Deep learning and diagnostic applications in oral and dental health have received significant att... more Deep learning and diagnostic applications in oral and dental health have received significant attention recently. In this review, studies applying deep learning to diagnose anomalies and diseases in dental image material were systematically compiled, and their datasets, methodologies, test processes, explainable artificial intelligence methods, and findings were analyzed. Tests and results in studies involving human-artificial intelligence comparisons are discussed in detail to draw attention to the clinical importance of deep learning. In addition, the review critically evaluates the literature to guide and further develop future studies in this field. An extensive literature search was conducted for the 2019–May 2023 range using the Medline (PubMed) and Google Scholar databases to identify eligible articles, and 101 studies were shortlisted, including applications for diagnosing dental anomalies (n = 22) and diseases (n = 79) using deep learning for classification, object detectio...
Journal of Computer & Electrical and Electronics Engineering Sciences
Aims: The interruption of blood flow to the heart muscle is called a heart attack. During a heart... more Aims: The interruption of blood flow to the heart muscle is called a heart attack. During a heart attack, the risk of permanent damage increases with every second the heart tissue cannot receive enough blood. If early and appropriate intervention is not performed, loss of heart tissue occurs. Causes such as smoking, cholesterol, diabetes, high blood pressure, old age, obesity, genetics, and high levels of certain substances produced in the liver are the main risk factors for heart attack. This study aims to predict the risk of heart attack with machine learning methods using a dataset created by considering risk factors. Methods: The performances of three types of Linear Discriminant Analysis classifiers, Normal, Ledoit-Wolf, and Oracle Shrinkage Approximating, were compared on the Cleveland dataset. Results: Normal Linear Discriminant Analysis made the best classification with 83.60% accuracy and performed better than regularized versions. Conclusion: Linear Discriminant Analysis m...
Endoscopic procedures for diagnosing gastrointestinal tract findings depend on specialist experie... more Endoscopic procedures for diagnosing gastrointestinal tract findings depend on specialist experience and inter-observer variability. This variability can cause minor lesions to be missed and prevent early diagnosis. In this study, deep learning-based hybrid stacking ensemble modeling has been proposed for detecting and classifying gastrointestinal system findings, aiming at early diagnosis with high accuracy and sensitive measurements and saving workload to help the specialist and objectivity in endoscopic diagnosis. In the first level of the proposed bi-level stacking ensemble approach, predictions are obtained by applying 5-fold cross-validation to three new CNN models. A machine learning classifier selected at the second level is trained according to the obtained predictions, and the final classification result is reached. The performances of the stacking models were compared with the performances of the deep learning models, and McNemar’s statistical test was applied to support ...
It is necessary to know the manufacturer and model of a previously implanted shoulder prosthesis ... more It is necessary to know the manufacturer and model of a previously implanted shoulder prosthesis before performing Total Shoulder Arthroplasty operations, which may need to be performed repeatedly in accordance with the need for repair or replacement. In cases where the patient’s previous records cannot be found, where the records are not clear, or the surgery was conducted abroad, the specialist should identify the implant manufacturer and model during preoperative X-ray controls. In this study, an auxiliary expert system is proposed for classifying manufacturers of shoulder implants on the basis of X-ray images that is automated, objective, and based on hybrid machine learning models. In the proposed system, ten different hybrid models consisting of a combination of deep learning and machine learning algorithms were created and statistically tested. According to the experimental results, an accuracy of 95.07% was achieved using the DenseNet201 + Logistic Regression model, one of t...
Anestezide ilac dozunun dogru hesaplanmasi cok onemli bir rol oynamaktadir. Preoperatif anestezid... more Anestezide ilac dozunun dogru hesaplanmasi cok onemli bir rol oynamaktadir. Preoperatif anestezide, bir anestezi uzmani, hipnotik ilaclarin dozlarini hastanin faktorlerine gore hesaplamakta ve bunlari klinik ortamda bir baslangic ve devam dozu seklinde uygulamaktadir. Bu calismada, hipnotik bir ajan olan propofolun baslangic dozu (mg), premedikasyon veya ek ilac kullanilmadigi varsayilarak cok katmanli ileri beslemeli yapay sinir agi (CKYSA) yapisi kullanilarak tahmin edilmistir. Yas (yil), agirlik (kg), boy (m) ve eslik eden hastalik faktorleri onerilen ongorucu agin girdilerini olusturmustur. Bu calisma icin veri seti 299 hasta ornegi ile uzman anestezistler tarafindan olusturulmustur. En iyi tahmin ediciyi bulmak icin farkli hiperparametrelerle tasarlanan bircok YSA modeli test edilmis ve sonuclari kaydedilmistir. Elde edilen sonuclara gore, en iyi tahminci % 92'nin uzerinde basari oranlariyla propofolun baslangic dozunu tahmin etmistir. Bu model sayesinde, potansiyel anestez...
Deep learning and diagnostic applications in oral and dental health have received significant att... more Deep learning and diagnostic applications in oral and dental health have received significant attention recently. In this review, studies applying deep learning to diagnose anomalies and diseases in dental image material were systematically compiled, and their datasets, methodologies, test processes, explainable artificial intelligence methods, and findings were analyzed. Tests and results in studies involving human-artificial intelligence comparisons are discussed in detail to draw attention to the clinical importance of deep learning. In addition, the review critically evaluates the literature to guide and further develop future studies in this field. An extensive literature search was conducted for the 2019–May 2023 range using the Medline (PubMed) and Google Scholar databases to identify eligible articles, and 101 studies were shortlisted, including applications for diagnosing dental anomalies (n = 22) and diseases (n = 79) using deep learning for classification, object detectio...
Journal of Computer & Electrical and Electronics Engineering Sciences
Aims: The interruption of blood flow to the heart muscle is called a heart attack. During a heart... more Aims: The interruption of blood flow to the heart muscle is called a heart attack. During a heart attack, the risk of permanent damage increases with every second the heart tissue cannot receive enough blood. If early and appropriate intervention is not performed, loss of heart tissue occurs. Causes such as smoking, cholesterol, diabetes, high blood pressure, old age, obesity, genetics, and high levels of certain substances produced in the liver are the main risk factors for heart attack. This study aims to predict the risk of heart attack with machine learning methods using a dataset created by considering risk factors. Methods: The performances of three types of Linear Discriminant Analysis classifiers, Normal, Ledoit-Wolf, and Oracle Shrinkage Approximating, were compared on the Cleveland dataset. Results: Normal Linear Discriminant Analysis made the best classification with 83.60% accuracy and performed better than regularized versions. Conclusion: Linear Discriminant Analysis m...
Endoscopic procedures for diagnosing gastrointestinal tract findings depend on specialist experie... more Endoscopic procedures for diagnosing gastrointestinal tract findings depend on specialist experience and inter-observer variability. This variability can cause minor lesions to be missed and prevent early diagnosis. In this study, deep learning-based hybrid stacking ensemble modeling has been proposed for detecting and classifying gastrointestinal system findings, aiming at early diagnosis with high accuracy and sensitive measurements and saving workload to help the specialist and objectivity in endoscopic diagnosis. In the first level of the proposed bi-level stacking ensemble approach, predictions are obtained by applying 5-fold cross-validation to three new CNN models. A machine learning classifier selected at the second level is trained according to the obtained predictions, and the final classification result is reached. The performances of the stacking models were compared with the performances of the deep learning models, and McNemar’s statistical test was applied to support ...
It is necessary to know the manufacturer and model of a previously implanted shoulder prosthesis ... more It is necessary to know the manufacturer and model of a previously implanted shoulder prosthesis before performing Total Shoulder Arthroplasty operations, which may need to be performed repeatedly in accordance with the need for repair or replacement. In cases where the patient’s previous records cannot be found, where the records are not clear, or the surgery was conducted abroad, the specialist should identify the implant manufacturer and model during preoperative X-ray controls. In this study, an auxiliary expert system is proposed for classifying manufacturers of shoulder implants on the basis of X-ray images that is automated, objective, and based on hybrid machine learning models. In the proposed system, ten different hybrid models consisting of a combination of deep learning and machine learning algorithms were created and statistically tested. According to the experimental results, an accuracy of 95.07% was achieved using the DenseNet201 + Logistic Regression model, one of t...
Anestezide ilac dozunun dogru hesaplanmasi cok onemli bir rol oynamaktadir. Preoperatif anestezid... more Anestezide ilac dozunun dogru hesaplanmasi cok onemli bir rol oynamaktadir. Preoperatif anestezide, bir anestezi uzmani, hipnotik ilaclarin dozlarini hastanin faktorlerine gore hesaplamakta ve bunlari klinik ortamda bir baslangic ve devam dozu seklinde uygulamaktadir. Bu calismada, hipnotik bir ajan olan propofolun baslangic dozu (mg), premedikasyon veya ek ilac kullanilmadigi varsayilarak cok katmanli ileri beslemeli yapay sinir agi (CKYSA) yapisi kullanilarak tahmin edilmistir. Yas (yil), agirlik (kg), boy (m) ve eslik eden hastalik faktorleri onerilen ongorucu agin girdilerini olusturmustur. Bu calisma icin veri seti 299 hasta ornegi ile uzman anestezistler tarafindan olusturulmustur. En iyi tahmin ediciyi bulmak icin farkli hiperparametrelerle tasarlanan bircok YSA modeli test edilmis ve sonuclari kaydedilmistir. Elde edilen sonuclara gore, en iyi tahminci % 92'nin uzerinde basari oranlariyla propofolun baslangic dozunu tahmin etmistir. Bu model sayesinde, potansiyel anestez...
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