KaydedilenlerDerginizi ekleyin

Dergiler / Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi / 2026 / Cilt: 40 Sayı: 2

Ağırlıklı CNN Topluluğu Tabanlı Kolorektal Kanser Tespiti

Mustafa Yurdakul(KIRIKKALE ÜNİVERSİTESİ)
Sakir Tasdemır(SELÇUK ÜNİVERSİTESİ)0000-0002-2433-246X
Sayfa
270–287
Yayın Tarihi
2024-08-31
DOI
—
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Deneysel sonuçlar, ağırlıklı topluluk yönteminin sınıflandırma performansını önemli ölçüde iyileştirdiğini göstermektedir.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:Kolorektal Kanser(KKR), dünya çapında yaygın ve potansiyel olarak ölümcül bir hastalıktır.
📊
Temel Bulgular & Veri:Erken ve doğru teşhis, teşhisin zaman alması, insan hatalarının olasılığı ve uzman doktor eksikliği nedeniyle zor bir süreçtir.
💡
Sonuç & Katkı:Deneysel sonuçlar, ağırlıklı topluluk yönteminin sınıflandırma performansını önemli ölçüde iyileştirdiğini göstermektedir.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Ağırlıklı Topluluk Öğrenmesi · Derin Öğrenme · Kolorektal Kanser

Özet

Kolorektal Kanser(KKR), dünya çapında yaygın ve potansiyel olarak ölümcül bir hastalıktır. Erken ve doğru teşhis, teşhisin zaman alması, insan hatalarının olasılığı ve uzman doktor eksikliği nedeniyle zor bir süreçtir. Bu çalışmada, tıbbi görüntülerden kolorektal kanserin teşhisini basitleştirmek ve hızlandırmak için derin öğrenme algoritmaları kullanılmıştır. Çeşitli KKR evrelerini içeren Enteroskop Biyopsi Histopatolojik H&E(EBHI) görüntü veri seti kullanıldı. Çeşitli önceden eğitilmiş Evrişimli Sinir Ağı modelleri, görüntüleri iyi huylu veya kötü huylu olarak sınıflandırmak için kullanıldı. Ayrıca, sınıflandırma doğruluğunu artırmak için üç en iyi model ağırlıklı topluluk yöntemiyle birleştirildi. Deneysel sonuçlar, ağırlıklı topluluk yönteminin sınıflandırma performansını önemli ölçüde iyileştirdiğini göstermektedir.

🔖

Bu Makaleye Atıf Yap

Yurdakul, M., Tasdemır, S. (2024). Ağırlıklı CNN Topluluğu Tabanlı Kolorektal Kanser Tespiti. Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi, Cilt: 40 Sayı: 2, 270–287. https://ajindex.com/articles/1266724

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (40)

  1. Keum, N. and E. Giovannucci, Global burden of colorectal cancer: emerging trends, risk factors and prevention strategies. Nature reviews Gastroenterology & hepatology, 2019. 16(12): p. 713- 732.
  2. Mármol, I., et al., Colorectal carcinoma: a general overview and future perspectives in colorectal cancer. International journal of molecular sciences, 2017. 18(1): p. 197.
  3. Świderska, M., et al., The diagnostics of colorectal cancer. Contemporary Oncology/Współczesna Onkologia, 2014. 18(1): p. 1-6.
  4. Pamudurthy, V., N. Lodhia, and V.J. Konda. Advances in endoscopy for colorectal polyp detection and classification. in Baylor University Medical Center Proceedings. 2020. Taylor & Francis.
  5. Wang, K.-S., et al., Accurate diagnosis of colorectal cancer based on histopathology images using artificial intelligence. BMC medicine, 2021. 19(1): p. 1-12.
  6. Hitchcock, C.L., The future of telepathology for the developing world. Archives of pathology & laboratory medicine, 2011. 135(2): p. 211-214.
  7. Black-Schaffer, W.S., et al., Training pathology residents to practice 21st century medicine: a proposal. Academic Pathology, 2016. 3: p. 2374289516665393.
  8. Murtaza, G., et al., Deep learning-based breast cancer classification through medical imaging modalities: state of the art and research challenges. Artificial Intelligence Review, 2020. 53: p. 1655-1720.
  9. Esteva, A., et al., Dermatologist-level classification of skin cancer with deep neural networks. nature, 2017. 542(7639): p. 115-118.
  10. Mohsen, H., et al., Classification using deep learning neural networks for brain tumors. Future Computing and Informatics Journal, 2018. 3(1): p. 68-71.
  11. Kuntz, S., et al., Gastrointestinal cancer classification and prognostication from histology using deep learning: Systematic review. European Journal of Cancer, 2021. 155: p. 200-215.
  12. Chen, X., et al., Recent advances and clinical applications of deep learning in medical image analysis. Medical Image Analysis, 2022: p. 102444.
  13. Litjens, G., et al., A survey on deep learning in medical image analysis. Medical image analysis, 2017. 42: p. 60-88.
  14. Rajpurkar, P., et al., Chexnet: Radiologist-level pneumonia detection on chest x-rays with deep learning. arXiv preprint arXiv:1711.05225, 2017.
  15. Akbari, M., et al. Classification of informative frames in colonoscopy videos using convolutional neural networks with binarized weights. in 2018 40th annual international conference of the IEEE engineering in medicine and biology society (EMBC). 2018. IEEE.
  16. Ponzio, F., et al. Colorectal cancer classification using deep convolutional networks. in Proceedings of the 11th international joint conference on biomedical engineering systems and technologies. 2018.
  17. Poudel, S., et al., Colorectal disease classification using efficiently scaled dilation in convolutional neural network. IEEE Access, 2020. 8: p. 99227-99238.
  18. Sarwinda, D., et al., Deep learning in image classification using residual network (ResNet) variants for detection of colorectal cancer. Procedia Computer Science, 2021. 179: p. 423-431.
  19. Su, Y., et al., Colon cancer diagnosis and staging classification based on machine learning and bioinformatics analysis. Computers in Biology and Medicine, 2022. 145: p. 105409.
  20. Naga Raju, M.S. and B. Srinivasa Rao, Lung and colon cancer classification using hybrid principle component analysis network‐extreme learning machine. Concurrency and Computation: Practice and Experience, 2023. 35(1): p. e7361.
  21. Kumar, A., A. Vishwakarma, and V. Bajaj, Crccn-net: Automated framework for classification of colorectal tissue using histopathological images. Biomedical Signal Processing and Control, 2023. 79: p. 104172.
  22. Hu, W., et al., EBHI: A new Enteroscope Biopsy Histopathological H&E Image Dataset for image classification evaluation. Physica Medica, 2023. 107: p. 102534.
  23. Yengec-Tasdemir, S.B., et al., Improved classification of colorectal polyps on histopathological images with ensemble learning and stain normalization. Computer Methods and Programs in Biomedicine, 2023. 232: p. 107441.
  24. Van der Maaten, L. and G. Hinton, Visualizing data using t-SNE. Journal of machine learning research, 2008. 9(11).
  25. Yurdakul, M., İ. Atabaş, and Ş. Taşdemir. Flower Pollination Algorithm-Optimized Deep CNN Features for Almond (Prunus dulcis) Classification. in 2024 International Conference on Emerging Systems and Intelligent Computing (ESIC). 2024. IEEE.
  26. Yurdakul, M., İ. Atabaş, and Ş. Taşdemir, Almond (Prunus dulcis) varieties classification with genetic designed lightweight CNN architecture. European Food Research and Technology, 2024: p. 1-14.
  27. Uyar, K., M. Yurdakul, and Ş. Taşdemir, Abc-based weighted voting deep ensemble learning model for multiple eye disease detection. Biomedical Signal Processing and Control, 2024. 96: p. 106617.
  28. Ayhan, B., E. Ayan, and Y. Bayraktar, A novel deep learning-based perspective for tooth numbering and caries detection. Clinical Oral Investigations, 2024. 28(3): p. 178.
  29. Ayan, E., et al., Dental student application of artificial intelligence technology in detecting proximal caries lesions. Journal of Dental Education, 2024. 88(4): p. 490-500.
  30. Ge, S., et al. Detecting masked faces in the wild with lle-cnns. in Proceedings of the IEEE conference on computer vision and pattern recognition. 2017.
  31. Akbar, S., et al. Transitioning between convolutional and fully connected layers in neural networks. in Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: Third International Workshop, DLMIA 2017, and 7th International Workshop, ML-CDS 2017, Held in Conjunction with MICCAI 2017, Québec City, QC, Canada, September 14, Proceedings 3. 2017. Springer.
  32. Zhang, Q. Convolutional neural networks. in Proceedings of the 3rd International Conference on Electromechanical Control Technology and Transportation. 2018.
  33. Albawi, S., T.A. Mohammed, and S. Al-Zawi. Understanding of a convolutional neural network. in 2017 international conference on engineering and technology (ICET). 2017. Ieee.
  34. Huang, G., et al. Densely connected convolutional networks. in Proceedings of the IEEE conference on computer vision and pattern recognition. 2017.
  35. Szegedy, C., et al. Rethinking the inception architecture for computer vision. in Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.
  36. Howard, A.G., et al., Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861, 2017.
  37. Simonyan, K. and A. Zisserman, Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556, 2014.
  38. Chollet, F. Xception: Deep learning with depthwise separable convolutions. in Proceedings of the IEEE conference on computer vision and pattern recognition. 2017.
  39. Polikar, R., Ensemble learning. Ensemble machine learning: Methods and applications, 2012: p. 1-34.
  40. Inaba, Y., J.A. Chen, and S.R. Bergmann, Carotid plaque, compared with carotid intima-media thickness, more accurately predicts coronary artery disease events: a meta-analysis. Atherosclerosis, 2012. 220(1): p. 128-133.