Browsing by Author "Saleh, Neven"

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Deep Learning-Based Automated Detection and Grading of Papilledema From OCT Images: A Promising Approach for Improved Clinical Diagnosis and Management
(John Wiley and Sons Inc, 2024-06) Salaheldin, Ahmed M; Abdel Wahed, Manal; Talaat, Manar; Saleh, Neven
Papilledema is a prevalent neuro-ophthalmic condition characterized by optic disk swelling. It is known to pose a significant risk of vision loss in its advanced stages. To address the pressing need for accurate detection and grading of papilledema, this study introduces a novel approach utilizing optical coherence tomography (OCT) scans. A cascaded model that combines four transfer learning models—SqueezeNet, AlexNet, GoogleNet, and ResNet-50—for both the detection and grading phases was proposed. Additionally, a specialized convolutional neural network (CNN) model is meticulously designed to cater specifically to the complexities of papilledema analysis. Unlike the fundus camera-based models, this study integrates deep learning models for the diagnosis of papilledema from OCT scans. A new dataset of OCT scans was collected to ensure a comprehensive evaluation of the models. It encompasses a wide range of papilledema, pseudopapilledema, and normal cases. This dataset serves as a valuable resource for training and testing of the proposed models. In addition, two validation strategies have been adopted to ensure the model's generalizability and robustness. Furthermore, it enhances the model's accuracy and reliability. The results are highly promising; remarkable accuracy rates have been achieved. Specifically, the SqueezeNet, AlexNet, GoogleNet, ResNet-50, and customized CNN models achieved accuracy levels of 98.44%, 98.50%, 98.28%, 98.30%, and 96.26%, respectively, for the handout validation strategy. These findings not only demonstrate the efficacy of using deep learning in papilledema detection and grading but also establish the superiority of the proposed models when compared with other relevant studies. By addressing the challenges associated with papilledema, the study significantly contributes to the advancement of neuro-ophthalmic diagnostics. The accurate and efficient detection of papilledema from OCT scans holds immense potential for guiding timely interventions and preserving patients' visual health.
An evaluation of AI-based methods for papilledema detection in retinal fundus images
(Elsevier BV, 2024-02) Salaheldin, Ahmed M; Abdel Wahed, Manal; Talaat, Manar; Saleh, Neven
The complexities inherent in diagnosing papilledema, particularly within the realm of neuro-ophthalmology, emphasize the pressing need for sophisticated diagnostic methodologies. This study highlights the application of novel models tailored explicitly for papilledema detection, distinguishing it from pseudo-papilledema and normal cases, through the strategic utilization of deep learning frameworks, specifically convolutional neural networks (CNNs) and recurrent neural networks (RNNs). Leveraging hierarchical feature extraction from retinal images, the multi-paths CNN model successfully identifies crucial indicators of papilledema, while the cascaded model, integrating ResNet-50 and long short-term memory (LSTM), effectively captures sequential features. Leveraging a meticulously curated dataset comprising 18,258 fundus images, the presented models exhibit exceptional performance, with the multi-paths CNN achieving an accuracy of 99.97 %, and the LSTM model demonstrating an accuracy of 99.81 %. Comparative analysis showcases the unparalleled efficacy of the models, underscoring their potential in clinical diagnostics. Notably, they demonstrate robustness in occlusion sensitivity tests, highlighting their resilience in scenarios involving obscured image components. This pioneering study represents a significant milestone in papilledema detection, with the promise of advancing patient outcomes and streamlining healthcare practices. The proposed deep learning models not only offer precise diagnoses but also hold the potential to automate elements of the diagnostic workflow, alleviating the workload of healthcare professionals and enhancing overall patient care outcomes.
A hybrid Model for The Detection of Retinal Disorders Using Artifi cial Intelligence Techniques
(Institute of Physics, 2023-08) Salaheldin, Ahmed M ; Abdel Wahed , Manal; Saleh, Neven
The prevalence of vision impairment is rising at an alarming rate. The goal of the study is to create an automated method that uses Optical Coherence Tomography (OCT) to classify retinal disorders into four categories, namely, Choroidal Neovascularization, Diabetic Macular Edema, Drusen, and normal cases. The study proposed a new framework that combines machine learning and deep learning-based techniques. The utilized classifi ers were Support Vector Machine (SVM), K-Nearest Neighbor (K-NN), Decision Tree (DT), and Ensemble Model (EM). A feature extractor was also employed, which was the InceptionV3 convolutional neural network. The performance of the models has been measured over nine criteria using a dataset of 18000 OCT images. For the SVM, K-NN, DT, and EM, the analysis exhibited state-of-the-art performance with classifi cation accuracies of 99.43%, 99.54%, 97.98%, and 99.31%, respectively. A promising methodology has been introduced for the automatic identifi cation and classifi cation of retinal disorders leading to reducing human error and saving time alike.
An integrative approach to medical laboratory equipment risk management
(Nature Publishing Group, 2024-02) Saleh, Neven; Gamal, Omnia; Eldosoky, Mohamed A.A; Shaaban, Abdel Rahman
Medical Laboratory Equipment (MLE) is one of the most infuential means for diagnosing a patient in healthcare facilities. The accuracy and dependability of clinical laboratory testing is essential for making disease diagnosis. A risk-reduction plan for managing MLE is presented in the study. The methodology was initially based on the Failure Mode and Efects Analysis (FMEA) method. Because of the drawbacks of standard FMEA implementation, a Technique for Ordering Preference by Similarity to the Ideal Solution (TOPSIS) was adopted in addition to the Simple Additive Weighting (SAW) method. Each piece of MLE under investigation was given a risk priority number (RPN), which in turn assigned its risk level. The equipment performance can be improved, and maintenance work can be prioritized using the generated RPN values. Moreover, fve machine learning classifers were employed to classify TOPSIS results for appropriate decision-making. The current study was conducted on 15 various hospitals in Egypt, utilizing a 150 MLE set of data from an actual laboratory, considering three diferent types of MLE. By applying the TOPSIS and SAW methods, new RPN values were obtained to rank the MLE risk. Because of its stability in ranking the MLE risk value compared to the conventional FMEA and SAW methods, the TOPSIS approach has been accepted. Thus, a prioritized list of MLEs was identifed to make decisions related to appropriate incoming maintenance and scrapping strategies according to the guidance of machine learning classifers.
Skin cancer classi¦cation based on an optimized convolutional neural network and multicriteria decision-making
(2024-03) Saleh, Neven; Hassan, Mohammed A; Salaheldin, Ahmed M
Skin cancer can be treated if it is detected early. Many artificial intelligence-based models have been developed for skin cancer detection and classification. Considering the development of multiple models according to various scenarios and selecting the optimum model, these models were rarely considered in previous works. This study aimed to develop multiple models for skin cancer classification and select the optimum model. Convolutional neural networks (CNNs) in the form of AlexNet, Inception V3, MobileNet V2, and ResNet 50 were used for feature extraction. Feature reduction was carried out using two algorithms of the gray wolf optimizer (GWO) in addition to using the original features. Skin cancer images were classified into four classes based on six machine learning (ML) classifiers. As a result, 51 models were developed with different combinations of CNN algorithms, without GWO algorithms, with two GWO algorithms, and with six ML classifiers. To select the optimum model with the best results, the multicriteria decisionmaking approach was utilized in the recent form of ranking the alternatives by perimeter similarity (RAPS). Model training and testing were conducted using the International Skin Imaging Collaboration (ISIC) 2017 dataset. Based on nine evaluation metrics and according to the RAPS method, the AlexNet algorithm with GWO yielded the optimum model, achieving a classification accuracy of 94.5%. This work presents the first study on benchmarking skin cancer classification with a large number of models. Feature reduction not only reduces the time spent on training but also improves classification accuracy. The RAPS method has proven its robustness in the problem of selecting the best model for skin cancer classification.