FARKLI KANSER TÜRLERİNDE DERİN ÖĞRENMEYE DAYALI GÖRÜNTÜ ANALİZİ: BİR LİTERATÜR TARAMASI

Yıl 2025, Cilt: 4 Sayı: 2, 105 - 120, 30.10.2025

Ejder Ertürkmen , Hanifi Çam , Ali Öter

https://doi.org/10.63742/ustbd.1744186

Öz

Bu çalışma, meme, akciğer, beyin, cilt, prostat ve kolon kanseri gibi yaygın kanser türlerinde kullanılan derin öğrenme (DÖ) tabanlı tıbbi görüntü analiz yöntemlerini sistematik bir bakış açısıyla incelemektedir. Literatür taraması kapsamında, son beş yılda yayınlanan güncel çalışmalar; kullanılan yöntemler (CNN, U-Net, ResNet vb.), veri setleri (DDSM, LIDC-IDRI, BraTS, ISIC vb.) ve elde edilen başarı metrikleri açısından karşılaştırmalı olarak analiz edilmiştir. Derin öğrenme mimarileri, görüntü sınıflandırma, segmentasyon, lezyon tespiti ve prognostik modelleme gibi görevlerde yüksek doğruluk oranları sunmakta; transfer öğrenme, attention mekanizmaları ve çok görevli öğrenme stratejileriyle performansları artırılmaktadır. Öte yandan, model açıklanabilirliği, veri güvenliği, etik denetim ve klinik entegrasyon gibi başlıklarda çeşitli sınırlılıklar da dikkat çekmektedir. Çalışma, derin öğrenme temelli yöntemlerin tıbbi görüntü analizinde mevcut durumu ve karşılaşılan zorlukları disiplinler arası bir çerçevede ortaya koymaktadır.

Anahtar Kelimeler

Derin öğrenme , Tıbbi görüntü analiz , Kanser teşhisi , Segmentasyon

Etik Beyan

ETİK BEYAN GEREKTİREN BİR ÇALIŞMA DEĞİLDİR

DEEP LEARNING-BASED IMAGE ANALYSIS IN DIFFERENT TYPES OF CANCER: A LITERATURE REVIEW

Öz

This research systematically reviews deep learning (DL)-based medical image analysis methods used in common types of cancer, such as breast, lung, brain, skin, prostate, and colon cancer. As part of the literature review, recent studies published over the past five years were compared and analyzed in terms of the methods used (CNN, U-Net, ResNet, etc.), the datasets employed (DDSM, LIDC-IDRI, BraTS, ISIC, etc.), and the performance metrics achieved. Deep learning architectures offer high accuracy rates in tasks such as image classification, segmentation, lesion detection, and prognostic modeling; their performance is enhanced through transfer learning, attention mechanisms, and multi-task learning strategies. On the other hand, various limitations in areas such as model explainability, data security, ethical oversight, and clinical integration are also noteworthy. The study highlights the current state of deep learning-based methods in medical image analysis and the challenges encountered within an interdisciplinary framework.

Anahtar Kelimeler

Deep learning , Medical image analysis , Cancer diagnosis , Segmentation

Kaynakça

• Acosta, M., Tovar, L., Garcia-Zapirain, M., & Percybrooks, W. (2021). Melanoma diagnosis using deep learning techniques on dermatoscopic images. BMC Medical Imaging, 21(1), 1–8. https://doi.org/10.1186/s12880-020-00534-8
• Adam, R., Dell'Aquila, K., Hodges, L., Maldjian, T., & Duong, T. (2023). Deep learning applications to breast cancer detection by magnetic resonance imaging: A literature review. Breast Cancer Research: BCR, 25. https://doi.org/10.1186/s13058-023-01687-4
• Adegun, A., & Viriri, S. (2020). Deep learning techniques for skin lesion analysis and melanoma cancer detection: A survey of state-of-the-art. Artificial Intelligence Review, 54, 811–841. https://doi.org/10.1007/s10462-020-09865-y
• Arora, A., Jayal, A., Gupta, M., Mittal, P., & Satapathy, S. (2021). Brain tumor segmentation of MRI images using processed image driven U-Net architecture. Computers, 10(11), 139. https://doi.org/10.3390/computers10110139
• Asuntha, A., & Srinivasan, A. (2020). Deep learning for lung cancer detection and classification. Multimedia Tools and Applications, 79, 7731–7762. https://doi.org/10.1007/s11042-019-08394-3
• Asuntha, A., & Srinivasan, A. (2020). Deep learning for lung cancer detection and classification. Multimedia Tools and Applications, 79, 7731–7762. https://doi.org/10.1007/s11042-019-08394-3
• Balkenende, L., Teuwen, J., & Mann, R. (2022). Application of deep learning in breast cancer imaging. Seminars in Nuclear Medicine, 52(5), 506–515. https://doi.org/10.1053/j.semnuclmed.2022.02.003
• Chaunzwa, T., Hosny, A., Xu, Y., Shafer, A., Diao, N., Lanuti, M., Christiani, D., Mak, R., & Aerts, H. (2021). Deep learning classification of lung cancer histology using CT images. Scientific Reports, 11. https://doi.org/10.1038/s41598-021-84630-x
• Chen, C., Chen, C., Yu, W., Chen, S., Chang, Y., Hsu, T., Hsiao, M., Yeh, C., & Chen, C. (2021). An annotation-free whole-slide training approach to pathological classification of lung cancer types using deep learning. Nature Communications, 12. https://doi.org/10.1038/s41467-021-21467-y
• Chugh, G., Kumar, S., & Singh, N. (2021). Survey on machine learning and deep learning applications in breast cancer diagnosis. Cognitive Computation, 13, 1451–1470. https://doi.org/10.1007/s12559-020-09813-6
• Coudray, N., Ocampo, P., Sakellaropoulos, T., Narula, N., Snuderl, M., Fenyö, D., Moreira, A., Razavian, N., & Tsirigos, A. (2018). Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning. Nature Medicine, 24, 1559–1567. https://doi.org/10.1038/s41591-018-0177-5
• Cuocolo, R., Cipullo, M., Stanzione, A., Ugga, L., Romeo, V., Radice, L., Brunetti, A., & Imbriaco, M. (2019). Machine learning applications in prostate cancer magnetic resonance imaging. European Radiology Experimental, 3(1), 1–8. https://doi.org/10.1186/s41747-019-0109-2
• Din, N., Dar, R. A., Rasool, M., & Assad, A. (2022). Breast cancer detection using deep learning: Datasets, methods, and challenges ahead. Computers in Biology and Medicine, 149, 106073. https://doi.org/10.1016/j.compbiomed.2022.106073
• Din, N., Dar, R., Rasool, M., & Assad, A. (2022). Breast cancer detection using deep learning: Datasets, methods, and challenges ahead. Computers in Biology and Medicine, 149, 106073. https://doi.org/10.1016/j.compbiomed.2022.106073
• Duman, A., Powell, J., Thomas, S., Sun, X., & Spezi, E. (2024). Generalizability of deep learning models on brain tumour segmentation. In Proceedings of the Cardiff University Engineering Research Conference 2023. https://doi.org/10.18573/conf1.b
• Ersöz, B., Öter, A., Sagiroglu, S., Akkaş, E., & Yapar, M.(2025). Deep learning based ResNet integrated U-Net approach for segmentation and classification of breast cancer images. Computers and Informatics,5(1). 12-22. https://doi.org/10.62189/ci.1604037
• Forte, G., Altmayer, S., Silva, R., Stefani, M., Libermann, L., Cavion, C., Youssef, A., Forghani, R., King, J., Mohamed, T., Andrade, R., & Hochhegger, B. (2022). Deep learning algorithms for diagnosis of lung cancer: A systematic review and meta-analysis. Cancers, 14. https://doi.org/10.3390/cancers14163856
• Gajera, H., Nayak, D., & Zaveri, M. (2023). A comprehensive analysis of dermoscopy images for melanoma detection via deep CNN features. Biomedical Signal Processing and Control, 79, 104186. https://doi.org/10.1016/j.bspc.2022.104186
• Gammoudi, I., Ghozi, R., & Mahjoub, M. (2022). HDFU-Net: An improved version of U-Net using a hybrid dice focal loss function for multi-modal brain tumor image segmentation. In 2022 International Conference on Cyberworlds (CW) (pp. 71–78). IEEE. https://doi.org/10.1109/CW55638.2022.00019
• Goyal, M., Oakley, A., Bansal, P., Dancey, D., & Yap, M. (2020). Skin lesion segmentation in dermoscopic images with ensemble deep learning methods. IEEE Access, 8, 4171–4181. https://doi.org/10.1109/ACCESS.2019.2960504
• Hassan, M., Islam, M., Uddin, M., Ghoshal, G., Hassan, M., Huda, S., & Fortino, G. (2022). Prostate cancer classification from ultrasound and MRI images using deep learning based explainable artificial intelligence. Future Generation Computer Systems, 127, 462–472. https://doi.org/10.1016/j.future.2021.09.030
• Horasan, A., & Güneş, A. (2024). Advancing prostate cancer diagnosis: A deep learning approach for enhanced detection in MRI images. Diagnostics, 14(17), 1871. https://doi.org/10.3390/diagnostics14171871
• Huang, J., Molleti, P., Lee, R., & Itakura, H. (2022). Deep learning-based brain tumor segmentation on limited sequences of magnetic resonance imaging. Journal of Clinical Oncology, 40(16_suppl), 2054. https://doi.org/10.1200/jco.2022.40.16_suppl.2054
• Ilhan, A., Şekeroğlu, B., & Abiyev, R. (2022). Brain tumor segmentation in MRI images using nonparametric localization and enhancement methods with U-net. International Journal of Computer Assisted Radiology and Surgery, 17, 589–600. https://doi.org/10.1007/s11548-022-02566-7
• Javed, R., Abbas, T., Khan, A., Daud, A., Bukhari, A., & Alharbey, R. (2024). Deep learning for lungs cancer detection: A review. Artificial Intelligence Review, 57, 197. https://doi.org/10.1007/s10462-024-10807-1
• Jiang, X., Hu, Z., Wang, S., & Zhang, Y. (2023). Deep learning for medical image-based cancer diagnosis. Cancers, 15(14), 3608. https://doi.org/10.3390/cancers15143608
• Jwaid, W., Al-Husseini, Z., & Sabry, A. (2021). Development of brain tumor segmentation of magnetic resonance imaging (MRI) using U-Net deep learning. Eastern-European Journal of Enterprise Technologies, 4(2), 33–39. https://doi.org/10.15587/1729-4061.2021.238957
• Kalkan, M., Guzel, M., Ekinci, F., Sezer, E., & Aşuroğlu, T. (2024). Comparative analysis of deep learning methods on CT images for lung cancer specification. Cancers, 16. https://doi.org/10.3390/cancers16193321
• Kaur, R., Gholamhosseini, H., & Lindén, M. (2025). Advanced deep learning models for melanoma diagnosis in computer-aided skin cancer detection. Sensors, 25(3), 594. https://doi.org/10.3390/s25030594
• Lee, Y., Moon, H., Choi, M., Jung, S., Park, Y., Lee, J., Kim, D., Rha, S., Kim, S., Lee, K., Choi, Y., Lee, Y., Lee, W., Lee, S., Grimm, R., Von Busch, H., Han, D., Lou, B., & Kamen, A. (2025). MRI-based deep learning algorithm for assisting clinically significant prostate cancer detection: A bicenter prospective study. Radiology, 314(3), e232788. https://doi.org/10.1148/radiol.232788
• Li, Y., & Shen, L. (2017). Skin lesion analysis towards melanoma detection using deep learning network. Sensors, 18(2), 556. https://doi.org/10.3390/s18020556
• Luo, L., Wang, X., Lin, Y., Tan, A., Chan, R., Vardhanabhuti, V., Chu, W., Cheng, K., & Chen, H. (2023). Deep learning in breast cancer imaging: A decade of progress and future directions. IEEE Reviews in Biomedical Engineering, 18, 130–151. https://doi.org/10.1109/RBME.2024.3357877
• Malarvannan, S., & Maruthamuthu, A. (2025). A review on lung cancer classification using deep learning techniques. IEEE Access, 13, 76161–76184. https://doi.org/10.1109/ACCESS.2025.3564633
• Mayank, S. (2024). Enhancing skin cancer detection through deep learning techniques. International Scientific Journal of Engineering and Management, 3(2), Article isjem01573. https://doi.org/10.55041/isjem01573
• Mokhtar, M., Abdel-Galil, H., & Khoriba, G. (2023). Brain tumor semantic segmentation using residual U-Net++ encoder-decoder architecture. International Journal of Advanced Computer Science and Applications, 14(6). https://doi.org/10.14569/ijacsa.2023.01406119
• Nasser, M., & Yusof, U. (2023). Deep learning based methods for breast cancer diagnosis: A systematic review and future direction. Diagnostics, 13. https://doi.org/10.3390/diagnostics13010161
• Paçal, I., Karaboğa, D., Bastürk, A., Akay, B., & Nalbantoglu, Ö. (2020). A comprehensive review of deep learning in colon cancer. Computers in Biology and Medicine, 126, 104003. https://doi.org/10.1016/j.compbiomed.2020.104003
• Sangui, S., Iqbal, T., Chandra, P., Ghosh, S., & Ghosh, A. (2023). 3D MRI segmentation using U-Net architecture for the detection of brain tumor. Procedia Computer Science, 218, 2005–2013. https://doi.org/10.1016/j.procs.2023.01.036
• Sharafaddini, A., Esfahani, K., & Mansouri, N. (2024). Deep learning approaches to detect breast cancer: A comprehensive review. Multimedia Tools and Applications. https://doi.org/10.1007/s11042-024-20011-6
• Shen, L., Margolies, L., Rothstein, J., Fluder, E., McBride, R., & Sieh, W. (2017). Deep learning to improve breast cancer detection on screening mammography. Scientific Reports, 9. https://doi.org/10.1038/s41598-019-48995-4
• Shimazaki, A., Ueda, D., Choppin, A., Yamamoto, A., Honjo, T., Shimahara, Y., & Miki, Y. (2022). Deep learning-based algorithm for lung cancer detection on chest radiographs using the segmentation method. Scientific Reports, 12. https://doi.org/10.1038/s41598-021-04667-w
• Singh, S., Sinha, A., Singh, H., Mahanti, A., Patel, A., Mahajan, S., Pandit, A., & Vijayakumar, V. (2023). A novel deep learning-based technique for detecting prostate cancer in MRI images. Multimedia Tools and Applications, 83, 14173–14187. https://doi.org/10.1007/s11042-023-15793-0
• Tembhurne, J., Hebbar, N., Patil, H., & Diwan, T. (2023). Skin cancer detection using ensemble of machine learning and deep learning techniques. Multimedia Tools and Applications, 1–24. https://doi.org/10.1007/s11042-023-14697-3
• Tolkach, Y., Dohmgörgen, T., Toma, M., & Kristiansen, G. (2020). High-accuracy prostate cancer pathology using deep learning. Nature Machine Intelligence, 2, 411–418. https://doi.org/10.1038/s42256-020-0200-7
• Tran, K., Kondrashova, O., Bradley, A., Williams, E., Pearson, J., & Waddell, N. (2021). Deep learning in cancer diagnosis, prognosis and treatment selection. Genome Medicine, 13(1), 152. https://doi.org/10.1186/s13073-021-00968-x
• Tufail, A., Yu, Y., Kaabar, M. K. A., Martínez, F., Junejo, A., Ullah, I., & Khan, R. (2021). Deep learning in cancer diagnosis and prognosis prediction: A minireview on challenges, recent trends, and future directions. Computational and Mathematical Methods in Medicine, 2021, 9025470. https://doi.org/10.1155/2021/9025470
• Turkbey, B. (2023). Editorial for “Deep-learning models for detection and localization of visible clinically significant prostate cancer on multi-parametric MRI”. Journal of Magnetic Resonance Imaging, 58(4), 1073–1074. https://doi.org/10.1002/jmri.28609
• Turkbey, B., & Haider, M. (2021). Deep learning-based artificial intelligence applications in prostate MRI: Brief summary. The British Journal of Radiology, 94(1124), 20210563. https://doi.org/10.1259/bjr.20210563
• Wang, L. (2022). Deep learning techniques to diagnose lung cancer. Cancers, 14(22), 5569. https://doi.org/10.3390/cancers14225569
• Xu, Y., Hosny, A., Zeleznik, R., Parmar, C., Coroller, T., Franco, I., Mak, R., & Aerts, H. (2019). Deep learning predicts lung cancer treatment response from serial medical imaging. Clinical Cancer Research, 25, 3266–3275. https://doi.org/10.1158/1078-0432.CCR-18-2495
• Xue, P., Wang, J., Qin, D., Yan, H., Qu, Y., Seery, S., Jiang, Y., & Qiao, Y. (2022). Deep learning in image-based breast and cervical cancer detection: A systematic review and meta-analysis. NPJ Digital Medicine, 5, 115. https://doi.org/10.1038/s41746-022-00559-z
• Yari, Y., Nguyen, T., & Nguyen, H. (2020). Deep learning applied for histological diagnosis of breast cancer. IEEE Access, 8, 162432–162448. https://doi.org/10.1109/ACCESS.2020.3021557
• Zhu, W., Xie, L., Han, J., & Guo, X. (2020). The application of deep learning in cancer prognosis prediction. Cancers, 12(3), 603. https://doi.org/10.3390/cancers12030603