Work place: Department of Computer Science and Engineering, Ladoke Akintola University of Technology, Ogbomoso
E-mail: ajisafedaniel@gmail.com
Website:
Research Interests: Computer systems and computational processes, Computer Vision, Data Structures and Algorithms
Biography
Daniel Ajisafe is currently a Masters student at the African Masters in Machine Intelligence Program funded by Google and Facebook. He worked as a Data scientist at KPMG Nigeria where he used machine learning and advanced analytical algorithms to draw insights from structured and semi-structured data. His research interest is in computer vision and machine learning applications in healthcare.
By Justice O. Emuoyibofarhe Daniel Ajisafe Ronke S. Babatunde Meinel Christoph
DOI: https://doi.org/10.5815/ijieeb.2020.02.04, Pub. Date: 8 Apr. 2020
Malignant melanoma is the most dangerous kind of skin cancer. It is mostly misidentified as benign lesion. The chance of surviving melanoma disease is high if detected early. In recent years, deep convolutional neural networks have attracted great attention owing to its outstanding performance in recognizing and classifying images. This research work performs a comparative analysis of three different convolutional neural networks (CNN) trained on skin cancerous and non-cancerous images, namely: a custom 3-layer CNN, VGG-16 CNN, and Google Inception V3.
Google Inception V3 achieved the best result, with training and test accuracy of 90% and 81% respectively and a sensitivity of 84%. This work contribution is mainly in the development of an android application that uses Google Inception V3 model for early detection of skin cancer.
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