Skin lesion classification with ensembles of deep convolutional neural networks

Publication date: Available online 10 August 2018Source: Journal of Biomedical InformaticsAuthor(s): Balazs HarangiAbstractSkin cancer is a major public health problem with over 123,000 newly diagnosed cases worldwide in every year. Melanoma is the deadliest form of skin cancer, responsible for over 9,000 deaths in the United States each year. Thus, reliable automatic melanoma screening systems would provide a great help for clinicians to detect the malignant skin lesions as early as possible. In the last five years, the efficiency of deep learning-based methods increased dramatically and their performances seem to outperform conventional image processing methods in classification tasks. However, this type of machine learning-based approaches have a main drawback, namely they require thousands of labeled images per classes for their training. In this paper, we investigate how we can create an ensemble of deep convolutional neural networks to improve further their individual accuracies in the task of classifying dermoscopy images into the three classes melanoma, nevus, and seborrheic keratosis when we have no opportunity to train them on adequate number of annotated images. To achieve high classification accuracy, we fuse the outputs of the classification layers of four different deep neural network architectures. More specifically, we propose the aggregation of robust convolutional neural networks (CNNs) into one framework, where the final classification is achieved based on ...
Source: Journal of Biomedical Informatics - Category: Information Technology Source Type: research