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Dr. Rasha Orban Mahmoud Abdulkarim :: Publications:

Automated Multi-Class Skin Cancer Classification through Concatenated Deep Learning Models
Authors: Rana Hassan Bedeir, Rasha Orban Mahmoud, Hala H. Zayed
Year: 2022
Keywords: Classification, Deep learning, HAM10000, ResNet50, Skin cancer
Journal: IAES International Journal of Artificial Intelligence (IJ-AI).
Volume: 11
Issue: 2
Pages: 764-772
Publisher: Not Available
Local/International: International
Paper Link:
Full paper Not Available
Supplementary materials Not Available

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%.

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