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This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering Indonesian Journal of Electrical Engineering and Computer Science
Hossam El-Din Moustafa
Mansoura University
Computer Science & IT Electrical & Electronics Engineering

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Journal : International Journal of Electrical and Computer Engineering

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Deep segmentation of the liver and the hepatic tumors from abdomen tomography images Nermeen Elmenabawy; Mervat El-Seddek; Hossam El-Din Moustafa; Ahmed Elnakib
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 1: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i1.pp303-310

Abstract

A pipelined framework is proposed for accurate, automated, simultaneous segmentation of the liver as well as the hepatic tumors from computed tomography (CT) images. The introduced framework composed of three pipelined levels. First, two different transfers deep convolutional neural networks (CNN) are applied to get high-level compact features of CT images. Second, a pixel-wise classifier is used to obtain two output-classified maps for each CNN model. Finally, a fusion neural network (FNN) is used to integrate the two maps. Experimentations performed on the MICCAI’2017 database of the liver tumor segmentation (LITS) challenge, result in a dice similarity coefficient (DSC) of 93.5% for the segmentation of the liver and of 74.40% for the segmentation of the lesion, using a 5-fold cross-validation scheme. Comparative results with the state-of-the-art techniques on the same data show the competing performance of the proposed framework for simultaneous liver and tumor segmentation.
A deep convolutional structure-based approach for accurate recognition of skin lesions in dermoscopy images Shimaa Fawzy; Hossam El-Din Moustafa; Ehab H. AbdelHay; Mohamed Maher Ata
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i5.pp5792-5803

Abstract

One-third of all cancer diagnoses worldwide are skin malignancies. One of the most common tumors, skin cancer can develop from a variety of dermatological conditions and is subdivided into different categories based on its textile, color, body, and other morphological characteristics. The most effective strategy to lower the mortality rate of melanoma is early identification because skin cancer incidence has been on the rise recently. In order to categorize dermoscopy images into the four diagnosis classifications of melanoma, benign, malignant, and human against machine (HAM) not melanoma, this research suggests a computer-aided diagnosis (CAD) system. Experimental results show that the suggested approach enabled 97.25% classification accuracy. In order to automate the identification of skin cancer and expedite the diagnosis process in order to save a life, the proposed technique offers a less complex and cutting-edge framework.
Co-Authors Ahmed Elnakib Ehab H. AbdelHay Eman AbdElhalim Fahmi Khalifa Gehad Ahmed Saleh Mahmoud El-Zalabany Mervat El-Seddek Mervat El-Seddek Mohamed Maher Ata Mona Ahmed Nermeen Elmenabawy Rania Mohamed Abd-ElGhaffar Shimaa Fawzy