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Refined Residual Deep Convolutional Network for Skin Lesion Classification
Faculty
Computer Science
Year:
2022
Type of Publication:
ZU Hosted
Pages:
Authors:
Khalied Mohamed Hosny
Staff Zu Site
Abstract In Staff Site
Journal:
Journal of Digital Imaging Springer
Volume:
Keywords :
Refined Residual Deep Convolutional Network , Skin
Abstract:
Skin cancer is the most common type of cancer that affects humans and is usually diagnosed by initial clinical screening, which is followed by dermoscopic analysis. Automated classification of skin lesions is still a challenging task because of the high visual similarity between melanoma and benign lesions. This paper proposes a new residual deep convolutional neural network (RDCNN) for skin lesions diagnosis. The proposed neural network is trained and tested using six well-known skin cancer datasets, PH2, DermIS and Quest, MED-NODE, ISIC2016, ISIC2017, and ISIC2018. Three different experiments are carried out to measure the performance of the proposed RDCNN. In the first experiment, the proposed RDCNN is trained and tested using the original dataset images without any pre-processing or segmentation. In the second experiment, the proposed RDCNN is tested using segmented images. Finally, the utilized trained model in the second experiment is saved and reused in the third experiment as a pre-trained model. Then, it is trained again using a different dataset. The proposed RDCNN shows significant high performance and outperforms the existing deep convolutional networks.
Author Related Publications
Khalied Mohamed Hosny, "SEMANTIC REPRESENTATION OF MUSIC DATABASE USING NEW ONTOLOGY-BASED SYSTEM", Journal of Theoretical and Applied Information Technology, 2020
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Khalied Mohamed Hosny, "Building a New Semantic Social Network Using Semantic Web-Based Techniques", ِASPG, 2021
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Khalied Mohamed Hosny, "New Graphical Ultimate Processor for Mapping Relational Database to Resource Description Framework", IEEE, 2022
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Khalied Mohamed Hosny, "Fast computation of accurate Zernike moments", Springer, 2008
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Khalied Mohamed Hosny, "Accurate Computation of QPCET for Color Images in Different Coordinate Systems", SPIE, 2017
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Department Related Publications
Walid Ibrahim Ibrahim Khedr, "Ad-hoc on Demand Authentication Chain Protocol - An Authentication Protocol for Ad-Hoc Networks", Institute for Systems and Technologies of Information, Control and Communication, 2015
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Khalied Mohamed Hosny, "Robust Color Image Hashing Using Quaternion Polar Complex Exponential Transform for Image Authentication", Springer, 2018
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Ehab Roshdy Mohamed, "Efficient compression of volumetric medical images using Legendre moments and differential evolution", Springer, 2020
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Asmaa Mohamed Khalid Mohamed Abbas, "Efficient compression of volumetric medical images using Legendre moments and differential evolution", Springer, 2020
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Khalied Mohamed Hosny, "Efficient compression of volumetric medical images using Legendre moments and differential evolution", Springer, 2020
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