Skin lesions identification using deep convolutional neural network

Tasneem Alkarakatly; Shatha Eidhah; Miaad Al-Sarawani; Alaa Al-Sobhi; Mohsin Bilal

doi:10.1109/AECT47998.2020.9194205

Skin lesions identification using deep convolutional neural network

Tasneem Alkarakatly
, Shatha Eidhah
, Miaad Al-Sarawani
, Alaa Al-Sobhi
, Mohsin Bilal

Umm Al-Qura University

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

23 Scopus citations

Abstract

Skin cancer is a serious public health problem due to its increasing incidence and subsequent high mortality rate. Deep learning is one of the most important approaches in image analysis used to detect melanoma skin cancer. In this paper, we propose a 5-layer Convolutional Neural Network (CNN) for classifying skin lesions of three categories, including melanoma belonging to deadly skin cancer. The CNN based classifier trained and tested on the PH2 dataset of Dermoscopic images, which is developed for research and benchmarking purposes. The proposed model was evaluated by four well-known performance measures namely, classification accuracy, sensitivity, specificity and area under the curve (AUC). It achieved almost 95% accuracy, 94% sensitivity, 97% specificity, and 100% AUC on the test set. Moreover, in one case of the experiment, the proposed model achieved 100% accuracy.

Original language	English
Title of host publication	2019 International Conference on Advances in the Emerging Computing Technologies, AECT 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728144528
DOIs	https://doi.org/10.1109/AECT47998.2020.9194205
State	Published - Feb 2020
Externally published	Yes
Event	2019 International Conference on Advances in the Emerging Computing Technologies, AECT 2019 - AlMadinah, AlManawarrah, Saudi Arabia Duration: 10 Feb 2020 → …

Publication series

Name	2019 International Conference on Advances in the Emerging Computing Technologies, AECT 2019

Conference

Conference	2019 International Conference on Advances in the Emerging Computing Technologies, AECT 2019
Country/Territory	Saudi Arabia
City	AlMadinah, AlManawarrah
Period	10/02/20 → …

UN SDGs

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

SDG 3 Good Health and Well-being

Keywords

Convolutional neural network (CNN)
Dermoscopy images
Melanoma detection
Skin lesion classification

Access to Document

10.1109/AECT47998.2020.9194205

Cite this

Alkarakatly, T., Eidhah, S., Al-Sarawani, M., Al-Sobhi, A., & Bilal, M. (2020). Skin lesions identification using deep convolutional neural network. In 2019 International Conference on Advances in the Emerging Computing Technologies, AECT 2019 Article 9194205 (2019 International Conference on Advances in the Emerging Computing Technologies, AECT 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/AECT47998.2020.9194205

Alkarakatly, Tasneem ; Eidhah, Shatha ; Al-Sarawani, Miaad et al. / Skin lesions identification using deep convolutional neural network. 2019 International Conference on Advances in the Emerging Computing Technologies, AECT 2019. Institute of Electrical and Electronics Engineers Inc., 2020. (2019 International Conference on Advances in the Emerging Computing Technologies, AECT 2019).

@inproceedings{18e46c5832a9487f97fd2fe9ff1ff546,

title = "Skin lesions identification using deep convolutional neural network",

abstract = "Skin cancer is a serious public health problem due to its increasing incidence and subsequent high mortality rate. Deep learning is one of the most important approaches in image analysis used to detect melanoma skin cancer. In this paper, we propose a 5-layer Convolutional Neural Network (CNN) for classifying skin lesions of three categories, including melanoma belonging to deadly skin cancer. The CNN based classifier trained and tested on the PH2 dataset of Dermoscopic images, which is developed for research and benchmarking purposes. The proposed model was evaluated by four well-known performance measures namely, classification accuracy, sensitivity, specificity and area under the curve (AUC). It achieved almost 95\% accuracy, 94\% sensitivity, 97\% specificity, and 100\% AUC on the test set. Moreover, in one case of the experiment, the proposed model achieved 100\% accuracy.",

keywords = "Convolutional neural network (CNN), Dermoscopy images, Melanoma detection, Skin lesion classification",

author = "Tasneem Alkarakatly and Shatha Eidhah and Miaad Al-Sarawani and Alaa Al-Sobhi and Mohsin Bilal",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2019 International Conference on Advances in the Emerging Computing Technologies, AECT 2019 ; Conference date: 10-02-2020",

year = "2020",

month = feb,

doi = "10.1109/AECT47998.2020.9194205",

language = "English",

series = "2019 International Conference on Advances in the Emerging Computing Technologies, AECT 2019",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2019 International Conference on Advances in the Emerging Computing Technologies, AECT 2019",

address = "United States",

}

Alkarakatly, T, Eidhah, S, Al-Sarawani, M, Al-Sobhi, A & Bilal, M 2020, Skin lesions identification using deep convolutional neural network. in 2019 International Conference on Advances in the Emerging Computing Technologies, AECT 2019., 9194205, 2019 International Conference on Advances in the Emerging Computing Technologies, AECT 2019, Institute of Electrical and Electronics Engineers Inc., 2019 International Conference on Advances in the Emerging Computing Technologies, AECT 2019, AlMadinah, AlManawarrah, Saudi Arabia, 10/02/20. https://doi.org/10.1109/AECT47998.2020.9194205

Skin lesions identification using deep convolutional neural network. / Alkarakatly, Tasneem; Eidhah, Shatha; Al-Sarawani, Miaad et al.
2019 International Conference on Advances in the Emerging Computing Technologies, AECT 2019. Institute of Electrical and Electronics Engineers Inc., 2020. 9194205 (2019 International Conference on Advances in the Emerging Computing Technologies, AECT 2019).

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

TY - GEN

T1 - Skin lesions identification using deep convolutional neural network

AU - Alkarakatly, Tasneem

AU - Eidhah, Shatha

AU - Al-Sarawani, Miaad

AU - Al-Sobhi, Alaa

AU - Bilal, Mohsin

PY - 2020/2

Y1 - 2020/2

N2 - Skin cancer is a serious public health problem due to its increasing incidence and subsequent high mortality rate. Deep learning is one of the most important approaches in image analysis used to detect melanoma skin cancer. In this paper, we propose a 5-layer Convolutional Neural Network (CNN) for classifying skin lesions of three categories, including melanoma belonging to deadly skin cancer. The CNN based classifier trained and tested on the PH2 dataset of Dermoscopic images, which is developed for research and benchmarking purposes. The proposed model was evaluated by four well-known performance measures namely, classification accuracy, sensitivity, specificity and area under the curve (AUC). It achieved almost 95% accuracy, 94% sensitivity, 97% specificity, and 100% AUC on the test set. Moreover, in one case of the experiment, the proposed model achieved 100% accuracy.

AB - Skin cancer is a serious public health problem due to its increasing incidence and subsequent high mortality rate. Deep learning is one of the most important approaches in image analysis used to detect melanoma skin cancer. In this paper, we propose a 5-layer Convolutional Neural Network (CNN) for classifying skin lesions of three categories, including melanoma belonging to deadly skin cancer. The CNN based classifier trained and tested on the PH2 dataset of Dermoscopic images, which is developed for research and benchmarking purposes. The proposed model was evaluated by four well-known performance measures namely, classification accuracy, sensitivity, specificity and area under the curve (AUC). It achieved almost 95% accuracy, 94% sensitivity, 97% specificity, and 100% AUC on the test set. Moreover, in one case of the experiment, the proposed model achieved 100% accuracy.

KW - Convolutional neural network (CNN)

KW - Dermoscopy images

KW - Melanoma detection

KW - Skin lesion classification

UR - https://www.scopus.com/pages/publications/85092399164

U2 - 10.1109/AECT47998.2020.9194205

DO - 10.1109/AECT47998.2020.9194205

M3 - Conference contribution

AN - SCOPUS:85092399164

T3 - 2019 International Conference on Advances in the Emerging Computing Technologies, AECT 2019

BT - 2019 International Conference on Advances in the Emerging Computing Technologies, AECT 2019

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2019 International Conference on Advances in the Emerging Computing Technologies, AECT 2019

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ER -

Alkarakatly T, Eidhah S, Al-Sarawani M, Al-Sobhi A, Bilal M. Skin lesions identification using deep convolutional neural network. In 2019 International Conference on Advances in the Emerging Computing Technologies, AECT 2019. Institute of Electrical and Electronics Engineers Inc. 2020. 9194205. (2019 International Conference on Advances in the Emerging Computing Technologies, AECT 2019). doi: 10.1109/AECT47998.2020.9194205

Skin lesions identification using deep convolutional neural network

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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