skin cancer with the performance comparable to the level of skilled dermatologists [8]. Thus CNNs have the potential to help develop dermatologist-level, computer-aided fast skin lesion classifiers.…”

Artificial intelligence in dermatology: the ‘unsupervised’ learning

“…As it is difficult to obtain expert‐labelled dermatological images, the unsupervised method has an advantage over the supervised method when a large amount of unlabelled data is present . However, this method suffers from the lack of an ‘expert’ touch during the training.…”

News and Notes

“…3 As it is difficult to obtain expert-labelled dermatological images, the unsupervised method has an advantage over the supervised method when a large amount of unlabelled data is present. 4 However, this method suffers from the lack of an 'expert' touch during the training. The combination method, or 'semisupervised learning', has also been introduced, which utilizes a small amount of labelled data and a larger amount of unlabelled data.…”

“…The combination method, or 'semisupervised learning', has also been introduced, which utilizes a small amount of labelled data and a larger amount of unlabelled data. 4 The unlabelled data are categorized using the algorithm obtained from the labelled data, and the same categorized labelled data can be used to retrain the algorithm (Figure 1c). Thus, this method has the advantage of using the knowledge of the professionals while minimizing the use of resources during the labelling of a large amount of data.…”