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  • Küçük Resim
    Öğe
    BI-RADS categories and breast lesions classification of mammographic images using artificial intelligence diagnostic model
    (Acad Sciences Czech Republic, Inst Computer Science, 2023) Türk, Fuat; Akkur, Erkan; Eroğul, Osman
    According to BI-RADS criteria, radiologists evaluate mammography images, and breast lesions are classified as malignant or benign. In this retrospective study, an evaluation was made on 264 mammogram images of 139 patients. First, data augmentation was applied, and then the total number of images was increased to 565. Two computer-aided models were then designed to classify breast lesions and BI-RADS categories. The first of these models is the support vector machine (SVM) based model, and the second is the convolutional neural network (CNN) based model. The SVM-based model could classify BI-RADS categories and malignant-benign discrimination with an accuracy rate of 86.42% and 92.59%, respectively. On the other hand, the CNN-based model showed 79.01% and 83.95% accuracy for BI-RADS categories and malignant benign discrimination, respectively. These results showed that a well-designed machine learning-based classification model can give better results than a deep learning model. Additionally, it can be used as a secondary system for radiologists to differentiate breast lesions and BI-RADS lesion categories.
  • [ X ]
    Öğe
    Optimized machine learning based predictive diagnosis approach for diabetes mellitus
    (2023) Akkur, Erkan; Türk, Fuat
    Aims: Diabetes mellitus is a metabolic disease caused by elevated blood sugar. If this disease is not diagnosed on time, it has the potential to pose a risk to other organs and tissues. Machine learning algorithms have started to preferred day by day in the detection of this disease, as in many other diseases. This study suggests a diabetes prediction approach incorporating optimized machine learning (ML) algorithms. Methods: The framework presented in this study starts with the application of different data pre-processing processes. Random forest (RF), support vector machine (SVM), K-nearest neighbor (K-NN) and decision tree (DT) algorithms are used for classification. Grid search is utilized for hyperparameter optimization of algorithms. Different performance evaluation measures are used to find the algorithm that best predicts diabetes. PIMA Indian dataset (PID) is chosen for testing the experiments. In addition, it is investigated to what extent the attributes in the data set affect the result using Shapley additive explanations (SHAP) analysis. Results: As a result of the experiments, the RF algorithm achieved the highest success rate with 89.06%, 84.33%, 84.33%, 84.33% and 0.88% accuracy, precision, sensitivity, F1-score and AUC scores. As a result of the SHAP analysis, it is found that the “Insulin”, “Age” and “Glucose” attributes contributed the most to the prediction model in identifying patients with diabetes. Conclusion: The hyperparameter optimized RF approach proposed in the framework of the study provided a good result in the prediction and diagnosis of diabetes mellitus when compared with similar studies in the literature. As a result, an expert system can be designed to detect diabetes early in real time using the proposed method.