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All Journal Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Rahouma, Kamel Hussein
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Electrical & Electronics Engineering

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Automated 3D convolutional neural network architecture design using genetic algorithm for pulmonary nodule classification Rahouma, Kamel Hussein; Mabrouk, Shahenda Mahmoud; Aouf, Mohamed
Bulletin of Electrical Engineering and Informatics Vol 13, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i3.6828

Abstract

Cancer of the lungs is considered one of the primary causes of death among patients globally. Early detection contributes significantly to the success of pulmonary cancer treatment. To aid the pulmonary nodule classification, many models for the analysis of medical image utilizing deep learning have been developed. Convolutional neural network (CNN) recently, has attained remarkable results in various image classification tasks. Nevertheless, the CNNs performance is heavily dependent on their architectures which still heavily reliant on human domain knowledge. This study introduces a cutting-edge approach that leverages genetic algorithms (GAs) to automatically design 3D CNN architectures for differentiation between benign and malignant pulmonary nodules. The suggested algorithm utilizes the dataset of lung nodule analysis 2016 (LUNA16) for evaluation. Notably, our approach achieved exceptional model accuracy, with evaluations on the testing dataset yielding up to 95.977%. Furthermore, the algorithm exhibited high sensitivity, showcasing its robust performance in distinguishing between benign and malignant nodules. Our findings demonstrate the outstanding capabilities of the proposed algorithm and show an outstanding performance and attain a state of art solution in lung nodule classification.
A deep learning-based system for accurate diagnosis of pelvic bone tumors Shouman, Mona; Rahouma, Kamel Hussein; Hamed, Hesham Fathy Aly
Bulletin of Electrical Engineering and Informatics Vol 13, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i3.6861

Abstract

Bone image analysis and categorizing bone cancers have both seen advancements thanks to deep learning (DL), more notably convolution neural networks (CNN). This study suggests a brand-new CNN-based methodology for categorizing pelvic bone tumors specifically. This work aims to create a pelvic bone computed tomography (CT) image categorization system based on deep learning. The proposed technique uses a convolutional neural network (CNN) architecture to automatically extract information from the CT images and classify them into distinct categories of tumors. A total of 178 3D CT pictures was discovered and added retroactively. DenseNet created the image-based model with Adam optimizer and cross entropy loss. The suggested system's accuracy is assessed using a variety of performance indicators, including sensitivity, specificity, and F1-score. As demonstrated by the experiment findings, the suggested deep learning based classification system has a high degree of accuracy (94%), making it useful for the diagnosis and treatment of pelvic bone tumors. Our promising results might hasten the use of DL-assisted CT diagnosis for pelvic bone tumors in the future.
Deep transfer learning classification of apple fruit diseases Loutfy, Shaimaa Kamal; Rahouma, Kamel Hussein
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i3.pp1556-1564

Abstract

This paper applies deep convolution neural networks (DCNN) to apple fruit disease classification. Twelve DCNN methods (SqueezeNet, GoogleNet, InceptionV3, DenseNet201, ReaNet50, ResNet101, Xception, InceptionResnetV2, EfficientnetB0, AlexNet, VGG16, and VGG19) have been used. These methods have been trained to classify apples into four categories: normal, blotch, rot, and scab. A dataset of 5179 images, including 3472 for normal, 171 for blotch, 1166 for rot, and 370 for scab, has been used. A practical test on 120 images (30 for each category) has been applied. Seven of these DCNNs—InceptionV3, DenseNet201, ResNet101, ResNet50, GoogleNet, AlexNet, and VGG16—have the best accuracy. InceptionV3 is the highest. It has achieved an accuracy of 100% for all categories. The used dataset is unbalanced and small. So, it's necessary to use data augmentation to overcome any overfitting that may cause. After applying data augmentation, the dataset is balanced and contains 13888 images (3472 for each category). The seven DCNNs are retrained by the balanced dataset and retested by the same 120 images. All DCNN's accuracy has enhanced except InceptionV3, which has decreased. On the other hand, RasNet101 has achieved an accuracy of 100% for all categories. Therefore, ResNet101 has been recommended for apple fruit disease classification.
Co-Authors Aouf, Mohamed Hamed, Hesham Fathy Aly Loutfy, Shaimaa Kamal Mabrouk, Shahenda Mahmoud Shouman, Mona