PROJECT TITLE :

High-Level Intuitive Features (HLIFs) for Intuitive Skin Lesion Description

ABSTRACT:

A set of high-level intuitive options (HLIFs) is proposed to quantitatively describe melanoma in standard camera pictures. Melanoma is that the deadliest kind of skin cancer. With rising incidence rates and subjectivity in current clinical detection strategies, there is a would like for melanoma call support systems. Feature extraction may be a critical step in melanoma call support systems. Existing feature sets for analyzing normal camera pictures are comprised of low-level features, that exist in high-dimensional feature spaces and limit the system's ability to convey intuitive diagnostic rationale. The proposed HLIFs were designed to model the ABCD criteria commonly used by dermatologists such that each HLIF represents a person's-observable characteristic. As such, intuitive diagnostic rationale will be conveyed to the user. Experimental results show that concatenating the proposed HLIFs with a full low-level feature set increased classification accuracy, and that HLIFs were in a position to separate the information higher than low-level options with statistical significance. An example of a graphical interface for providing intuitive rationale is given.


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