Diagnostic Decision Making on Medical Images Using Deep Learning Models

Gizem Yıldız; Önder Yakut

doi:10.5281/zenodo.8237726

Authors

Gizem Yıldız Department of Information Systems Engineering, Faculty of Technology, Kocaeli University, Kocaeli, Türkiye https://orcid.org/0000-0001-9389-9366
Önder Yakut Department of Information Systems Engineering, Faculty of Technology, Kocaeli University, Kocaeli, Turkey https://orcid.org/0000-0003-0265-7252

DOI:

https://doi.org/10.5281/zenodo.8237726

Keywords:

Convolution neural network (CNN), Deep learning models, Diagnostic system, Google colaboratory, Medical image analysis

Abstract

According to the World Health Organization (WHO), one of every six deaths in the world is caused by cancer. Cancers of the breast, lung, prostate, colon and rectum are the most common. More than 25 per cent of all types cancer are lung and colon cancers. When the disease is detected at an early stage, the survival rate of cancer cases is importantly higher. Therefore, medical image analysis is an important field of study for cancer detection and classification. In other studies in the literature and in this study, the aim was to automate cancer diagnosis by detecting more cases in a shorter time with high performance using artificial intelligence. In this study, image processing and deep learning techniques were used to analyze histopathological colon images from the LC25000 dataset. The colon histopathological images in this dataset were resized, and data augmentation methods were used. In this study, three different CNN model architectures with 3, 6 and 8 layers were developed. In addition, the pretrained CNN architectures VGG16, VGG19, and Resnet50 CNN models were also used in this study. This study proposed a 3-layer CNN model that detects colon cancer with a high accuracy rate of 98.00%. When the results of the proposed 3-layer CNN model were examined, satisfactory performance was observed. Thus, it was concluded that the proposed method can be used for computer-aided decision making systems to diagnose colon cancer. The artificial intelligence models used and developed in this study were implemented on Colab Notebook, a Google Cloud Computing service.

References

Alzubaidi L., Zhang J., Humaidi A. J., Al-Dujaili A., Duan Y., Al-Shamma O., Farhan L. (2021). Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions. Journal of big Data, 8, 1-74. https://doi.org/10.1186/s40537-021-00444-8

Baranwal N., Doravari P., Kachhoria R. (2022). Classification of histopathology images of lung cancer using convolutional neural network (CNN). Shinde S. V., Mahalle P. N., Bendre, V., Castillo O. (Eds.), Disruptive Developments in Biomedical Applications, (pp. 75-89). Florida: CRC Press. https://doi.org/10.48550/arXiv.2112.13553

Başaran E., Cömert Z., Çelik Y., Velappan S., Toğaçar M. (2020). Determination of tympanic membrane region in the middle ear otoscope images with convolutional neural network based YOLO method. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi, 22(66), 919-928. https://doi.org/10.21205/deufmd.2020226625

Borkowski A. A., Bui M. M., Thomas L. B., Wilson C. P., DeLand L. A., Mastorides S. M. (2019). Lung and colon cancer histopathological image dataset (lc25000). arXiv preprint, 1-2.

https://doi.org/10.48550/arXiv.1912.12142

Doğan F., Türkoğlu İ. (2018). Derin öğrenme algoritmalarının yaprak sınıflandırma başarımlarının karşılaştırılması. Sakarya University Journal of Computer and Information Sciences, 1(1), 10-21.

Eker A. G., Duru N. (2021). Medikal görüntü işlemede derin öğrenme uygulamaları. Acta Infologica, 5(2), 459-474. https://doi.org/10.26650/acin.927561

Gazel S. E. R., Bati C. T. (2019). Derin sinir ağları ile en iyi modelin belirlenmesi: mantar verileri üzerine Keras uygulaması. Yuzuncu Yıl University Journal of Agricultural Sciences, 29(3), 406-417. https://doi.org/10.29133/yyutbd.505086

Hatuwal B. K., Thapa H. C. (2020). Lung cancer detection using convolutional neural network on histopathological images. Int. J. Comput. Trends Technol, 68(10), 21-24. https://doi.org/10.14445/22312803/IJCTT-V68I10P104

He K., Zhang X., Ren S., Sun J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition. Las Vegas, NV, USA. https://doi.org/10.1109/CVPR.2016.90

Kadirappa R., Subbian D., Ramasamy P., Ko S. B. (2023). Histopathological carcinoma classification using parallel, cross‐concatenated and grouped convolutions deep neural network. International Journal of Imaging Systems and Technology, 33(3), 1048-1061. https://doi.org/10.1002/ima.22846

Lin J., Han G., Pan X., Liu Z., Chen H., Li D., Han C. (2022). PDBL: Improving Histopathological Tissue Classification with Plug-and-Play Pyramidal Deep-Broad Learning. IEEE Transactions on Medical Imaging, 41(9), 2252-2262. https://doi.org/10.1109/TMI.2022.3161787

Mangal, S., Chaurasia, A., Khajanchi, A. (2020). Convolution neural networks for diagnosing colon and lung cancer histopathological images. arXiv preprint, 1-10. https://doi.org/10.48550/arXiv.2009.03878

Masud M., Sikder N., Nahid A. A., Bairagi A. K., AlZain M. A. (2021). A machine learning approach to diagnosing lung and colon cancer using a deep learning-based classification framework. Sensors, 21(3), 748. https://doi.org/10.3390/s21030748

Osinga D. (2018). Deep learning cookbook: practical recipes to get started quickly. California : O'Reilly Media, Inc.

Öztürk K., Şahin M. E. (2018). Yapay sinir ağları ve yapay zekâ’ya genel bir bakış. Takvim-i Vekayi, 6(2), 25-36.

Raju M. S. N., Rao B. S. (2022, December). Classification of colon cancer through analysis of histopathology images using Transfer Learning. In 2022 IEEE 2nd International Symposium on Sustainable Energy, Signal Processing and Cyber Security (iSSSC). Gunupur, Odisha, India. https://doi.org/10.1109/iSSSC56467.2022.10051631

Rathore S., Hussain M., Ali A., Khan A. (2013). A recent survey on colon cancer detection techniques. IEEE/ACM Transactions on computational biology and bioinformatics, 10(3), 545-563. https://doi.org/10.1109/TCBB.2013.84

Sarwinda D., Bustamam A., Paradisa R. H., Argyadiva T., Mangunwardoyo W. (2020, November). Analysis of deep feature extraction for colorectal cancer detection. In 2020 4th International Conference on Informatics and Computational Sciences. Semarang, Indonesia. https://doi.org/10.1109/ICICoS51170.2020.929899

Seyyarer E., Ayata F., Uçkan T., Karci, A. (2020). Derin öğrenmede kullanilan optimizasyon algoritmalarinin uygulanmasi ve kiyaslanmasi. Computer Science, 5(2), 90-98.

Shandilya S., Nayak S.R. (2022). Analysis of Lung Cancer by Using Deep Neural Network. Mishra M., Sharma R., Kumar Rathore A., Nayak J., Naik B. (Eds) Innovation in Electrical Power Engineering, Communication, and Computing Technology, Lecture Notes in Electrical Engineering, (pp. 427-436). Singapore: Springer. https://doi.org/10.1007/978-981-16-7076-3_37

Siegel R. L., Miller K. D., Goding Sauer A., Fedewa S. A., Butterly L. F., Anderson J. C., Jemal A. (2020). Colorectal cancer statistics, 2020. CA: a cancer journal for clinicians, 70(3), 145-164. https://doi.org/10.3322/caac.21601

Tasnim Z., Chakraborty S., Shamrat F. M. J. M., Chowdhury A. N., Nuha H. A., Karim A., Billah M. M. (2021). Deep learning predictive model for colon cancer patient using CNN-based classification. Int. J. Adv. Comput. Sci. Appl, 12, 687-696. https://doi.org/10.14569/IJACSA.2021.0120880

Toğaçar M., Ergen B., Özyurt F. (2020). Evrişimsel Sinir Ağı Modellerinde Özellik Seçim Yöntemlerini Kullanarak Çiçek Görüntülerinin Sınıflandırılması. Fırat Üniversitesi Mühendislik Bilimleri Dergisi, 32(1), 47-56. https://doi.org/10.35234/fumbd.573630

Toğaçar M. (2021). Disease type detection in lung and colon cancer images using the complement approach of inefficient sets. Computers in Biology and Medicine, 137, 104827. https://doi.org/10.1016/j.compbiomed.2021.104827

Toraman S., Gi̇rgi̇n M, Üstündağ B., Türkoğlu İ. (2019). Classification of the likelihood of colon cancer with machine learning techniques using FTIR signals obtained from plasma. Turkish Journal of Electrical Engineering and Computer Sciences, 27(3), 1765-1779. https://doi.org/10.3906/elk-1801-259

Urban G., Tripathi P., Alkayal, T., Mittal M., Jalali F., Karnes W., Baldi P. (2018). Deep learning localizes and identifies polyps in real time with 96% accuracy in screening colonoscopy. Gastroenterology, 155(4), 1069-1078. https://doi.org/10.1053/j.gastro.2018.06.037

Yakut Ö., (2020). Comparison of clustering methods for early stage diabetes risk prediction using cloud computing. International Black Sea Coastline Countries Symposium-5, Zonguldak, Turkey.

Yakut, Ö. (2023). Diabetes Prediction Using Colab Notebook Based Machine Learning Methods . International Journal of Computational and Experimental Science and Engineering , 9 (1), 36-41. https://doi.org/10.22399/ijcesen.1185474

Yıldırım M., Çınar A. (2022). Classification with respect to colon adenocarcinoma and colon benign tissue of colon histopathological images with a new CNN model: MA_ColonNET. International Journal of Imaging Systems and Technology, 32(1), 155-162. https://doi.org/10.1002/ima.22623

Yurtsever U., (2019). Bölütlenmiş histopatolojik görüntüler üzerinde derin öğrenme yöntemiyle kolon kanseri tespiti, (Doctoral Dissertation), Sakarya Universitesi, Turkey.

Diagnostic Decision Making on Medical Images Using Deep Learning Models

Authors

DOI:

Keywords:

Abstract

References

Downloads

Published

How to Cite

Issue

Section

License

Most read articles by the same author(s)

Make a Submission

Language

Information