Segmentation and Classification in Radiological Images Using Deep Learning Approaches: A Comprehensive Comparison Study on Carotid Artery and Breast Lesions

Hüseyin Kutlu; Cemil Çolak

doi:10.18521/ktd.1909571

Segmentation and Classification in Radiological Images Using Deep Learning Approaches: A Comprehensive Comparison Study on Carotid Artery and Breast Lesions

Abstract

Aim: This study aimed to comprehensively compare the performance of different deep learning architectures for automatic segmentation of carotid artery ultrasound images and classification of breast ultrasound images, and to develop clinical decision support systems.

Material and Methods: Nine segmentation algorithms (U-Net, SegNet, DeepLab v3+, Mask R-CNN, Attention U-Net, U-Net++, TransUNet, Swin-UNet, Vision Mamba UNet) and ten classification algorithms were evaluated on a single held-out partition of the Common Carotid Artery (CCA; 1100 images from 11 subjects) and Breast Ultrasound Images (BUSI; 780 images) datasets; the segmentation models were trained with the configurations recommended in their original publications and the classification models with a shared SGD configuration. Performance was measured using the Dice similarity coefficient (DSC), intersection over union (IoU), precision, sensitivity and F1 score.

Results: Vision Mamba UNet achieved the highest segmentation performance for carotid artery (DSC=0.943, IoU=0.896). For the BUSI dataset, Vision Mamba UNet obtained the highest segmentation DSC (0.834) and Vision Transformer the highest classification F1 (0.887). The three highest-ranked segmentation models differed by at most 0.005 DSC on CCA and 0.029 on BUSI.

Conclusion: In this single-run evaluation, state space model-based and transformer-based segmentation architectures obtained higher scores than conventional CNN approaches; the ranking was not tested statistically. The web-based applications are prototypes that have not been evaluated in a clinical setting.

Keywords

References

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Details

Primary Language

English

Subjects

Health Informatics and Information Systems

Journal Section

Research Article

Authors

Hüseyin Kutlu ^*
0000-0003-0091-9984
Türkiye

Cemil Çolak
0000-0001-5406-098X
Türkiye

Publication Date

October 27, 2026

Submission Date

March 14, 2026

Acceptance Date

September 24, 2026

Published in Issue

Year 2026 Volume: 18 Number: 3

DOI

https://doi.org/10.18521/ktd.1909571

IZ

https://izlik.org/JA49KX42YC

Cite

RIS / Bibtex

APA

Kutlu, H., & Çolak, C. (2026). Segmentation and Classification in Radiological Images Using Deep Learning Approaches: A Comprehensive Comparison Study on Carotid Artery and Breast Lesions. Konuralp Medical Journal, 18(3), 250-258. https://doi.org/10.18521/ktd.1909571

AMA

1.Kutlu H, Çolak C. Segmentation and Classification in Radiological Images Using Deep Learning Approaches: A Comprehensive Comparison Study on Carotid Artery and Breast Lesions. Konuralp Medical Journal. 2026;18(3):250-258. doi:10.18521/ktd.1909571

Chicago

Kutlu, Hüseyin, and Cemil Çolak. 2026. “Segmentation and Classification in Radiological Images Using Deep Learning Approaches: A Comprehensive Comparison Study on Carotid Artery and Breast Lesions”. Konuralp Medical Journal 18 (3): 250-58. https://doi.org/10.18521/ktd.1909571.

EndNote

Kutlu H, Çolak C (October 1, 2026) Segmentation and Classification in Radiological Images Using Deep Learning Approaches: A Comprehensive Comparison Study on Carotid Artery and Breast Lesions. Konuralp Medical Journal 18 3 250–258.

IEEE

[1]H. Kutlu and C. Çolak, “Segmentation and Classification in Radiological Images Using Deep Learning Approaches: A Comprehensive Comparison Study on Carotid Artery and Breast Lesions”, Konuralp Medical Journal, vol. 18, no. 3, pp. 250–258, Oct. 2026, doi: 10.18521/ktd.1909571.

ISNAD

Kutlu, Hüseyin - Çolak, Cemil. “Segmentation and Classification in Radiological Images Using Deep Learning Approaches: A Comprehensive Comparison Study on Carotid Artery and Breast Lesions”. Konuralp Medical Journal 18/3 (October 1, 2026): 250-258. https://doi.org/10.18521/ktd.1909571.

JAMA

1.Kutlu H, Çolak C. Segmentation and Classification in Radiological Images Using Deep Learning Approaches: A Comprehensive Comparison Study on Carotid Artery and Breast Lesions. Konuralp Medical Journal. 2026;18:250–258.

MLA

Kutlu, Hüseyin, and Cemil Çolak. “Segmentation and Classification in Radiological Images Using Deep Learning Approaches: A Comprehensive Comparison Study on Carotid Artery and Breast Lesions”. Konuralp Medical Journal, vol. 18, no. 3, Oct. 2026, pp. 250-8, doi:10.18521/ktd.1909571.

Vancouver

1.Hüseyin Kutlu, Cemil Çolak. Segmentation and Classification in Radiological Images Using Deep Learning Approaches: A Comprehensive Comparison Study on Carotid Artery and Breast Lesions. Konuralp Medical Journal. 2026 Oct. 1;18(3):250-8. doi:10.18521/ktd.1909571