Browsing by Author "Akinci, M. Bilal"

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A Comprehensive Exploration of Deep Learning Approaches for Pulmonary Nodule Classification and Segmentation in Chest Ct Images
(Springer London Ltd, 2024) Canayaz, Murat; Sehribanoglu, Sanem; Ozgokce, Mesut; Akinci, M. Bilal
Accurately determining whether nodules on CT images of the lung are benign or malignant plays an important role in the early diagnosis and treatment of tumors. In this study, the classification and segmentation of benign and malignant nodules on CT images of the lung were performed using deep learning models. A new approach, C+EffxNet, is used for classification. With this approach, the features are extracted from CT images and then classified with different classifiers. In other phases of the study, a segmentation between benign and malignant was performed and, for the first time, a comparison of nodes was made during segmentation. The deep learning models InceptionV3, DenseNet121, and SeResNet101 were used as backbone models for feature extraction in the segmentation phase. In the classification phase, an accuracy of 0.9798, a precision of 0.9802, a recognition of 0.9798, an F1 score of 0.9798, and a kappa value of 0.9690 were achieved. During segmentation, the highest values of 0.8026 Jacard index and 0.8877 Dice coefficient were achieved.
The Efficiency of Acoustic Radiation Force Impulse (Arfi) Elastography in the Differentiation of Renal Cell Carcinoma and Oncocytoma
(Bentham Science Publ Ltd, 2024) Ozgokce, Mesut; Dundar, Ilyas; Durmaz, Fatma; Ozkacmaz, Sercan; Kankilic, Nazim A.; Aslan, Rahmi; Akinci, M. Bilal
Purpose This study is to investigate the effectiveness of Acoustic Radiation Force Impulse (ARFI) elastography in differentiating radiologically similar renal cell carcinoma (RCC) and oncocytoma in solid masses of the kidney. Methods The patients with solid renal mass histopathological diagnosed after excision or tru-cat biopsy who underwent a preoperative ARFI elastography of the lesion during a 4-year period were included in this study. Preoperative shear wave velocity (SWV) values were measured in all the lesions. SWV results of RCCs and oncocytomas were compared by an independent t-test, and cut-off, sensitivity and specificity values were calculated. Results Forty-two of the 60 patients included in the study were men (70%) and, 18 were women (30%), and the mean age was 59.7 +/- 14 (27-94) years. Among 46 RCCs (76.6%), 23 and 14 oncocytomas, 5 (23.4%) were located in the right kidney (p:0.34722). Mean SWV values were found to be significantly higher in RCCs (2.87 +/- 0.74 (0.96-4.14) m/s) than oncocytomas (1.83 +/- 0.78 (0.80-3.76) m/s) (p <0.001). In the ROC analysis, a cut-off value of 2.29 m/s was found to havean 80.4% sensitivity and a 78.6% specificity for the discrimination of RCCs from oncocytomas. Conclusion ARFI elastography measurements may be useful in distinguishing RCC and oncocytomas that may have similar solid radiological imaging features.