Staff Members - Benha University
Automated Multi-Class Skin Cancer Classification through Concatenated Deep Learning Models
IAES International Journal of Artificial Intelligence (IJ-AI). • 2022
Publication Information
Authors
Rana Hassan Bedeir, Rasha Orban Mahmoud, Hala H. Zayed
Keywords
Classification,
Deep learning,
HAM10000,
ResNet50,
Skin cancer
Journal
IAES International Journal of Artificial Intelligence (IJ-AI).
Publisher
Not Available
Volume
11
Issue
2
Pages
764-772
publication.type
International
Paper Link
Open Link
Supplementary Materials
Not Available
Abstract
Skin cancer is the most annoying type of cancer diagnosis according to its fast spread to various body areas, so it was necessary
to establish computer-assisted diagnostic support systems. State-of-the-art classifiers based on convolutional neural networks
(CNNs) are used to classify images of skin cancer. This paper tries to get the most accurate model to classify and detect skin
cancer types from seven different classes using deep learning techniques; ResNet-50, VGG-16, and the merged model of these
two techniques through the concatenate function. The performance of the proposed model was evaluated through a set of
experiments on the HAM10000 database. The proposed system has succeeded in achieving a recognition accuracy of up to
94.14%.
to establish computer-assisted diagnostic support systems. State-of-the-art classifiers based on convolutional neural networks
(CNNs) are used to classify images of skin cancer. This paper tries to get the most accurate model to classify and detect skin
cancer types from seven different classes using deep learning techniques; ResNet-50, VGG-16, and the merged model of these
two techniques through the concatenate function. The performance of the proposed model was evaluated through a set of
experiments on the HAM10000 database. The proposed system has succeeded in achieving a recognition accuracy of up to
94.14%.