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您的检索式：作者名="Ghada Atteia"
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| 1 | Adaptive Dynamic Dipper Throated Optimization for Feature Selection in Medical Data显示文摘The rapid population growth results in a crucial problem in the early detection of diseases inmedical research.Among all the cancers unveiled,breast cancer is considered the second most severe cancer.Consequently,an exponential rising in death cases incurred by breast cancer is expected due to the rapid population growth and the lack of resources required for performing medical diagnoses.Utilizing recent advances in machine learning could help medical staff in diagnosing diseases as they offer effective,reliable,and rapid responses,which could help in decreasing the death risk.In this paper,we propose a new algorithm for feature selection based on a hybrid between powerful and recently emerged optimizers,namely,guided whale and dipper throated optimizers.The proposed algorithm is evaluated using four publicly available breast cancer datasets.The evaluation results show the effectiveness of the proposed approach from the accuracy and speed perspectives.To prove the superiority of the proposed algorithm,a set of competing feature selection algorithms were incorporated into the conducted experiments.In addition,a group of statistical analysis experiments was conducted to emphasize the superiority and stability of the proposed algorithm.The best-achieved breast cancer prediction average accuracy based on the proposed algorithm is 99.453%.This result is achieved in an average time of 3.6725 s,the best result among all the competing approaches utilized in the experiments. | Ghada Atteia El-Sayed M.El-kenawy Nagwan Abdel Samee Mona M.Jamjoom Abdelhameed Ibrahim Abdelaziz A.Abdelhamid Ahmad Taher Azar Nima Khodadadi Reham A.Ghanem Mahmoud Y.Shams | 2023 | Computers, Materials & Continua2023，，4： | 0 |
| 2 | Metaheuristic Optimization Through Deep Learning Classification of COVID-19 in Chest X-Ray Images显示文摘As corona virus disease(COVID-19)is still an ongoing global outbreak,countries around the world continue to take precautions and measures to control the spread of the pandemic.Because of the excessive number of infected patients and the resulting deficiency of testing kits in hospitals,a rapid,reliable,and automatic detection of COVID-19 is in extreme need to curb the number of infections.By analyzing the COVID-19 chest X-ray images,a novel metaheuristic approach is proposed based on hybrid dipper throated and particle swarm optimizers.The lung region was segmented from the original chest X-ray images and augmented using various transformation operations.Furthermore,the augmented images were fed into the VGG19 deep network for feature extraction.On the other hand,a feature selection method is proposed to select the most significant features that can boost the classification results.Finally,the selected features were input into an optimized neural network for detection.The neural network is optimized using the proposed hybrid optimizer.The experimental results showed that the proposed method achieved 99.88%accuracy,outperforming the existing COVID-19 detection models.In addition,a deep statistical analysis is performed to study the performance and stability of the proposed optimizer.The results confirm the effectiveness and superiority of the proposed approach. | Nagwan Abdel Samee El-Sayed M.El-Kenawy Ghada Atteia Mona M.Jamjoom Abdelhameed Ibrahim Abdelaziz A.Abdelhamid Noha E.El-Attar Tarek Gaber Adam Slowik Mahmoud Y.Shams | 2022 | Computers, Materials & Continua2022，，11： | 0 |
| 3 | Latent Space Representational Learning of Deep Features for Acute Lymphoblastic Leukemia Diagnosis显示文摘Acute Lymphoblastic Leukemia(ALL)is a fatal malignancy that is featured by the abnormal increase of immature lymphocytes in blood or bone marrow.Early prognosis of ALL is indispensable for the effectual remediation of this disease.Initial screening of ALL is conducted through manual examination of stained blood smear microscopic images,a process which is time-consuming and prone to errors.Therefore,many deep learning-based computer-aided diagnosis(CAD)systems have been established to automatically diagnose ALL.This paper proposes a novel hybrid deep learning system for ALL diagnosis in blood smear images.The introduced system integrates the proficiency of autoencoder networks in feature representational learning in latent space with the superior feature extraction capability of standard pretrained convolutional neural networks(CNNs)to identify the existence of ALL in blood smears.An augmented set of deep image features are formed from the features extracted by GoogleNet and Inception-v3 CNNs from a hybrid dataset of microscopic blood smear images.A sparse autoencoder network is designed to create an abstract set of significant latent features from the enlarged image feature set.The latent features are used to perform image classification using Support Vector Machine(SVM)classifier.The obtained results show that the latent features improve the classification performance of the proposed ALL diagnosis system over the original image features.Moreover,the classification performance of the system with various sizes of the latent feature set is evaluated.The retrieved results reveal that the introduced ALL diagnosis system superiorly compete the state of the art. | Ghada Emam Atteia | 2023 | Computer Systems Science & Engineering2023，45，4： | 0 |
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