In order to classify skin lesions, many efforts have been made to create various automated diagnostic systems. For that purpose many efforts have been put in creating various automated diagnostics systems Nowadays, with the rapid advancements in deep learning, Vision Transformers have emerged as powerful models for image processing and analysis purposes. This type of model has already proved useful for cancer detection and classification tasks in particular. However, the complexity and variability of skin lesions present significant challenges in accurately classifying them. Integrating the concept of fractal dimension into Vision Transformers can potentially improve their performance by capturing the intricate structural patterns of skin lesions. This paper aims to explore the integration of fractal dimension metrics into a Vision Transformer for skin cancer classification. The problem at hand is to investigate the integration of fractal dimension metrics into the existing Vision Transformer architecture for the accurate classification of skin lesions as cancerous or non-cancerous. Fractal dimensions provide a measure of the complexity and irregularity of an object, which can be informative in characterizing skin cancer lesions. We aim to research possability and ways of incorporating fractal dimension metrics into the Vision Transformer model for results improvements.
In the current research, we continue our previous study regarding motion-based user biometric verification, which consumes sensory data. Sensory-based verification systems empower the continuous authentication narrative – as physiological biometric methods mainly based on photo or video input meet a lot of difficulties in implementation. The research aims to analyze how various components of sensor data from an accelerometer affect and contribute to defining the process of unique person motion patterns and understanding how it may express the human behavioral patterns with different activity types. The study used the recurrent long-short-term-memory autoencoder as a baseline model. The choice of model was based on our previous research. The research results have shown that various data components contribute differently to the verification process depending on the type of activity. However, we conclude that a single sensor data source may not be enough for a robust authentication system. The multimodal authentication system should be proposed to utilize and aggregate the input streams from multiple sensors as further research.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.