Pub Date : 2024-03-10DOI: 10.21608/fcihib.2024.234205.1094
مصطفى كمال محمود, د. عزت سعد, نرمين عثمان
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Pub Date : 2024-01-01DOI: 10.21608/fcihib.2023.211465.1087
عبدالله رضا رشوان, ليلى الفنجري, صفاء عزام
—Heart failure (HF) is one of the most common diseases in recent years, and a large number of people die annually around the world from it. The heart is considered one of the most important organs in the human body, so it requires high accuracy when predicting the presence of heart disease or not, as an error in prediction may cause human death, so it requires a high-accuracy method in predicting HF. Artificial intelligence (AI) plays a large and important role in many fields today, especially in the medical field, as AI helps doctors obtain a quick and accurate diagnosis of the patient’s condition, which contributes to saving time during the diagnosis. It is important to predict HF using AI to help with rapid and accurate diagnosis and thus reduce the number of deaths from this disease. AI techniques increase the accuracy of predicting whether or not HF is present compared to traditional methods. Also, in rural areas where there are fewer physicians, it is very important to provide such technologies to aid in diagnosis. Many studies point to new AI-based HF prediction techniques. These technologies relied on different algorithms and datasets of different sizes and types. Each of these technologies has advantages and limitations. Therefore, this paper presents an illustrative study of the most advanced AI methods for HF prediction. This study also included a comparison between the different methods based on the most famous standards.
{"title":"A Comparative Study Of Artificial Intelligence Techniques For Categorization And Prediction Of Heart Diseases","authors":"عبدالله رضا رشوان, ليلى الفنجري, صفاء عزام","doi":"10.21608/fcihib.2023.211465.1087","DOIUrl":"https://doi.org/10.21608/fcihib.2023.211465.1087","url":null,"abstract":"—Heart failure (HF) is one of the most common diseases in recent years, and a large number of people die annually around the world from it. The heart is considered one of the most important organs in the human body, so it requires high accuracy when predicting the presence of heart disease or not, as an error in prediction may cause human death, so it requires a high-accuracy method in predicting HF. Artificial intelligence (AI) plays a large and important role in many fields today, especially in the medical field, as AI helps doctors obtain a quick and accurate diagnosis of the patient’s condition, which contributes to saving time during the diagnosis. It is important to predict HF using AI to help with rapid and accurate diagnosis and thus reduce the number of deaths from this disease. AI techniques increase the accuracy of predicting whether or not HF is present compared to traditional methods. Also, in rural areas where there are fewer physicians, it is very important to provide such technologies to aid in diagnosis. Many studies point to new AI-based HF prediction techniques. These technologies relied on different algorithms and datasets of different sizes and types. Each of these technologies has advantages and limitations. Therefore, this paper presents an illustrative study of the most advanced AI methods for HF prediction. This study also included a comparison between the different methods based on the most famous standards.","PeriodicalId":515131,"journal":{"name":"النشرة المعلوماتية في الحاسبات والمعلومات","volume":"269 19‐23","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139636266","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-01DOI: 10.21608/fcihib.2024.189094.1079
مى مختار, هالة عبد الجليل, غادة خوريبه
—Medical image segmentation is essential for detecting and localizing tumors in medical image analysis. Image segmentation involves the identification of anatomical structures in images. Medical image segmentation starts with manual segmentation using Atlas methods, then auto-segmentation, facilitated by deep learning algorithms. Deep learning-based medical image segmentation retains a significant pledge in reducing treatment planning, radiation-related toxicities, and side effects. This study provides a complete overview of deep-learning medical image segmentation models. We review various deep-learning models and architectures applied to medical image segmentation, including fully convolutional networks, U-Net, and attention-based models. This literature review discusses using different loss functions, data augmentation techniques, and transfer learning in deep learning-based medical image segmentation and several types of medical image modality. Evaluation analysis encloses benchmark datasets for human body organs such as the brain, lungs, chest, and liver. Finally, we summarize the challenges and future directions of deep learning for medical image segmentation.
{"title":"Deep Learning Medical Image Segmentation Methods: A Survey","authors":"مى مختار, هالة عبد الجليل, غادة خوريبه","doi":"10.21608/fcihib.2024.189094.1079","DOIUrl":"https://doi.org/10.21608/fcihib.2024.189094.1079","url":null,"abstract":"—Medical image segmentation is essential for detecting and localizing tumors in medical image analysis. Image segmentation involves the identification of anatomical structures in images. Medical image segmentation starts with manual segmentation using Atlas methods, then auto-segmentation, facilitated by deep learning algorithms. Deep learning-based medical image segmentation retains a significant pledge in reducing treatment planning, radiation-related toxicities, and side effects. This study provides a complete overview of deep-learning medical image segmentation models. We review various deep-learning models and architectures applied to medical image segmentation, including fully convolutional networks, U-Net, and attention-based models. This literature review discusses using different loss functions, data augmentation techniques, and transfer learning in deep learning-based medical image segmentation and several types of medical image modality. Evaluation analysis encloses benchmark datasets for human body organs such as the brain, lungs, chest, and liver. Finally, we summarize the challenges and future directions of deep learning for medical image segmentation.","PeriodicalId":515131,"journal":{"name":"النشرة المعلوماتية في الحاسبات والمعلومات","volume":"80 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140522343","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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