Medical Informatics IV

EnglishTürkçe

CHAPTER


DOI :10.26650/B/T3.2024.40.009   IUP :10.26650/B/T3.2024.40.009    Full Text (PDF)

Applications of Deep Learning Methods in the Field of Health: Prediction of Diabetic Retinopathy

Cihan Çiftçi, Halim Kazan

For many years, medical imaging techniques have been used as an important tool in the diagnosis and treatment of diseases. Deep learning methods, which are a sub-branch of machine learning, play an important role in the early diagnosis of diseases, in the rapid interpretation of images obtained from medical imaging tools, in reducing the workload of medical professionals, providing insights in the disagreements to be experienced at the point of decision-making, and making the right decisions. In the management, interpretation and analysis of heterogeneous big data obtained from different sources in the field of health, deep learning methods are used today as a more powerful tool than traditional methods in the field of image processing. In recent years, deep learning methods have increasingly become the focus in the analysis of big data obtained in image processing in disease detection. In this study, the use of deep learning methods in the field of image processing in the early diagnosis of diseases has been examined. In addition, deep learning models were used in the early diagnosis of diabetic retinopathy in the study. The image data obtained from APTOS 2019 Blindness Detection was used in the diagnosis of diabetic retinopathy disease. According to the results obtained, it was concluded that deep learning models are an important method in image processing, and its use in the early diagnosis of diabetic retinopathy will be a serious tool for medical professionals.


Keywords: Deep learning, image processing, health applications, early diagnosis
DOI :10.26650/B/T3.2024.40.009   IUP :10.26650/B/T3.2024.40.009    Full Text (PDF)

Deri̇n Öğrenme Yöntemleri̇ni̇n Sağlık Alanında Uygulamaları: Di̇yabeti̇k Reti̇nopati̇ Tahmi̇ni̇

Cihan Çiftçi, Halim Kazan

Uzun yıllardır tıbbi görüntüleme teknikleri hastalıkların tanı ve tedavisinde önemli bir araç olarak kullanılmaktadır. Makine öğrenmesinin bir alt dalı olan derin öğrenme yöntemleri, hastalıkların erken teşhisinde, tıbbi görüntüleme araçlarından elde edilen görüntülerin hızlı yorumlanmasında, tıp profesyonellerinin iş yükünün azaltılmasında, karar verme noktasında yaşanacak anlaşmazlıklara dair içgörü sağlanmasında ve doğru kararlar verilmesinde önemli rol oynamaktadır. Sağlık alanında farklı kaynaklardan elde edilen heterojen büyük verilerin yorumlanması ve analizinde, görüntü işleme alanında günümüzde geleneksel yöntemlere göre daha güçlü bir araç olarak derin öğrenme yöntemleri kullanılmaktadır. Son yıllarda hastalık tespitinde görüntü işlemede elde edilen büyük verilerin analizinde derin öğrenme yöntemleri giderek daha fazla odak noktası haline gelmiştir. Bu çalışmada görüntü işleme alanında derin öğrenme yöntemlerinin hastalıkların erken teşhisinde kullanım alanları incelenmiştir. Ayrıca çalışmada diyabetik retinopatinin erken teşhisinde derin öğrenme modelleri oluşturulmuştur. APTOS 2019 Körlük Tespiti’nden elde edilen görüntü verileri diyabetik retinopati hastalığının tanısında kullanılmıştır. Elde edilen sonuçlara göre derin öğrenme modellerinin görüntü işlemede önemli bir yöntem olduğu ve diyabetik retinopatinin erken tanısında kullanımının tıp profesyonelleri için ciddi bir araç olacağı sonucuna varılmıştır.


Keywords: Derin öğrenme, görüntü işleme, sağlık uygulamaları, erken teşhis

References

  • Abramoff, M. D., Lou, Y., Erginay, A., Clarida, W., Amelon, R., Folk, J. C., & Niemeijer, M. (2016). Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning. Investigative Ophthalmology & Visual Science, 57(13), 5200-5206. google scholar
  • Alyoubi, W. L., Abulkhair, M. F., & Shalash, W. M. (2021). Diabetic retinopathy fundus image classification and lesions localization system using deep learning. Sensors, 21(11), 3704. google scholar
  • Behrmann, J., Etmann, C., Boskamp, T., Casadonte, R., Kriegsmann, J., & Maap, P. (2018). Deep learning for tumor classification in imaging mass spectrometry. Bioinformatics, 34(7), 1215-1223. google scholar
  • Chudzik, P., Majumdar, S., Caliva, F., Al-Diri, B., & Hunter, A. (2018). Microaneurysm detection using fully convolutional neural networks. Computer Methods and Programs in Biomedicine, 158, 185-192. google scholar
  • Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115-118. google scholar
  • Gayathri, S., Gopi, V. P., & Palanisamy, P. (2020). A lightweight CNN for Diabetic Retinopathy classification from fundus images. Biomedical Signal Processing and Control, 62, 102115. google scholar
  • Guariguata, L., Whiting, D. R., Hambleton, I., Beagley, J., Linnenkamp, U., & Shaw, J. E. (2014). Global estimates of diabetes prevalence for 2013 and projections for 2035. Diabetes Research and Clinical Prac-tice, 103(2), 137-149. https://doi.org/10.1016Zj.diabres.2013.11.002 google scholar
  • Hosseini-Asl, E., Keynton, R. & El-Baz, A. Alzheimer’s disease diagnostics by a 3D deeply supervised adaptable convolutional network. Frontiers In Bioscience. Landmark, 23, 584-596 (2018). google scholar
  • Hussain, E., Mahanta, L. B., Das, C. R., & Talukdar, R. K. (2020). A comprehensive study on the multi-class cervical cancer diagnostic prediction on pap smear images using a fusion-based decision from ensemble deep convolutional neural network. Tissue and Cell, 65, 101347. google scholar
  • Jinfeng, G., Qummar, S., Junming, Z., Ruxian, Y., & Khan, F. G. (2020). Ensemble framework of deep CNNs for diabetic retinopathy detection. Computational Intelligence and Neuroscience, (1), 8864698. google scholar
  • Jinnai, S., Yamazaki, N., Hirano, Y., Sugawara, Y., Ohe, Y., & Hamamoto, R. (2020). The development of a skin cancer classification system for pigmented skin lesions using deep learning. Biomolecules, 10(8), 1123. google scholar
  • Khan, S., Islam, N., Jan, Z., Din, I. U., & Rodrigues, J. J. C. (2019). A novel deep learning based framework for the detection and classification of breast cancer using transfer learning. Pattern Recognition Letters, 125, 1-6. google scholar
  • Lakshmanaprabu, S. K., Mohanty, S. N., Shankar, K., Arunkumar, N., & Ramirez, G. (2019). Optimal deep learning model for classification of lung cancer on CT images. Future Generation Computer Systems, 92, 374-382. google scholar
  • Li, R., Zhang, W., Suk, H. I., Wang, L., Li, J., Shen, D., & Ji, S. (2014). Deep learning based imaging data completion for improved brain disease diagnosis. In Medical Image Computing and Computer-Assisted Intervention—MICCAI 2014: 17th International Conferen.ce, Boston, MA, USA, September 14-18, 2014, Proceedings, Part III 17 (pp. 305-312). Springer International Publishing. google scholar
  • Mushtaq, G., & Siddiqui, F. (2021, February). Detection of diabetic retinopathy using deep learning metho-dology. In IOP conference series: materials science and engineering, 1070(1), 012049. IOP Publishing. google scholar
  • Nawaz, M., Sewissy, A. A., & Soliman, T. H. A. (2018). Multi-class breast cancer classification using deep learning convolutional neural network. International Journal of Advanced Computer Science and Applica-tions, 9(6), 316-332. google scholar
  • Nguyen, Q. H., Muthuraman, R., Singh, L., Sen, G., Tran, A. C., Nguyen, B. P., & Chua, M. (2020, January). Diabetic retinopathy detection using deep learning. In Proceedings of the 4th International Conference on Machine Learning and Soft Computing (pp. 103-107). google scholar
  • Oulhadj, M., Riffi, J., Chaimae, K., Mahraz, A. M., Ahmed, B., Yahyaouy, A., ... & Tairi, H. (2022). Diabetic retinopathy prediction based on deep learning and deformable registration. Multimedia Tools and Applica-tions, 81(20), 28709-28727. google scholar
  • Pour, A. M., Seyedarabi, H., Jahromi, S. H. A., & Javadzadeh, A. (2020). Automatic detection and monitoring of diabetic retinopathy using efficient convolutional neural networks and contrast limited adaptive histogram equalization. IEEE Access, 8, 136668-136673. google scholar
  • Pratt, H., Coenen, F., Broadbent, D. M., Harding, S. P., & Zheng, Y. (2016). Convolutional neural networks for diabetic retinopathy. Procedia Computer Science, 90, 200-205. google scholar
  • Raja Kumar, R., Pandian, R., Prem Jacob, T., Pravin, A., & Indumathi, P. (2021). Detection of diabetic retino-pathy using deep convolutional neural networks. In Computational Vision and Bio-Inspired Computing: ICCVBIC 2020 (pp. 415-430). Springer Singapore. google scholar
  • Rajpurkar, P. (2017). CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Lear-ning. ArXiv abs/1711, 5225. google scholar
  • Shen, S., Han, S. X., Aberle, D. R., Bui, A. A., & Hsu, W. (2019). An interpretable deep hierarchical semantic convolutional neural network for lung nodule malignancy classification. Expert Systems with Applicati-ons, 128, 84-95. google scholar
  • Sopharak, A., Uyyanonvara, B., & Barman, S. (2013). Automated microaneurysm detection algorithms applied to diabetic retinopathy retinal images. Maejo International Journal of Science & Technology, 7(2). google scholar
  • Thota, N. B., & Reddy, D. U. (2020, August). Improving the accuracy of diabetic retinopathy severity classifi-cation with transfer learning. In 2020 IEEE 63rd International Midwest Symposium on Circuits and Systems (MWSCAS) (pp. 1003-1006). IEEE. google scholar
  • Ucuzal, H., YAŞAR, Ş., & Çolak, C. (2019, October). Classification of brain tumor types by deep learning with convolutional neural network on magnetic resonance images using a developed web-based interface. In 2019 3rd international symposium on multidisciplinary studies and innovative technologies (ISMSIT) (pp. 1-5). IEEE. google scholar
  • Waghmare, V. K., & Kolekar, M. H. (2021). Brain tumor classification using deep learning. Internet of Things for Healthcare Technologies, 155-175. google scholar
  • Wang, S., Kang, B., Ma, J., Zeng, X., Xiao, M., Guo, J., ... & Xu, B. (2021). A deep learning algorithm using CT images to screen for Corona Virus Disease (COVID-19). European Radiology, 31, 6096-6104. google scholar
  • Yang, D., Martinez, C., Visuna, L., Khandhar, H., Bhatt, C., & Carretero, J. (2021). Detection and analysis of COVID-19 in medical images using deep learning techniques. Scientific Reports, 11(1), 19638. google scholar
  • Zhang, H., Han, L., Chen, K., Peng, Y., & Lin, J. (2020). Diagnostic efficiency of the breast ultrasound com-puter-aided prediction model based on convolutional neural network in breast cancer. Journal of Digital Imaging, 33, 1218-1223. google scholar


SHARE




Istanbul University Press aims to contribute to the dissemination of ever growing scientific knowledge through publication of high quality scientific journals and books in accordance with the international publishing standards and ethics. Istanbul University Press follows an open access, non-commercial, scholarly publishing.