Computer-Based Characterization of Specific Cystic Renal Masses of Bosniac Classification with Traditional Machine Learning and Modified Deep Learning Methods

Mehmet Ezer; Ali Berkan Ural; Canver Önal

Article Details

Computer-Based Characterization of Specific Cystic Renal Masses of Bosniac Classification with Traditional Machine Learning and Modified Deep Learning Methods

Authors:

Mehmet Ezer | Ali Berkan Ural | Canver Önal

Publisher:

Kafkas University

Main Topics:

Klinik Tıp Bilimleri (Diğer)

Journal:

Kafkas Tıp Bilimleri Dergisi Vol.14 Issue 3, pp.311-320

Document Type:

Article

Publication Date:

12/2024

Document Language:

English

Document ID (Org No.):

2001337794

pISSN:

2146-2631

Keywords:

Abstract:

 Aim: This study aims to develop a novel artificial intelligencebased pre-diagnosis system for classifying cystic renal masses CRM according to the Bosniak classification. The objective is to distinguish between the five diagnostic stages of the Bosniak classification using traditional machine learning ML and deep learning DL techniques. Material and Method: A total of 20 contrast-enhanced CT images were collected for each of the five Bosniak stages I, II, IIF, III, IV, verified by a uro-oncologist and radiologist. Additional image variations were generated using the Keras image processing library during the data augmentation phase, resulting in 600 images per stage. This process included operations such as brightness and contrast modification, image rotation, noise addition, and flipping. These augmented images were then used to train both ML and DL models. The k-Nearest Neighbors kNN algorithm was applied for the ML approach, while modified Convolutional Neural Networks CNN and VGG-16 models were used for the DL approach. Model performances were evaluated using receiver operating characteristic ROC analysis and area under the curve AUC metrics. Results: The kNN algorithm accurately classified the Bosniak stages with an AUC of 0.854. The VGG-16 model demonstrated superior performance, with an AUC of 0.978, achieving higher classification accuracy than the kNN model. Conclusion: The computerized Bosniak classification system based on CT images effectively differentiates between the five Bosniak stages. This system, utilizing both ML and DL models, has the potential to enhance the pre-diagnosis of CRM in clinical settings and can also effectively exclude other types of renal masses.

 Aim: This study aims to develop a novel artificial intelligencebased pre-diagnosis system for classifying cystic renal masses CRM according to the Bosniak classification. The objective is to distinguish between the five diagnostic stages of the Bosniak classification using traditional machine learning ML and deep learning DL techniques. Material and Method: A total of 20 contrast-enhanced CT images were collected for each of the five Bosniak stages I, II, IIF, III, IV, verified by a uro-oncologist and radiologist. Additional image variations were generated using the Keras image processing library during the data augmentation phase, resulting in 600 images per stage. This process included operations such as brightness and contrast modification, image rotation, noise addition, and flipping. These augmented images were then used to train both ML and DL models. The k-Nearest Neighbors kNN algorithm was applied for the ML approach, while modified Convolutional Neural Networks CNN and VGG-16 models were used for the DL approach. Model performances were evaluated using receiver operating characteristic ROC analysis and area under the curve AUC metrics. Results: The kNN algorithm accurately classified the Bosniak stages with an AUC of 0.854. The VGG-16 model demonstrated superior performance, with an AUC of 0.978, achieving higher classification accuracy than the kNN model. Conclusion: The computerized Bosniak classification system based on CT images effectively differentiates between the five Bosniak stages. This system, utilizing both ML and DL models, has the potential to enhance the pre-diagnosis of CRM in clinical settings and can also effectively exclude other types of renal masses.

 Aim: This study aims to develop a novel artificial intelligencebased pre-diagnosis system for classifying cystic renal masses CRM according to the Bosniak classification. The objective is to distinguish between the five diagnostic stages of the Bosniak classification using traditional machine learning ML and deep learning DL techniques. Material and Method: A total of 20 contrast-enhanced CT images were collected for each of the five Bosniak stages I, II, IIF, III, IV, verified by a uro-oncologist and radiologist. Additional image variations were generated using the Keras image processing library during the data augmentation phase, resulting in 600 images per stage. This process included operations such as brightness and contrast modification, image rotation, noise addition, and flipping. These augmented images were then used to train both ML and DL models. The k-Nearest Neighbors kNN algorithm was applied for the ML approach, while modified Convolutional Neural Networks CNN and VGG-16 models were used for the DL approach. Model performances were evaluated using receiver operating characteristic ROC analysis and area under the curve AUC metrics. Results: The kNN algorithm accurately classified the Bosniak stages with an AUC of 0.854. The VGG-16 model demonstrated superior performance, with an AUC of 0.978, achieving higher classification accuracy than the kNN model. Conclusion: The computerized Bosniak classification system based on CT images effectively differentiates between the five Bosniak stages. This system, utilizing both ML and DL models, has the potential to enhance the pre-diagnosis of CRM in clinical settings and can also effectively exclude other types of renal masses.

 Aim: This study aims to develop a novel artificial intelligencebased pre-diagnosis system for classifying cystic renal masses CRM according to the Bosniak classification. The objective is to distinguish between the five diagnostic stages of the Bosniak classification using traditional machine learning ML and deep learning DL techniques. Material and Method: A total of 20 contrast-enhanced CT images were collected for each of the five Bosniak stages I, II, IIF, III, IV, verified by a uro-oncologist and radiologist. Additional image variations were generated using the Keras image processing library during the data augmentation phase, resulting in 600 images per stage. This process included operations such as brightness and contrast modification, image rotation, noise addition, and flipping. These augmented images were then used to train both ML and DL models. The k-Nearest Neighbors kNN algorithm was applied for the ML approach, while modified Convolutional Neural Networks CNN and VGG-16 models were used for the DL approach. Model performances were evaluated using receiver operating characteristic ROC analysis and area under the curve AUC metrics. Results: The kNN algorithm accurately classified the Bosniak stages with an AUC of 0.854. The VGG-16 model demonstrated superior performance, with an AUC of 0.978, achieving higher classification accuracy than the kNN model. Conclusion: The computerized Bosniak classification system based on CT images effectively differentiates between the five Bosniak stages. This system, utilizing both ML and DL models, has the potential to enhance the pre-diagnosis of CRM in clinical settings and can also effectively exclude other types of renal masses.

Article Details

Computer-Based Characterization of Specific Cystic Renal Masses of Bosniac Classification with Traditional Machine Learning and Modified Deep Learning Methods

Authors:

Mehmet Ezer | Ali Berkan Ural | Canver Önal

Publisher:

Kafkas University

Main Topics:

Klinik Tıp Bilimleri (Diğer)

Journal:

Kafkas Tıp Bilimleri Dergisi Vol.14 Issue 3, pp.311-320

Document Type:

Article

Publication Date:

12/2024

Document Language:

English

Document ID (Org No.):

2001337794

pISSN:

2146-2631

Renal Mass

Bosniak Classification

Machine Learning

Feature Extraction

Deep Learning

Issues

Article Details

Computer-Based Characterization of Specific Cystic Renal Masses of Bosniac Classification with Traditional Machine Learning and Modified Deep Learning Methods

Authors: Mehmet Ezer | Ali Berkan Ural | Canver Önal

Publisher: Kafkas University

Main Topics: Klinik Tıp Bilimleri (Diğer)

Journal: Kafkas Tıp Bilimleri Dergisi Vol.14 Issue 3, pp.311-320

Document Type: Article

Publication Date: 12/2024

Document Language: English

Document ID (Org No.): 2001337794

pISSN: 2146-2631

Renal Mass

Bosniak Classification

Machine Learning

Feature Extraction

Deep Learning

Issues

Authors:

Mehmet Ezer | Ali Berkan Ural | Canver Önal

Publisher:

Kafkas University

Main Topics:

Klinik Tıp Bilimleri (Diğer)

Journal:

Kafkas Tıp Bilimleri Dergisi Vol.14 Issue 3, pp.311-320

Document Type:

Article

Publication Date:

12/2024

Document Language:

English

Document ID (Org No.):

2001337794

pISSN:

2146-2631