Ahmet Tartar

Classification of Pulmonary Nodules by Using Hybrid Features

Early detection of pulmonary nodules is extremely important for the diagnosis and treatment of lung cancer. In this study, a new classification approach for pulmonary nodules from CT imagery is presented by using hybrid features. Four different methods are introduced for the proposed system. The overall detection performance is evaluated using various classifiers. The results are compared to similar techniques in the literature by using standard measures. The proposed approach with the hybrid features results in 90.7% classification accuracy (89.6% sensitivity and 87.5% specificity).

A new method for pulmonary nodule detection using decision trees

A computer-aided detection (CAD) can help radiologists in diagnosing of lung diseases at an early level. In this study, a new CAD system for pulmonary nodule detection from CT imagery is presented by using morphological features and patient information properties. Decision trees are utilized for classification and overall detection performance is evaluated. Results are compared to similar techniques in the literature by using standard measures. Proposed CAD system with random forest classifier result in 90.5 % sensitivity and 87.6 % specificity of detection performance.

A Novel Approach to Malignant-Benign Classification of Pulmonary Nodules by Using Ensemble Learning Classifiers

Computer-aided detection systems can help radiologists to detect pulmonary nodules at an early stage. In this paper, a novel Computer-Aided Diagnosis system (CAD) is proposed for the classification of pulmonary nodules as malignant and benign. The proposed CAD system using ensemble learning classifiers, provides an important support to radiologists at the diagnosis process of the disease, achieves high classification performance. The proposed approach with bagging classifier results in 94.7 %, 90.0 % and 77.8 % classification sensitivities for benign, malignant and undetermined classes (89.5 % accuracy), respectively.

Ensemble learning approaches to classification of pulmonary nodules

Lung cancer is one of the primary causes of cancer-related death worldwide. A computer-aided detection (CAD) can help radiologists by offering a second opinion and making the whole process faster at an early level. In this study, we propose a new classification approach for pulmonary nodule detection from CT imagery by using morphological features of nodule patterns. Ensemble learning approaches are used for classification process and overall detection performance is evaluated. Results are compared to similar techniques in the literature by using standard measures. The performance of the proposed system with random forest based on ensemble learning approaches results in an overall accuracy of 98.7 % with a sensitivity of 100 % and specificity of 97.3 % in training data set and an overall accuracy of 80.7 % with a sensitivity of 80.7 % and specificity of 80.6 % in testing dataset.

A new radiation dose measurement approach for X-ray units on biomedical technical service applications: A study of radiology

Recently, the improvement of radiology systems along with the developing technologies has provided an important contribution in the diagnosis and treatment of numerous medical cases. In this study, we propose a new radiation dose measurement approach for patients and radiation workers on X-ray units. The approach is extremely important to correct the shortcomings in this area as well as to provide a “gold standard” for radiation quality control measurement of X-ray units. The implementation of the approach will allow to the elimination of concerns of patients and radiation workers about radiation. Also it will present the opportunity to work in a safe environment for radiologists and radiation workers.

A new quality control method on X-ray radiography units for biomedical technical service applications

In the study, a new technical method/protocol is first proposed in the literature for the biomedical technical services. In this context, energy quality control test and focus adjustment test are made for X-ray radiography systems. The method will provide a unique contribution to the application field of the technical services of biomedical and clinical engineering on quality control assessments of radiology equipment.

Malignant-benign classification of pulmonary nodules by bagging-decision trees

Today, computer-aided detection systems have been highly needed in many clinical applications. In this study, a new Computer-aided Diagnosis system (CAD) was proposed for classifying pulmonary nodules as malignant and benign. The classifiers of the Bagging-decision trees were utilized. On the classifying of malign and benign nodule patterns, classification performance values are calculated as 94.7 % sensitivity and 0.950 AUROC for benign class; 80.0 % sensitivity and 0.888 AUROC for malign class; 77.8 % sensitivity and 0.935 AUROC for uncertain class by 86.8 % accuracy of the classifier.

Performance of ensemble learning classifiers on malignant-benign classification of pulmonary nodules

Computer-aided detection systems can help radiologists to detect pulmonary nodules at an early stage. In this study, a novel Computer-aided Diagnosis system (CAD) is proposed for the classification of pulmonary nodules as malignant and benign. Proposed CAD system, providing an important support to radiologists at the diagnosis process of the disease, achieves high classification performance using ensemble learning classifiers.

A new computer-aided detection system for pulmonary nodules

Computer-aided detection (CAD) systems can help radiologists in early stage diagnosing of lung abnormalities. In this study, a new CAD system is presented by using wavelet transform for pulmonary nodule detection. Classification is performed by using kernels of support vector machines. Results are compared to similar works in the literature. Proposed CAD system results in 82.1% sensitivity.