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  1. Article ; Online: Hybrid Deep Learning Model–Based Prediction of Images Related to Cyberbullying

    Elmezain Mahmoud / Malki Amer / Gad Ibrahim / Atlam El-Sayed

    International Journal of Applied Mathematics and Computer Science, Vol 32, Iss 2, Pp 323-

    2022  Volume 334

    Abstract: Cyberbullying has become more widespread as a result of the common use of social media, particularly among teenagers and young people. A lack of studies on the types of advice and support available to victims of bullying has a negative impact on ... ...

    Abstract Cyberbullying has become more widespread as a result of the common use of social media, particularly among teenagers and young people. A lack of studies on the types of advice and support available to victims of bullying has a negative impact on individuals and society. This work proposes a hybrid model based on transformer models in conjunction with a support vector machine (SVM) to classify our own data set images. First, seven different convolutional neural network architectures are employed to decide which is best in terms of results. Second, feature extraction is performed using four top models, namely, ResNet50, EfficientNetB0, MobileNet and Xception architectures. In addition, each architecture extracts the same number of features as the number of images in the data set, and these features are concatenated. Finally, the features are optimized and then provided as input to the SVM classifier. The accuracy rate of the proposed merged models with the SVM classifier achieved 96.05%. Furthermore, the classification precision of the proposed merged model is 99% in the bullying class and 93% in the non-bullying class. According to these results, bullying has a negative impact on students’ academic performance. The results help stakeholders to take necessary measures against bullies and increase the community’s awareness of this phenomenon.
    Keywords cyberbullying ; resnet50 ; mobilenetv2 ; support vector machine ; Mathematics ; QA1-939 ; Electronic computers. Computer science ; QA75.5-76.95
    Subject code 006
    Language English
    Publishing date 2022-06-01T00:00:00Z
    Publisher Sciendo
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article ; Online: Reliable Sarcoidosis Detection Using Chest X-rays with EfficientNets and Stain-Normalization Techniques.

    Baghdadi, Nadiah / Maklad, Ahmed S / Malki, Amer / Deif, Mohanad A

    Sensors (Basel, Switzerland)

    2022  Volume 22, Issue 10

    Abstract: Sarcoidosis is frequently misdiagnosed as tuberculosis (TB) and consequently mistreated due to inherent limitations in radiological presentations. Clinically, to distinguish sarcoidosis from TB, physicians usually employ biopsy tissue diagnosis and blood ...

    Abstract Sarcoidosis is frequently misdiagnosed as tuberculosis (TB) and consequently mistreated due to inherent limitations in radiological presentations. Clinically, to distinguish sarcoidosis from TB, physicians usually employ biopsy tissue diagnosis and blood tests; this approach is painful for patients, time-consuming, expensive, and relies on techniques prone to human error. This study proposes a computer-aided diagnosis method to address these issues. This method examines seven EfficientNet designs that were fine-tuned and compared for their abilities to categorize X-ray images into three categories: normal, TB-infected, and sarcoidosis-infected. Furthermore, the effects of stain normalization on performance were investigated using Reinhard's and Macenko's conventional stain normalization procedures. This procedure aids in improving diagnostic efficiency and accuracy while cutting diagnostic costs. A database of 231 sarcoidosis-infected, 563 TB-infected, and 1010 normal chest X-ray images was created using public databases and information from several national hospitals. The EfficientNet-B4 model attained accuracy, sensitivity, and precision rates of 98.56%, 98.36%, and 98.67%, respectively, when the training X-ray images were normalized by the Reinhard stain approach, and 97.21%, 96.9%, and 97.11%, respectively, when normalized by Macenko's approach. Results demonstrate that Reinhard stain normalization can improve the performance of EfficientNet -B4 X-ray image classification. The proposed framework for identifying pulmonary sarcoidosis may prove valuable in clinical use.
    MeSH term(s) Humans ; Radiography ; Sarcoidosis/diagnostic imaging ; Staining and Labeling ; Tuberculosis ; Tuberculosis, Pulmonary/diagnostic imaging ; X-Rays
    Language English
    Publishing date 2022-05-19
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2052857-7
    ISSN 1424-8220 ; 1424-8220
    ISSN (online) 1424-8220
    ISSN 1424-8220
    DOI 10.3390/s22103846
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: A

    Baghdadi, Nadiah A / Malki, Amer / Balaha, Hossam Magdy / Badawy, Mahmoud / Elhosseini, Mostafa

    Sensors (Basel, Switzerland)

    2022  Volume 22, Issue 11

    Abstract: Alzheimer's disease (AD) is a chronic disease that affects the elderly. There are many different types of dementia, but Alzheimer's disease is one of the leading causes of death. AD is a chronic brain disorder that leads to problems with language, ... ...

    Abstract Alzheimer's disease (AD) is a chronic disease that affects the elderly. There are many different types of dementia, but Alzheimer's disease is one of the leading causes of death. AD is a chronic brain disorder that leads to problems with language, disorientation, mood swings, bodily functions, memory loss, cognitive decline, mood or personality changes, and ultimately death due to dementia. Unfortunately, no cure has yet been developed for it, and it has no known causes. Clinically, imaging tools can aid in the diagnosis, and deep learning has recently emerged as an important component of these tools. Deep learning requires little or no image preprocessing and can infer an optimal data representation from raw images without prior feature selection. As a result, they produce a more objective and less biased process. The performance of a convolutional neural network (CNN) is primarily affected by the hyperparameters chosen and the dataset used. A deep learning model for classifying Alzheimer's patients has been developed using transfer learning and optimized by Gorilla Troops for early diagnosis. This study proposes the A3C-TL-GTO framework for MRI image classification and AD detection. The A3C-TL-GTO is an empirical quantitative framework for accurate and automatic AD classification, developed and evaluated with the Alzheimer's Dataset (four classes of images) and the Alzheimer's Disease Neuroimaging Initiative (ADNI). The proposed framework reduces the bias and variability of preprocessing steps and hyperparameters optimization to the classifier model and dataset used. Our strategy, evaluated on MRIs, is easily adaptable to other imaging methods. According to our findings, the proposed framework was an excellent instrument for this task, with a significant potential advantage for patient care. The ADNI dataset, an online dataset on Alzheimer's disease, was used to obtain magnetic resonance imaging (MR) brain images. The experimental results demonstrate that the proposed framework achieves 96.65% accuracy for the Alzheimer's Dataset and 96.25% accuracy for the ADNI dataset. Moreover, a better performance in terms of accuracy is demonstrated over other state-of-the-art approaches.
    MeSH term(s) Aged ; Alzheimer Disease/diagnostic imaging ; Humans ; Machine Learning ; Neuroimaging
    Language English
    Publishing date 2022-06-02
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2052857-7
    ISSN 1424-8220 ; 1424-8220
    ISSN (online) 1424-8220
    ISSN 1424-8220
    DOI 10.3390/s22114250
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: An automated diagnosis and classification of COVID-19 from chest CT images using a transfer learning-based convolutional neural network.

    Baghdadi, Nadiah A / Malki, Amer / Abdelaliem, Sally F / Magdy Balaha, Hossam / Badawy, Mahmoud / Elhosseini, Mostafa

    Computers in biology and medicine

    2022  Volume 144, Page(s) 105383

    Abstract: Researchers have developed more intelligent, highly responsive, and efficient detection methods owing to the COVID-19 demands for more widespread diagnosis. The work done deals with developing an AI-based framework that can help radiologists and other ... ...

    Abstract Researchers have developed more intelligent, highly responsive, and efficient detection methods owing to the COVID-19 demands for more widespread diagnosis. The work done deals with developing an AI-based framework that can help radiologists and other healthcare professionals diagnose COVID-19 cases at a high level of accuracy. However, in the absence of publicly available CT datasets, the development of such AI tools can prove challenging. Therefore, an algorithm for performing automatic and accurate COVID-19 classification using Convolutional Neural Network (CNN), pre-trained model, and Sparrow search algorithm (SSA) on CT lung images was proposed. The pre-trained CNN models used are SeresNext50, SeresNext101, SeNet154, MobileNet, MobileNetV2, MobileNetV3Small, and MobileNetV3Large. In addition, the SSA will be used to optimize the different CNN and transfer learning(TL) hyperparameters to find the best configuration for the pre-trained model used and enhance its performance. Two datasets are used in the experiments. There are two classes in the first dataset, while three in the second. The authors combined two publicly available COVID-19 datasets as the first dataset, namely the COVID-19 Lung CT Scans and COVID-19 CT Scan Dataset. In total, 14,486 images were included in this study. The authors analyzed the Large COVID-19 CT scan slice dataset in the second dataset, which utilized 17,104 images. Compared to other pre-trained models on both classes datasets, MobileNetV3Large pre-trained is the best model. As far as the three-classes dataset is concerned, a model trained on SeNet154 is the best available. Results show that, when compared to other CNN models like LeNet-5 CNN, COVID faster R-CNN, Light CNN, Fuzzy + CNN, Dynamic CNN, CNN and Optimized CNN, the proposed Framework achieves the best accuracy of 99.74% (two classes) and 98% (three classes).
    MeSH term(s) COVID-19/diagnostic imaging ; Deep Learning ; Humans ; Neural Networks, Computer ; SARS-CoV-2 ; Tomography, X-Ray Computed/methods
    Language English
    Publishing date 2022-03-10
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 127557-4
    ISSN 1879-0534 ; 0010-4825
    ISSN (online) 1879-0534
    ISSN 0010-4825
    DOI 10.1016/j.compbiomed.2022.105383
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article ; Online: An optimized deep learning approach for suicide detection through Arabic tweets.

    Baghdadi, Nadiah A / Malki, Amer / Magdy Balaha, Hossam / AbdulAzeem, Yousry / Badawy, Mahmoud / Elhosseini, Mostafa

    PeerJ. Computer science

    2022  Volume 8, Page(s) e1070

    Abstract: Many people worldwide suffer from mental illnesses such as major depressive disorder (MDD), which affect their thoughts, behavior, and quality of life. Suicide is regarded as the second leading cause of death among teenagers when treatment is not ... ...

    Abstract Many people worldwide suffer from mental illnesses such as major depressive disorder (MDD), which affect their thoughts, behavior, and quality of life. Suicide is regarded as the second leading cause of death among teenagers when treatment is not received. Twitter is a platform for expressing their emotions and thoughts about many subjects. Many studies, including this one, suggest using social media data to track depression and other mental illnesses. Even though Arabic is widely spoken and has a complex syntax, depressive detection methods have not been applied to the language. The Arabic tweets dataset should be scraped and annotated first. Then, a complete framework for categorizing tweet inputs into two classes (such as Normal or Suicide) is suggested in this study. The article also proposes an Arabic tweet preprocessing algorithm that contrasts lemmatization, stemming, and various lexical analysis methods. Experiments are conducted using Twitter data scraped from the Internet. Five different annotators have annotated the data. Performance metrics are reported on the suggested dataset using the latest Bidirectional Encoder Representations from Transformers (BERT) and Universal Sentence Encoder (USE) models. The measured performance metrics are balanced accuracy, specificity, F1-score, IoU, ROC, Youden Index, NPV, and weighted sum metric (WSM). Regarding USE models, the best-weighted sum metric (WSM) is 80.2%, and with regards to Arabic BERT models, the best WSM is 95.26%.
    Language English
    Publishing date 2022-08-23
    Publishing country United States
    Document type Journal Article
    ISSN 2376-5992
    ISSN (online) 2376-5992
    DOI 10.7717/peerj-cs.1070
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Article ; Online: Classification of breast cancer using a manta-ray foraging optimized transfer learning framework.

    Baghdadi, Nadiah A / Malki, Amer / Magdy Balaha, Hossam / AbdulAzeem, Yousry / Badawy, Mahmoud / Elhosseini, Mostafa

    PeerJ. Computer science

    2022  Volume 8, Page(s) e1054

    Abstract: Due to its high prevalence and wide dissemination, breast cancer is a particularly dangerous disease. Breast cancer survival chances can be improved by early detection and diagnosis. For medical image analyzers, diagnosing is tough, time-consuming, ... ...

    Abstract Due to its high prevalence and wide dissemination, breast cancer is a particularly dangerous disease. Breast cancer survival chances can be improved by early detection and diagnosis. For medical image analyzers, diagnosing is tough, time-consuming, routine, and repetitive. Medical image analysis could be a useful method for detecting such a disease. Recently, artificial intelligence technology has been utilized to help radiologists identify breast cancer more rapidly and reliably. Convolutional neural networks, among other technologies, are promising medical image recognition and classification tools. This study proposes a framework for automatic and reliable breast cancer classification based on histological and ultrasound data. The system is built on CNN and employs transfer learning technology and metaheuristic optimization. The Manta Ray Foraging Optimization (MRFO) approach is deployed to improve the framework's adaptability. Using the Breast Cancer Dataset (two classes) and the Breast Ultrasound Dataset (three-classes), eight modern pre-trained CNN architectures are examined to apply the transfer learning technique. The framework uses MRFO to improve the performance of CNN architectures by optimizing their hyperparameters. Extensive experiments have recorded performance parameters, including accuracy, AUC, precision, F1-score, sensitivity, dice, recall, IoU, and cosine similarity. The proposed framework scored 97.73% on histopathological data and 99.01% on ultrasound data in terms of accuracy. The experimental results show that the proposed framework is superior to other state-of-the-art approaches in the literature review.
    Language English
    Publishing date 2022-08-08
    Publishing country United States
    Document type Journal Article
    ISSN 2376-5992
    ISSN (online) 2376-5992
    DOI 10.7717/peerj-cs.1054
    Database MEDical Literature Analysis and Retrieval System OnLINE

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