Investigating usefulness and usability of medical content-based image retrieval tool for skin lesion images

Resource type
Thesis type
(Thesis) M.Sc.
Date created
2018-09-26
Authors/Contributors
Abstract
Content-Based Image Retrieval (CBIR) is an application of computer vision techniques for searching an existing database for visually similar entries to a specific query image. One application of CBIR in the dermatology domain is displaying a set of visually similar images with a pathology-confirmed diagnosis for a given query image of a skin lesion. Recently, CBIR algorithms using machine learning techniques have gained more attention; however, we lack insights into how interactive CBIR decision support tools are actually perceived by end users. We present the design and evaluation of a CBIR user interface for dermoscopic skin images, and investigate users' classification accuracy, and users' confidence, trust, timing, and the system's educational value. Our study with 34 non-medical users indicates that CBIR enables users to make significantly more accurate and confident classifications on new skin lesion images from four categories commonly observed in clinical practice: nevus, seborrheic keratosis, basal cell carcinoma, and malignant melanoma.
Identifier
etd19922
Copyright statement
Copyright is held by the author.
Permissions
This thesis may be printed or downloaded for non-commercial research and scholarly purposes.
Scholarly level
Supervisor or Senior Supervisor
Thesis advisor: Atkins, Stella
Thesis advisor: Chilana, Parmit
Member of collection
Model
English