A smart dermoscope design using artificial neural network

Serkan Turkeli; Mehmet Salih Oguz; Salim Burak Abay; Tufan Kumbasar; Hüseyin Tanzer Atay; Kenan Kaan Kurt

doi:10.1109/IDAP.2017.8090211

A smart dermoscope design using artificial neural network

Serkan Turkeli, Mehmet Salih Oguz, Salim Burak Abay, Tufan Kumbasar, Hüseyin Tanzer Atay, Kenan Kaan Kurt

Department of Control and Automation Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

5 Citations (Scopus)

Abstract

Melanoma is certainly the deadliest skin cancer. Clinicians try to detect melanoma at early stages in order to increase the successful treatment rate by using dermoscopes. We have designed a digital dermoscope that is both mobile and highly sensitive for automatic classification. We developed an accurate image processing software and a learning program that uses artificial neural network learning algorithm. A dataset of 200 images were used for training and 12 features were extracted. We considered common nevus, atypical nevus and melanoma as our diagnostic results. By doing that, we acquire three sensitivity and specifity values for each of the outputs. For the common nevus detection, SE = 100%, SP = 98.3%, for the atypical nevus detection, SE = 95%, SP = 97.5%, for the melanoma detection, SE = 92.5%, SP = 98.75%.

Original language	English
Title of host publication	IDAP 2017 - International Artificial Intelligence and Data Processing Symposium
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781538618806
DOIs	https://doi.org/10.1109/IDAP.2017.8090211
Publication status	Published - 30 Oct 2017
Event	2017 International Artificial Intelligence and Data Processing Symposium, IDAP 2017 - Malatya, Turkey Duration: 16 Sept 2017 → 17 Sept 2017

Publication series

Name	IDAP 2017 - International Artificial Intelligence and Data Processing Symposium

Conference

Conference	2017 International Artificial Intelligence and Data Processing Symposium, IDAP 2017
Country/Territory	Turkey
City	Malatya
Period	16/09/17 → 17/09/17

Bibliographical note

Publisher Copyright:
© 2017 IEEE.

Keywords

Classification
Dermoscope
Feature selection
Melanoma
Nevus
Segmentation

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/IDAP.2017.8090211

Cite this

Turkeli, S., Oguz, M. S., Abay, S. B., Kumbasar, T., Atay, H. T., & Kurt, K. K. (2017). A smart dermoscope design using artificial neural network. In IDAP 2017 - International Artificial Intelligence and Data Processing Symposium Article 8090211 (IDAP 2017 - International Artificial Intelligence and Data Processing Symposium). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IDAP.2017.8090211

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title = "A smart dermoscope design using artificial neural network",

abstract = "Melanoma is certainly the deadliest skin cancer. Clinicians try to detect melanoma at early stages in order to increase the successful treatment rate by using dermoscopes. We have designed a digital dermoscope that is both mobile and highly sensitive for automatic classification. We developed an accurate image processing software and a learning program that uses artificial neural network learning algorithm. A dataset of 200 images were used for training and 12 features were extracted. We considered common nevus, atypical nevus and melanoma as our diagnostic results. By doing that, we acquire three sensitivity and specifity values for each of the outputs. For the common nevus detection, SE = 100%, SP = 98.3%, for the atypical nevus detection, SE = 95%, SP = 97.5%, for the melanoma detection, SE = 92.5%, SP = 98.75%.",

keywords = "Classification, Dermoscope, Feature selection, Melanoma, Nevus, Segmentation",

author = "Serkan Turkeli and Oguz, {Mehmet Salih} and Abay, {Salim Burak} and Tufan Kumbasar and Atay, {H{\"u}seyin Tanzer} and Kurt, {Kenan Kaan}",

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day = "30",

doi = "10.1109/IDAP.2017.8090211",

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Turkeli, S, Oguz, MS, Abay, SB, Kumbasar, T, Atay, HT & Kurt, KK 2017, A smart dermoscope design using artificial neural network. in IDAP 2017 - International Artificial Intelligence and Data Processing Symposium., 8090211, IDAP 2017 - International Artificial Intelligence and Data Processing Symposium, Institute of Electrical and Electronics Engineers Inc., 2017 International Artificial Intelligence and Data Processing Symposium, IDAP 2017, Malatya, Turkey, 16/09/17. https://doi.org/10.1109/IDAP.2017.8090211

A smart dermoscope design using artificial neural network. / Turkeli, Serkan; Oguz, Mehmet Salih; Abay, Salim Burak et al.
IDAP 2017 - International Artificial Intelligence and Data Processing Symposium. Institute of Electrical and Electronics Engineers Inc., 2017. 8090211 (IDAP 2017 - International Artificial Intelligence and Data Processing Symposium).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Turkeli, Serkan

AU - Oguz, Mehmet Salih

AU - Abay, Salim Burak

AU - Kumbasar, Tufan

AU - Atay, Hüseyin Tanzer

AU - Kurt, Kenan Kaan

PY - 2017/10/30

Y1 - 2017/10/30

N2 - Melanoma is certainly the deadliest skin cancer. Clinicians try to detect melanoma at early stages in order to increase the successful treatment rate by using dermoscopes. We have designed a digital dermoscope that is both mobile and highly sensitive for automatic classification. We developed an accurate image processing software and a learning program that uses artificial neural network learning algorithm. A dataset of 200 images were used for training and 12 features were extracted. We considered common nevus, atypical nevus and melanoma as our diagnostic results. By doing that, we acquire three sensitivity and specifity values for each of the outputs. For the common nevus detection, SE = 100%, SP = 98.3%, for the atypical nevus detection, SE = 95%, SP = 97.5%, for the melanoma detection, SE = 92.5%, SP = 98.75%.

AB - Melanoma is certainly the deadliest skin cancer. Clinicians try to detect melanoma at early stages in order to increase the successful treatment rate by using dermoscopes. We have designed a digital dermoscope that is both mobile and highly sensitive for automatic classification. We developed an accurate image processing software and a learning program that uses artificial neural network learning algorithm. A dataset of 200 images were used for training and 12 features were extracted. We considered common nevus, atypical nevus and melanoma as our diagnostic results. By doing that, we acquire three sensitivity and specifity values for each of the outputs. For the common nevus detection, SE = 100%, SP = 98.3%, for the atypical nevus detection, SE = 95%, SP = 97.5%, for the melanoma detection, SE = 92.5%, SP = 98.75%.

KW - Classification

KW - Dermoscope

KW - Feature selection

KW - Melanoma

KW - Nevus

KW - Segmentation

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Turkeli S, Oguz MS, Abay SB, Kumbasar T, Atay HT, Kurt KK. A smart dermoscope design using artificial neural network. In IDAP 2017 - International Artificial Intelligence and Data Processing Symposium. Institute of Electrical and Electronics Engineers Inc. 2017. 8090211. (IDAP 2017 - International Artificial Intelligence and Data Processing Symposium). doi: 10.1109/IDAP.2017.8090211

A smart dermoscope design using artificial neural network

Abstract

Publication series

Conference

Bibliographical note

Keywords

UN SDGs

Access to Document

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