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Automating Skin Disease Diagnosis Using Image Classification | 12282
ISSN:2167-7964

OMICS Journal of Radiology
Open Access

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Automating skin disease diagnosis using image classification

International Conference on Radiology & Imaging

Damilola Ayokunle Okuboyejo

AcceptedAbstracts: OMICS J Radiology

DOI: 10.4172/2167-7964.S1.004

Abstract
Several attempts have been made to implement traditional telemedicine across the world especially in the developing countries, but the efforts has been characterized with challenges such as high-cost of sustaining telemedicine solutions and insufficient access to medical expertise when needed. Cancerous Skin disease such as melanoma and nevi typically results from environmental factors (such as exposure to sunlight) among other causes. The necessary tools needed for early detection of these diseases are still not a reality in most African communities. In recent years, there have been high expectations for techniques such as Dermoscopy or Epiluminiscence Light Microscopy (ELM) in aiding diagnosis; however evaluation of pigmented skin lesions using ELM is not only non-affordable by most of African communities but also complex and highly subjective, thus motivating researches in diagnosis automation. This study focuses on designing and modeling a system that will collate past Pigmented Skin Lesion (PSL) image results, their analysis, corresponding observations and conclusions by medical experts. This wealth of information would be used as a library. A part of the system would use computational intelligence technique to analyze, process, and classify the image library data based on texture and other possible morphological features of the images. Trained medical personnel in a remote location can use mobile data acquisition devices (such as cell phone) to generate images of PSL, supply such images as input to the proposed system, which in turns should intelligently be able to specify the malignancy or benign status of the imaged PSL.
Biography
Damilola Ayokunle Okuboyejo completed his first Master?s Degree at the age of 28 from University of Lagos, Nigeria. Couple with his years in Software Industry, He is currently engaged in research in the domain of Computational Intelligence and Image Analysis towards his second Master?s Degree at Tshwane University of Technology where he also delivers presentations on System Analysis and Design as a lecturer.
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