Abstract
Malignant melanoma is the deadliest form of skin cancer and, in recent years, is rapidly growing in terms of the incidence worldwide rate. The most effective approach to targeted treatment is early diagnosis. Deep learning algorithms, specifically convolutional neural networks, represent a methodology for the image analysis and representation. They optimize the features design task, essential for an automatic approach on different types of images, including medical. In this paper, we adopted pretrained deep convolutional neural networks architectures for the image representation with purpose to predict skin lesion melanoma. Firstly, we applied a transfer learning approach to extract image features. Secondly, we adopted the transferred learning features inside an ensemble classification context. Specifically, the framework trains individual classifiers on balanced subspaces and combines the provided predictions through statistical measures. Experimental phase on datasets of skin lesion images is performed and results obtained show the effectiveness of the proposed approach with respect to state-of-the-art competitors.
Highlights
Among the types of malignant cancer, melanoma is the deadliest form of skin cancer and its incidence rate is growing rapidly around the world
In order to overcome the aforementioned issues, in this paper, we introduce a novel framework based on transfer deep learning and ensemble classification for melanoma detection
In order to produce compliant performance, the settings included in well-known melanoma classification methods, in which the main critical issue concerns the features extraction for image representation, are adopted
Summary
Among the types of malignant cancer, melanoma is the deadliest form of skin cancer and its incidence rate is growing rapidly around the world. Due to the similarity of the various skin lesions (melanoma and not-melanoma) [1], the visual analysis could be unsuitable and would lead to a wrong diagnosis. In this regard, image processing and artificial intelligence tools can provide a fundamental aid to a step of automatic classification [2]. The last issue and not the least, is the additional skin conditions such as hair, veins or variations due to image capturing To this end, many solutions have been provided to improve the task
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.