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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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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.
Classification of focal liver disease in egyptian patients using ultrasound images and convolutional neural networks Rania Mohamed Abd-ElGhaffar; Mahmoud El-Zalabany; Hossam El-Din Moustafa; Mervat El-Seddek
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 2: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i2.pp793-802

Abstract

Recently, computer-aided diagnostic systems for various diseases have received great attention. One of the latest technologies used is deep learning architectures for analyzing and classifying medical images. In this paper, a new system that uses deep learning to classify three focal diseases in the liver besides the normal liver is proposed. A pre-trained convolutional neural network is utilized. Two types of networks are used, ResNet50 and AlexNet with fully connected networks (FCNs). After extracting the deep features using deep learning, FCNs can input images in different states of the disease, such as Normal, Hem, HCC, and Cyst. Dataset is obtained from the Egyptian Liver Research Institute. Two classifiers are utilized, the first includes two classes (Normal/Cyst, Normal/Hem, Normal/HCC, HCC/Cyst, HCC/Hem, Cyst/Hem) and the second contains four classes (Normal/Cyst/ HCC/Hem) to distinguish liver images. Using performance criteria, it has been shown that the two-category classifiers have given better results than the four-class classifier, and accordingly a hybrid classifier was suggested to merge the weighted probabilities of the classes obtained by each singular classifier. Experimental results have achieved an accuracy of 96.1% using ResNet50 which means that it can be used as an assistive diagnostic method for classification of focal liver disease.
A deep learning based system for accurate diagnosis of brain tumors using T1-w MRI Mona Ahmed; Fahmi Khalifa; Hossam El-Din Moustafa; Gehad Ahmed Saleh; Eman AbdElhalim
Indonesian Journal of Electrical Engineering and Computer Science Vol 28, No 2: November 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v28.i2.pp1192-1202

Abstract

Detection and classification of brain tumors are of formidable importance in neuroscience. Deep learning (DL), specifically convolution neural networks (CNN), has demonstrated breakthroughs in the field of brain image analysis and brain tumors classification. This work proposes a novel CNN based model for brain tumor classification. Our pipeline starts with prepossessing and data augmentation techniques. Then, a CNN classification step is developed and utilizes ResNet50 architecture as its core. Particularly, our design modified the ResNet50 output with a global average pooling (GAP) layer to avoid over-fitting. The proposed model is trained and tested using different optimization algorithms. The final classification is achieved using a sigmoid layer. We tested the proposed structure on T1 weighted contrast-enhanced magnetic resonance images (T1-w MRI) that are collected from three datasets. A total of 3586 images containing two classes (i.e., bengin, and malignant) were used in our experiments. The proposed model reach highest accuracy 99.8%, and optimal error 0.005 using Adam when compared with other six well-known CNN architectures.
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