Deep convolutional neural network (DCNN) for skin cancer classification

Nour Aburaed; Alavikunhu Panthakkan; Mina Al-Saad; Saad Ali Amin; Wathiq Mansoor

doi:10.1109/ICECS49266.2020.9294814

Deep convolutional neural network (DCNN) for skin cancer classification

Nour Aburaed, Alavikunhu Panthakkan, Mina Al-Saad, Saad Ali Amin, Wathiq Mansoor

Electronic And Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

22 Citations (Scopus)

Abstract

Skin cancer is one of the most threatening types of cancer, with an increasing rates throughout the decade. Detecting and classifying skin cancer in its early stages provides better chances for treatment. In the recent years, Convolutional Neural Networks (CNNs) emerged as a powerful solution that aids the diagnosis of skin cancer. In this paper, Human Against Machine (HAM) 10000 dataset is used to demonstrate skin cancer classification strategy. VGG16, VGG19, and a Deep CNN proposed in this paper are implemented, trained, and evaluated. The dataset pre-processing steps and methodology are illustrated, and the network parameters and training process are explained. The performance of all three networks are compared in terms of the average overall accuracy and loss.

Original language	English
Title of host publication	ICECS 2020 - 27th IEEE International Conference on Electronics, Circuits and Systems, Proceedings
Place of Publication	Piscataway, N.J.
Publisher	IEEE
ISBN (Electronic)	9781728160443
DOIs	https://doi.org/10.1109/ICECS49266.2020.9294814
Publication status	Published - 23 Nov 2020
Event	27th IEEE International Conference on Electronics, Circuits and Systems, ICECS 2020 - Glasgow, United Kingdom Duration: 23 Nov 2020 → 25 Nov 2020

Conference

Conference	27th IEEE International Conference on Electronics, Circuits and Systems, ICECS 2020
Country/Territory	United Kingdom
City	Glasgow
Period	23/11/20 → 25/11/20

Keywords

biomedical imaging
classification
convolutional neural networks
deep learning
image processing
skin cancer

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10.1109/ICECS49266.2020.9294814

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@inproceedings{9b017e7c18aa4983be3a90f52df39c38,

title = "Deep convolutional neural network (DCNN) for skin cancer classification",

abstract = "Skin cancer is one of the most threatening types of cancer, with an increasing rates throughout the decade. Detecting and classifying skin cancer in its early stages provides better chances for treatment. In the recent years, Convolutional Neural Networks (CNNs) emerged as a powerful solution that aids the diagnosis of skin cancer. In this paper, Human Against Machine (HAM) 10000 dataset is used to demonstrate skin cancer classification strategy. VGG16, VGG19, and a Deep CNN proposed in this paper are implemented, trained, and evaluated. The dataset pre-processing steps and methodology are illustrated, and the network parameters and training process are explained. The performance of all three networks are compared in terms of the average overall accuracy and loss.",

keywords = "biomedical imaging, classification, convolutional neural networks, deep learning, image processing, skin cancer",

author = "Nour Aburaed and Alavikunhu Panthakkan and Mina Al-Saad and Amin, {Saad Ali} and Wathiq Mansoor",

year = "2020",

month = nov,

day = "23",

doi = "10.1109/ICECS49266.2020.9294814",

language = "English",

booktitle = "ICECS 2020 - 27th IEEE International Conference on Electronics, Circuits and Systems, Proceedings",

publisher = "IEEE",

note = "27th IEEE International Conference on Electronics, Circuits and Systems, ICECS 2020 ; Conference date: 23-11-2020 Through 25-11-2020",

}

Aburaed, N, Panthakkan, A, Al-Saad, M, Amin, SA & Mansoor, W 2020, Deep convolutional neural network (DCNN) for skin cancer classification. in ICECS 2020 - 27th IEEE International Conference on Electronics, Circuits and Systems, Proceedings. IEEE, Piscataway, N.J., 27th IEEE International Conference on Electronics, Circuits and Systems, ICECS 2020, Glasgow, United Kingdom, 23/11/20. https://doi.org/10.1109/ICECS49266.2020.9294814

TY - GEN

T1 - Deep convolutional neural network (DCNN) for skin cancer classification

AU - Aburaed, Nour

AU - Panthakkan, Alavikunhu

AU - Al-Saad, Mina

AU - Amin, Saad Ali

AU - Mansoor, Wathiq

PY - 2020/11/23

Y1 - 2020/11/23

N2 - Skin cancer is one of the most threatening types of cancer, with an increasing rates throughout the decade. Detecting and classifying skin cancer in its early stages provides better chances for treatment. In the recent years, Convolutional Neural Networks (CNNs) emerged as a powerful solution that aids the diagnosis of skin cancer. In this paper, Human Against Machine (HAM) 10000 dataset is used to demonstrate skin cancer classification strategy. VGG16, VGG19, and a Deep CNN proposed in this paper are implemented, trained, and evaluated. The dataset pre-processing steps and methodology are illustrated, and the network parameters and training process are explained. The performance of all three networks are compared in terms of the average overall accuracy and loss.

AB - Skin cancer is one of the most threatening types of cancer, with an increasing rates throughout the decade. Detecting and classifying skin cancer in its early stages provides better chances for treatment. In the recent years, Convolutional Neural Networks (CNNs) emerged as a powerful solution that aids the diagnosis of skin cancer. In this paper, Human Against Machine (HAM) 10000 dataset is used to demonstrate skin cancer classification strategy. VGG16, VGG19, and a Deep CNN proposed in this paper are implemented, trained, and evaluated. The dataset pre-processing steps and methodology are illustrated, and the network parameters and training process are explained. The performance of all three networks are compared in terms of the average overall accuracy and loss.

KW - biomedical imaging

KW - classification

KW - convolutional neural networks

KW - deep learning

KW - image processing

KW - skin cancer

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U2 - 10.1109/ICECS49266.2020.9294814

DO - 10.1109/ICECS49266.2020.9294814

M3 - Conference contribution book

AN - SCOPUS:85099457985

BT - ICECS 2020 - 27th IEEE International Conference on Electronics, Circuits and Systems, Proceedings

PB - IEEE

CY - Piscataway, N.J.

T2 - 27th IEEE International Conference on Electronics, Circuits and Systems, ICECS 2020

Y2 - 23 November 2020 through 25 November 2020

ER -

Deep convolutional neural network (DCNN) for skin cancer classification

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Keywords

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