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Article
Green & Sustainable Science & Technology
Omneya Attallah et al.
Summary: This paper proposes an efficient and robust ensemble deep learning-based diagnosis method for inter-turn short-circuit faults detection in induction rotating machines. The proposed technique achieves 100% classification accuracy and reduces the input features' size to the classifier.
Article
Chemistry, Analytical
Murat Altun et al.
Summary: In this study, a deep learning method was used to detect monkeypox disease in a fast and safe way. By combining transfer learning and hyperparameter optimization, a hybrid function transfer learning model was developed. The optimized hybrid MobileNetV3-s model achieved the best performance.
Review
Virology
Nicola L. Bragazzi et al.
Summary: An emerging global outbreak of monkeypox infection is spreading rapidly, with clinical characteristics and transmission route different from previous outbreaks. Preliminary risk factors have been identified, but further research is needed.
JOURNAL OF MEDICAL VIROLOGY
(2023)
Article
Chemistry, Multidisciplinary
Omneya Attallah
Summary: Routine checks can help prevent cervical cancer. Artificial intelligence can enhance the traditional testing procedure and improve diagnostic accuracy.
APPLIED SCIENCES-BASEL
(2023)
Article
Medicine, General & Internal
Omneya Attallah
Summary: Retinopathy of prematurity (ROP) is a serious ocular problem in premature infants. This paper proposes an automated CAD tool called GabROP, which uses Gabor wavelets and multiple deep learning models for ROP diagnosis. GabROP analyzes fundus images using Gabor wavelets and trains three CNN models independently. The features from these models are fused using discrete cosine transform (DCT) to obtain the final diagnosis result. Experimental results demonstrate the accuracy and efficiency of GabROP.
Article
Medicine, General & Internal
Dilber Uzun Ozsahin et al.
Summary: Monkeypox is a zoonotic viral disease with skin lesions as a prominent symptom, which can be mistaken for chickenpox, leading to misdiagnosis and further spread of the disease. A deep learning algorithm using digital skin images was proposed for early detection and classification, and achieved high accuracy in detecting monkeypox.
Review
Public, Environmental & Occupational Health
Jannatul Ferdous et al.
Summary: Monkeypox is a viral zoonotic disease caused by the monkeypox virus, and it can be transmitted between animals and humans. This study reviews the outbreak, diagnosis, treatment, and prevention of monkeypox based on available literature. Improved surveillance and detailed investigation of cases and contacts are necessary to understand the changing epidemiology of monkeypox.
HEALTH SCIENCE REPORTS
(2023)
Article
Computer Science, Artificial Intelligence
Md Manjurul Ahsan et al.
Summary: Monkeypox is a global challenge with increasing cases, and machine learning has the potential to be used for its diagnosis. In this study, a Monkeypox diagnosis model was developed using transfer learning approaches and tested on different convolutional neural network models. The results showed high accuracy in distinguishing individuals with and without Monkeypox, and Residual Network-101 performed best for multiclass classification. The proposed approach was computationally efficient and Local Interpretable Model-Agnostic Explanations provided deeper understanding of the disease onset indications.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Diponkor Bala et al.
Summary: The monkeypox virus poses a new pandemic threat. However, there is currently no reliable monkeypox database available for training and testing deep learning models. The MSID dataset has been developed for this purpose, providing a collection of monkeypox patient images for building confident deep learning models. The proposed MonkeyNet model can accurately identify monkeypox disease and assist doctors in making early diagnoses.
Article
Multidisciplinary Sciences
Rishav Pramanik et al.
Summary: We have observed the healing process of our society from the impact of COVID-19. The prevention of future pandemics requires appropriate protocols and methodologies to efficiently deal with outbreaks. This paper proposes an ensemble learning-based framework for the early detection of Monkeypox virus from skin lesion images. The framework achieves high accuracy, precision, recall, and F1 scores, making it effective in identifying the presence of Monkeypox.
Article
Medicine, General & Internal
Maram Fahaad Almufareh et al.
Summary: This paper introduces the diagnosis method and importance of monkeypox disease caused by monkeypox virus. Advanced technologies such as IoT and AI can be used for smarter and safer diagnosis. A non-invasive, non-contact, computer-vision-based method is proposed to diagnose monkeypox by analyzing skin lesion images. Deep learning techniques are employed to classify skin lesions as monkeypox virus positive or negative, and the proposed method is evaluated on two datasets. The results show high accuracy and feasibility, demonstrating the potential for wide-scale deployment in detecting monkeypox in underprivileged areas.
Review
Medicine, General & Internal
Krishnaraj Chadaga et al.
Summary: Monkeypox or Mpox is an infectious virus mainly found in Africa. Symptoms include headaches, chills, fever, lumps, and rashes. Artificial intelligence models have been developed for accurate and early diagnosis.
Article
Automation & Control Systems
Omneya Attallah
Summary: An automated tool called RADIC was developed to train deep learning models using radiomics-generated images, improving the accuracy of COVID-19 diagnosis with a final accuracy of 99.4% and 99% on two benchmark datasets.
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
(2023)
Article
Horticulture
Omneya Attallah
Summary: Tomatoes are valuable vegetables and an economic pillar for many countries. Automatic identification of tomato leaf diseases using deep learning models has been widely studied, but existing methods suffer from high computational complexity and large dimensionality. This study proposes a pipeline that utilizes compact convolutional neural networks and transfer learning for condensed and high-level representation. The pipeline also applies a hybrid feature selection approach to reduce dimensions and achieves high accuracy in tomato leaf disease identification.
Proceedings Paper
Computer Science, Artificial Intelligence
Vanessa Alcala-Rmz et al.
Summary: Machine learning has been used in medical applications, particularly in classification models for diagnosis support. In the field of dermatology, it plays a crucial role in differentiating between similar pathologies, such as skin cancer. Monkeypox, a recently emerged exanthematic disease, poses a challenge for accurate identification due to its similarity with other pathologies. Therefore, this study aims to utilize a public database of monkeypox and control group images to develop a diagnostic support system. With the implementation of MiniGoggleNet and various experiments, the best model achieved an accuracy of 0.9708, AUC values ranging from 0.74 to 0.76.
PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON UBIQUITOUS COMPUTING & AMBIENT INTELLIGENCE (UCAMI 2022)
(2023)
Review
Biology
Esra'a Alhenawi et al.
Summary: This systematic review provides comprehensive information on the main research directions for gene expression classification in the past seven years, with a focus on feature selection. The review identified 'propose hybrid FS methods' as the most interesting research direction, guiding researchers to select competitive research areas based on the findings. The papers were thoroughly reviewed based on six perspectives to provide insights for further research in this field.
COMPUTERS IN BIOLOGY AND MEDICINE
(2022)
Article
Biology
Omneya Attallah et al.
Summary: Pediatric medulloblastomas (MBs), the most common malignant brain tumors in children, are heterogenous and challenging to classify accurately based on histopathological images. This study combines textural analysis and deep learning techniques to improve the subtype identification of pediatric MBs. The automated pipeline proposed in this study shows an increased accuracy in classification compared to previous methods, providing a powerful tool for individualized therapies and identification of high-risk complications in children.
Article
Biology
Omneya Attallah
Summary: This paper proposes a new method, ECG-BiCoNet, for diagnosing COVID-19 using electrocardiogram (ECG) data. The results show that ECG data can be used to accurately diagnose COVID-19, which helps clinicians with automatic diagnosis and overcomes limitations of manual diagnosis.
COMPUTERS IN BIOLOGY AND MEDICINE
(2022)
Article
Infectious Diseases
Alimuddin Zumla et al.
LANCET INFECTIOUS DISEASES
(2022)
Article
Chemistry, Analytical
Omneya Attallah
Summary: Accurate and rapid diagnosis of COVID-19 is crucial for controlling its spread, reducing lockdown restrictions, and alleviating the burden on healthcare systems. This study introduces a novel automated diagnostic tool based on ECG data, utilizing deep learning models to achieve high accuracy in diagnosing COVID-19 at binary and multiclass levels.
Article
Chemistry, Multidisciplinary
Emmanuel Gbenga Dada et al.
Summary: The monkeypox outbreak has become a global public health emergency, and effective measures to treat and control the disease are still poorly understood. This research aims to predict the transmission rate of monkeypox using historical data and employs stacking ensemble learning and machine learning techniques. Experimental results demonstrate that the proposed stacking ensemble learning outperforms other machine learning approaches in terms of predictive performance.
APPLIED SCIENCES-BASEL
(2022)
Article
Medicine, General & Internal
Doaa Sami Khafaga et al.
Summary: Human skin diseases, including monkeypox, have become increasingly prevalent, leading to the need for computer-aided diagnosis. This study proposes a novel framework using deep CNN and optimization algorithms for automated diagnosis of monkeypox disease, outperforming prior research in skin disease classification.
Article
Computer Science, Artificial Intelligence
Omneya Attallah et al.
Summary: The quick and accurate diagnosis of COVID-19 is crucial for preventing its spread and improving treatment outcomes. This study proposes a deep learning-based pipeline called CoviWavNet, which utilizes 3D multiview CT slices and spectral-temporal information to enhance the diagnostic accuracy of COVID-19.
APPLIED SOFT COMPUTING
(2022)
Article
Energy & Fuels
Omneya Attallah et al.
Summary: This study proposes an online, non-intrusive fault diagnosis method based on infrared imaging for induction machines. It utilizes convolutional neural networks for deep learning and processes the features through discrete wavelet transform and principal component analysis. The method demonstrates superior performance.
Article
Mathematics
Abdelaziz A. Abdelhamid et al.
Summary: The world is still recovering from the devastation caused by COVID-19, and now monkeypox threatens to become a global pandemic. Though not as deadly or contagious as COVID-19, numerous countries are reporting new cases of monkeypox daily. The lack of necessary precautions being taken makes another global pandemic a possibility.
Proceedings Paper
Cell & Tissue Engineering
Muhammed Coskun Irmak et al.
Summary: Monkeypox virus is a viral disease that has caused concern in many countries. It is difficult to diagnose at an early stage due to its similarity in symptoms to chickenpox and measles. The use of deep learning methods can be effective in diagnosing the disease when PCR tests are not available.
2022 MEDICAL TECHNOLOGIES CONGRESS (TIPTEKNO'22)
(2022)
Article
Health Care Sciences & Services
Veysel Harun Sahin et al.
Summary: This article introduces an Android mobile application based on deep learning that can be used to quickly identify and isolate infected individuals during human monkeypox outbreaks. The application collects images through the device's camera and uses a deep convolutional neural network for classification to detect monkeypox. The system has been successfully tested on three devices and achieved a classification accuracy rate of 91.11%.
JOURNAL OF MEDICAL SYSTEMS
(2022)
Article
Health Care Sciences & Services
Chiranjibi Sitaula et al.
Summary: This study compares 13 different deep learning models for Monkeypox virus detection and proposes an ensemble method, achieving encouraging results applicable for mass screening by healthcare practitioners.
JOURNAL OF MEDICAL SYSTEMS
(2022)
Article
Health Care Sciences & Services
Omneya Attallah
Summary: With the current health crisis caused by the COVID-19 pandemic, patients' preference for limited contact with doctors or clinicians has led to the development of computer-aided facial diagnosis systems. This study introduces FaceDisNet, a novel system that utilizes deep learning techniques and a new public dataset to diagnose single and multiple diseases accurately without physical contact with patients. The high accuracy achieved by FaceDisNet demonstrates its reliability and potential for assisting physicians in manual diagnosis.
Article
Health Care Sciences & Services
Omneya Attallah
Summary: The accurate and rapid detection of novel coronavirus is crucial to prevent its spread, and artificial intelligence techniques can aid in this process. This study proposes a computer-assisted diagnostic framework based on deep learning and texture-based radiomics approaches. By fusing deep features from multiple convolutional neural networks, the diagnostic accuracy is improved. The performance of this framework allows radiologists to achieve fast and accurate diagnosis of coronavirus.
Article
Computer Science, Artificial Intelligence
Zhou Tao et al.
Summary: This study proposes an ensemble deep learning model for rapid detection of COVID-19 from CT images, achieving higher accuracy and sensitivity compared to individual classifiers. This approach can better meet the requirements for rapid detection of the novel coronavirus disease COVID-19.
APPLIED SOFT COMPUTING
(2021)
Article
Medicine, General & Internal
Omneya Attallah
Summary: Medulloblastoma (MB) is a dangerous malignant pediatric brain tumor. Precise diagnosis is essential. The computer-aided diagnosis system MB-AI-His combines deep learning and texture analysis for automatic diagnosis of pediatric MB and its subtypes from histopathological images efficiently and reliably.
Article
Computer Science, Artificial Intelligence
Omneya Attallah et al.
Summary: The article proposes a CADx system named Gastro-CADx for classifying multiple GI diseases using DL techniques. The system consists of three stages: extracting spatial features using CNN, extracting temporal-frequency and spatial-frequency features using DWT and DCT, and fusing multiple feature combinations for optimal output selection.
PEERJ COMPUTER SCIENCE
(2021)
Article
Medicine, General & Internal
Omneya Attallah
Summary: Retinopathy of Prematurity (ROP) affects preterm neonates and could lead to blindness. Deep Learning (DL) can assist ophthalmologists in diagnosing ROP, and the automated diagnostic tool DIAROP achieved an accuracy of 93.2%.
Article
Computer Science, Artificial Intelligence
Omneya Attallah et al.
Summary: Breast cancer is a common cancer affecting females globally, and late diagnosis can lead to severe consequences. The Histo-CADx system proposed in this study successfully improves the accuracy of breast cancer diagnosis through two fusion stages.
PEERJ COMPUTER SCIENCE
(2021)
Article
Multidisciplinary Sciences
Ankur Manna et al.
Summary: This research proposes an ensemble method for cervical cancer single cell and whole slide image classification by combining three different convolutional neural network architectures. The proposed model is evaluated on two publicly available datasets, showing high classification accuracy and sensitivity.
SCIENTIFIC REPORTS
(2021)
Article
Mathematical & Computational Biology
Omneya Attallah
Summary: Childhood medulloblastoma (MB) is a common malignant tumor, and early and accurate classification is crucial. CoMB-Deep combines deep learning and texture analysis feature extraction techniques to effectively classify MB and improve diagnostic accuracy.
FRONTIERS IN NEUROINFORMATICS
(2021)
Article
Radiology, Nuclear Medicine & Medical Imaging
Yicheng Fang et al.
Article
Medicine, General & Internal
Omneya Attallah
Article
Engineering, Biomedical
Jayeeta Chakraborty et al.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2020)
Review
Health Care Sciences & Services
Syed Muhammad Anwar et al.
JOURNAL OF MEDICAL SYSTEMS
(2018)
Article
Engineering, Biomedical
Omneya Attallah et al.
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART H-JOURNAL OF ENGINEERING IN MEDICINE
(2017)
Article
Immunology
Andrea M. McCollum et al.
CLINICAL INFECTIOUS DISEASES
(2014)
Article
Computer Science, Hardware & Architecture
Girish Chandrashekar et al.
COMPUTERS & ELECTRICAL ENGINEERING
(2014)
