Melanoma detection using regular convolutional neural networks

Aya Abu Ali, Hasan Al-Marzouqi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

32 Scopus citations

Abstract

In this paper, we propose a method for classifying melanoma images into benign and malignant using Convolutional Neural Networks (CNNs). Having an automated method for melanoma detection will assist dermatologists in the early diagnosis of this type of skin cancer. A regular convolutional network employing a modest number of parameters is used to detect melanoma images. The architecture is used to classify the dataset of the ISBI 2016 challenge in melanoma classification. The dataset was not segmented or cropped prior to classification. The proposed method was then evaluated for accuracy, sensitivity and specificity. Comparisons with the winning entry in the competition demonstrate that one can achieve a performance level comparable to state-of-the-art using standard convolutional neural network architectures that employ a lower number of parameters.

Original languageBritish English
Title of host publication2017 International Conference on Electrical and Computing Technologies and Applications, ICECTA 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-5
Number of pages5
ISBN (Electronic)9781538608722
DOIs
StatePublished - 28 Jun 2017
Event2017 International Conference on Electrical and Computing Technologies and Applications, ICECTA 2017 - Ras Al Khaimah, United Arab Emirates
Duration: 21 Nov 201723 Nov 2017

Publication series

Name2017 International Conference on Electrical and Computing Technologies and Applications, ICECTA 2017
Volume2018-January

Conference

Conference2017 International Conference on Electrical and Computing Technologies and Applications, ICECTA 2017
Country/TerritoryUnited Arab Emirates
CityRas Al Khaimah
Period21/11/1723/11/17

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