Automated Diagnosis of Skin Cancer.

Simple item page
dc.contributor.authorNyiramugisha, Shallon
dc.contributor.authorNuwahereza, Godfrey
dc.date.accessioned2025-01-15T08:39:38Z
dc.date.available2025-01-15T08:39:38Z
dc.date.issued2024
dc.description.abstractSkin cancer is one of the most common types of cancer worldwide. Over the past few years, different approaches have been proposed to deal with automated skin cancer detection. Nonetheless, most of them are based only on dermoscopic images and do not take into account the patient's clinical information, an important clue towards clinical diagnosis. In this work, we present an approach to fill this gap. First, we introduce a new dataset composed of clinical images, collected using smartphones, and clinical data related to the patient. Next, we propose a straightforward method that includes an aggregation mechanism in well-known deep-learning models to combine features from images and clinical data. Last, we carry out experiments to compare the models’ performance with and without using this mechanism. The results present an improvement of approximately 7% in balanced accuracy when the aggregation method is applied. Overall, the impact of clinical data on models’ performance is significant and shows the importance of including these features in automated skin cancer detection.
dc.identifier.citationNyiramugisha, S. & Nuwahereza, G. (2024). Automated Diagnosis of Skin Cancer. Kabale: Kabale University.
dc.identifier.urihttp://hdl.handle.net/20.500.12493/2777
dc.language.isoen
dc.publisherKabale University
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 United Statesen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/us/
dc.subjectAutomated Diagnosis
dc.subjectSkin Cancer
dc.titleAutomated Diagnosis of Skin Cancer.
dc.typeThesis

Files

Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
Nyiramugisha Shallon-Nuwahereza Godfrey-2024-KCS-Research Dissertation.pdf
Size:
729.16 KB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

Faculty of Education (FEDU)