Classification of Cataract Disease with a DenseNet201 Based Deep Learning Model

Year 2022, Volume: 12 Issue: 3, 1264 - 1276, 01.09.2022

Halit Çetiner , İbrahim Çetiner

https://doi.org/10.21597/jist.1098718

Cited By: 3

Abstract

Cataracts are among the most serious eye diseases and can cause blindness if left untreated. Since it is a treatable disease, professional knowledge of specialist ophthalmologists is needed. Ophthalmologists need to analyze images of the eye to detect clinical cataracts in an early stage. Detection of cataracts at an early stage prevents the disease from progressing and causing serious costs such as blindness. At this point, it is a tiring and costly process for specialist ophthalmologists to constantly check their patients. It is not possible for ophthalmologists to constantly monitor their patients. Due to the stated problems, in this article, a study was carried out to develop a deep learning model that helps specialist ophthalmologists through cataract images. In the developed model, an automatic classification of images with normal and cataract lesions was performed by proposing a model based on pre-trained neural networks. During the development of the proposed model, the performance of the classification process was increased by making fine adjustments to the pre-trained neural network called DenseNet201. To compare the performance level of the proposed model, the results obtained from the model consisting of the basic DenseNet201 structure without using any additional layers were used. When both models are evaluated, it has been shown that the proposed deep learning model achieves 10% more success than the basic DenseNet201 deep learning model. The proposed model can be used as an auxiliary tool for doctors in different health problems such as cataracts, which are commonly encountered today.

Keywords

Cataract, DenseNet201, Transfer learning, Classification.

References

• Allen, D., Vasavada, A. 2006. Cataract and surgery for cataract. BMJ (Clinical Research Ed.), 333(7559): 128–132. https://doi.org/10.1136/bmj.333.7559.128.
• Bakator, M., Radosav, D. 2018. Deep Learning and Medical Diagnosis: A Review of Literature. Multimodal Technologies and Interaction . https://doi.org/10.3390/mti2030047.
• Çetiner, H., Kara, B. 2022. Recurrent Neural Network Based Model Development for Wheat Yield Forecasting. Journal of Engineering Sciences of Adiyaman University, 9(16): 204–218. https://doi.org/10.54365/adyumbd.1075265.
• Chouhan, V., Singh, S. K., Khamparia, A., Gupta, D., Tiwari, P., Moreira, C., de Albuquerque, V. H. C. 2020. A Novel Transfer Learning Based Approach for Pneumonia Detection in Chest X-ray Images. Applied Sciences . https://doi.org/10.3390/app10020559.
• Doi, K. 2007. Computer-aided diagnosis in medical imaging: historical review, current status and future potential. Computerized Medical Imaging and Graphics, 31(4–5): 198–211.
• Ertuğrul, Ö. F., Acar, E., Aldemir, E., Öztekin, A. 2021. Automatic diagnosis of cardiovascular disorders by sub images of the ECG signal using multi-feature extraction methods and randomized neural network. Biomedical Signal Processing and Control, 64. https://doi.org/https://doi.org/10.1016/j.bspc.2020.102260.
• Fan, W., Shen, R., Zhang, Q., Yang, J.-J., Li, J. 2015. Principal component analysis based cataract grading and classification. In 2015 17th International Conference on E-health Networking, Application & Services (HealthCom), 459–462. https://doi.org/10.1109/HealthCom.2015.7454545.
• Flaxman, S. R., Bourne, R. R. A., Resnikoff, S., Ackland, P., Braithwaite, T., Cicinelli, M. V, Kempen, J. H. 2017. Global causes of blindness and distance vision impairment 1990–2020: a systematic review and meta-analysis. The Lancet Global Health, 5(12).
• Foster, P. J., Wong, T. Y., Machin, D., Johnson, G. J., Seah, S. K. L. 2003. Risk factors for nuclear, cortical and posterior subcapsular cataracts in the Chinese population of Singapore: the Tanjong Pagar Survey. The British Journal of Ophthalmology, 87(9): 1112–1120. https://doi.org/10.1136/bjo.87.9.1112.
• Fraser, M. L., Meuleners, L. B., Lee, A. H., Ng, J. Q., Morlet, N. 2013. Vision, quality of life and depressive symptoms after first eye cataract surgery. Psychogeriatrics, 13(4): 237–243.
• Gao, X., Lin, S., Wong, T. Y. 2015. Automatic feature learning to grade nuclear cataracts based on deep learning. IEEE Transactions on Biomedical Engineering, 62(11): 2693–2701.
• Goutte, C., Gaussier, E. 2005. A Probabilistic Interpretation of Precision, Recall and F-Score, with Implication for Evaluation. In Lecture Notes in Computer Science, 3408: 345–359. https://doi.org/10.1007/978-3-540-31865-1_25.
• Grewal, P. S., Oloumi, F., Rubin, U., Tennant, M. T. S. 2018. Deep learning in ophthalmology: a review. Canadian Journal of Ophthalmology, 53(4): 309–313. https://doi.org/https://doi.org/10.1016/j.jcjo.2018.04.019.
• Guilbert, J. J. 1999. The World Health Report 1998--Life in the 21st Century. A Vision for All. Education for Health, 12(3): 391.
• Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K. Q. 2017. Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, (pp. 4700–4708).
• Imran, A., Li, J., Pei, Y., Akhtar, F., Mahmood, T., Zhang, L. 2021. Fundus image-based cataract classification using a hybrid convolutional and recurrent neural network. The Visual Computer, 37(8): 2407–2417.
• Jayachitra, S., Nitheesh Kanna, K., Pavithra, G., Ranjeetha, T. 2021. A Novel Eye Cataract Diagnosis and Classification Using Deep Neural Network. Journal of Physics: Conference Series, 1937(1).
• K S, Y., Mithra, N. M., KS, V., R, K. 2021. Classification of diabetic retinopathy through identification of diagnostic keywords. In 2021 Third International Conference on Inventive Research in Computing Applications (ICIRCA), 716–721.
• Kingma, D., Ba, J. 2014. Adam: A Method for Stochastic Optimization. International Conference on Learning Representations.
• Kumar, B. R., Shimna, M. P. 2017. Recent approaches for automatic cataract detection analysis using image processing. Journal of Network Communications and Emerging Technologies (JNCET), 7(10).
• LeCun, Y., Bengio, Y., Hinton, G. 2015. Deep learning. Nature, 521(7553): 436–444. https://doi.org/10.1038/nature14539.
• Lee, A., Taylor, P., Kalpathy-Cramer, J., Tufail, A. 2017. Machine Learning Has Arrived! Ophthalmology, 124(12): 1726–1728.
• Li, J., Xie, L., Zhang, L., Liu, L., Li, P., Yang, J., Wang, Q. 2019. Interpretable Learning: A Result-Oriented Explanation for Automatic Cataract Detection. In Lecture Notes in Electrical Engineering 542: 296–306). Springer Singapore.
• Liu, X., Jiang, J., Zhang, K., Long, E., Cui, J., Zhu, M., Lin, Z. 2017. Localization and diagnosis framework for pediatric cataracts based on slit-lamp images using deep features of a convolutional neural network. PloS One, 12(3).
• Manchalwar, M., Warhade, K. 2017. Detection of Cataract and Conjunctivitis Disease Using Histogram of Oriented Gradient. International Journal of Engineering and Technology, 9: 2400–2406. https://doi.org/10.21817/ijet/2017/v9i3/1709030214.
• Matryx, O. 2019. Ocular Disease Recognition. Retrieved from https://www.kaggle.com/datasets/andrewmvd/ocular-disease-recognition-odir5k.
• Mobley, J. A., Brueggemeier, R. W. 2002. Increasing the DNA damage threshold in breast cancer cells. Toxicology and Applied Pharmacology, 180(3): 219–226. https://doi.org/10.1006/taap.2002.9391.
• Pacal, I., Karaboga, D. 2021. A robust real-time deep learning based automatic polyp detection system. Computers in Biology and Medicine, 134: 104519. https://doi.org/https://doi.org/10.1016/j.compbiomed.2021.104519.
• Pacal, I., Karaboga, D., Basturk, A., Akay, B., Nalbantoglu, U. 2020. A comprehensive review of deep learning in colon cancer. Computers in Biology and Medicine, 126: 104003. https://doi.org/https://doi.org/10.1016/j.compbiomed.2020.104003.
• Pacal, I., Karaman, A., Karaboga, D., Akay, B., Basturk, A., Nalbantoglu, U., Coskun, S. 2022. An efficient real-time colonic polyp detection with YOLO algorithms trained by using negative samples and large datasets. Computers in Biology and Medicine, 141: 105031. https://doi.org/https://doi.org/10.1016/j.compbiomed.2021.105031.
• Pizzarello, L., Abiose, A., Ffytche, T., Duerksen, R., Thulasiraj, R., Taylor, H., Resnikoff, S. 2004. VISION 2020: The Right to Sight: A Global Initiative to Eliminate Avoidable Blindness. Archives of Ophthalmology, 122(4): 615–620. https://doi.org/10.1001/archopht.122.4.615.
• Pleiss, G., Chen, D., Huang, G., Li, T., Van der Maaten, L., Weinberger, K. Q. 2017. Memory-Efficient Implementation of DenseNets.
• Qiao, Z., Zhang, Q., Dong, Y., Yang, J. 2017. Application of SVM based on genetic algorithm in classification of cataract fundus images. In 2017 IEEE International Conference on Imaging Systems and Techniques (IST), 1–5. https://doi.org/10.1109/IST.2017.8261541.
• Qin, X., Zhou, Y., He, Z., Wang, Y., Tang, Z. 2017. A faster R-CNN based method for comic characters face detection. In 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), 1: 1074–1080. IEEE.
• Wang, Y., Tang, C., Wang, J., Sang, Y., Lv, J. 2021. Cataract detection based on ocular B-ultrasound images by collaborative monitoring deep learning. Knowledge-Based Systems, 231: 107442. https://doi.org/https://doi.org/10.1016/j.knosys.2021.107442.
• Wong, T. Y., Loon, S.-C., Saw, S.-M. 2006. The epidemiology of age related eye diseases in Asia. The British Journal of Ophthalmology, 90(4): 506–511. https://doi.org/10.1136/bjo.2005.083733.
• Xi, P., Shu, C., Goubran, R. 2018. Abnormality detection in mammography using deep convolutional neural networks. In 2018 IEEE International Symposium on Medical Measurements and Applications (MeMeA), 1–6.
• Xiong, L., Li, H., Xu, L. 2017. An Approach to Evaluate Blurriness in Retinal Images with Vitreous Opacity for Cataract Diagnosis. Journal of Healthcare Engineering, 1–16. https://doi.org/10.1155/2017/5645498.
• Xu, X., Guan, Y., Li, J., Ma, Z., Zhang, L., Li, L. 2021. Automatic glaucoma detection based on transfer induced attention network. BioMedical Engineering OnLine, 20(1): 39. https://doi.org/10.1186/s12938-021-00877-5.
• Xu, X., Zhang, L., Li, J., Guan, Y., Zhang, L. 2020. A Hybrid Global-Local Representation CNN Model for Automatic Cataract Grading. IEEE Journal of Biomedical and Health Informatics, 24(2): 556–567. https://doi.org/10.1109/JBHI.2019.2914690.
• Yadav, S., Das, S., Murugan, R., Dutta Roy, S., Agrawal, M., Goel, T., Dutta, A. 2022. Performance analysis of deep neural networks through transfer learning in retinal detachment diagnosis using fundus images. Sādhanā, 47(2): 49. https://doi.org/10.1007/s12046-022-01822-5.
• Zhang, L., Li, J., Zhang, i, Han, H., Liu, B., Yang, J., Wang, Q. 2017. Automatic cataract detection and grading using Deep Convolutional Neural Network. In 2017 IEEE 14th International Conference on Networking, Sensing and Control (ICNSC), 60–65. https://doi.org/10.1109/ICNSC.2017.8000068.