Detection and Multiclass Classification of Ocular Diseases using Deep Learning-based Ensemble Model
Keywords:
Automatic Detection, Artificial Intelligence, Deep Learning, Ocular Diseases, Optical Coherence Tomography, Choroidal Neovascularization, Diabetic Macular Edema, DrusenAbstract
The technological advances have made it possible to design and develop automated systems for the detection of diseases which saves time and enables early treatment, fostering good health. A lot of people in developing countries lose their vision to ocular diseases at an early age. To prevent irreversible damage to the eyesight timely detection and treatment are imperative. Nowadays, automatic detection of such vision-threatening diseases is possible with the help of artificial intelligence (AI) systems. Diabetic Mellitus or diabetes is one of the diseases which causes many ocular diseases such as diabetic retinopathy, diabetic macular edema, cataract, and glaucoma. The proposed work automatically detects three such ocular diseases: Choroidal neovascularization, Diabetic macular edema and Drusen. The proposed work uses an ensemble approach to detect ocular diseases wherein three models have been designed. The ensembles have been designed using the feature extractor method of the VGG16, Xception and mobilenet models and then extracted features are given to a convolutional neural network which then trains and classifies the input tomographic images into Choroidal neovascularization, Diabetic macular edema, Drusen and Normal. The proficiency of the ensemble models is assessed using the metrics- prediction accuracy, class-wise accuracy, precision, recall and f1-score. The ensemble model – MobileNet with CNN yields the best results with an average accuracy of 95.34%.
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L. Cioffi, Drug Delivery Challenges and Novel Therapeutic Approaches for Retinal Diseases. Springer, 2020.
X. Luo, J. Li, M. Chen, X. Yang, and X. Li, “Ophthalmic disease detection via deep learning with a novel mixture loss function,” IEEE J Biomed Health Inform, vol. 25, no. 9, pp. 3332–3339, 2021.
L. Dandona et al., “Is current eye-care-policy focus almost exclusively on cataract adequate to deal with blindness in India?,” The Lancet, vol. 351, no. 9112, pp. 1312–1316, 1998.
J. Besharse and D. Bok, The retina and its disorders. Academic Press, 2011.
M. Hoon, H. Okawa, L. Della Santina, and R. O. L. Wong, “Functional architecture of the retina: Development and disease,” Prog Retin Eye Res, vol. 42, pp. 44–84, 2014, doi: https://doi.org/10.1016/j.preteyeres.2014.06.003.
“Computed tomography (CT) exams | Health care use | OECD iLibrary.” Accessed: Nov. 23, 2023. [Online]. Available: https://www.oecd-ilibrary.org/social-issues-migration-health/computed-tomography-ct-exams/indicator/english_3c994537-en
“Magnetic resonance imaging (MRI) exams | Health care use | OECD iLibrary.” Accessed: Nov. 23, 2023. [Online]. Available: https://www.oecd-ilibrary.org/social-issues-migration-health/magnetic-resonance-imaging-mri-exams/indicator/english_1d89353f-en
H. E. Grossniklaus and W. R. Green, “Choroidal neovascularization,” Am J Ophthalmol, vol. 137, no. 3, pp. 496–503, 2004, doi: https://doi.org/10.1016/j.ajo.2003.09.042.
R. F. Spaide, “Choroidal Neovascularization,” in Pathologic Myopia, K. and Y. L. A. Spaide Richard F. and Ohno-Matsui, Ed., Cham: Springer International Publishing, 2021, pp. 271–293. doi: 10.1007/978-3-030-74334-5_19.
X. Zhang and S. Sivaprasad, “Drusen and pachydrusen: the definition, pathogenesis, and clinical significance,” Eye, vol. 35, no. 1, pp. 121–133, 2021, doi: 10.1038/s41433-020-01265-4.
K. N. Khan et al., “Differentiating drusen: Drusen and drusen-like appearances associated with ageing, age-related macular degeneration, inherited eye disease and other pathological processes,” Prog Retin Eye Res, vol. 53, pp. 70–106, 2016, doi: https://doi.org/10.1016/j.preteyeres.2016.04.008.
G. E. Lang, “Diabetic Macular Edema,” Ophthalmologica, vol. 227, no. Suppl. 1, pp. 21–29, Apr. 2012, doi: 10.1159/000337156.
E. J. Kim, W. V Lin, S. M. Rodriguez, A. Chen, A. Loya, and C. Y. Weng, “Treatment of Diabetic Macular Edema,” Curr Diab Rep, vol. 19, no. 9, p. 68, 2019, doi: 10.1007/s11892-019-1188-4.
S. Sharma, K. Guleria, S. Kumar, and S. Tiwari, “Deep Learning based Model for Detection of Vitiligo Skin Disease using Pre-trained Inception V3,” Article in International Journal of Mathematical Engineering and Management Sciences, vol. 8, no. 5, pp. 1024–1039, 2023, doi: 10.33889/IJMEMS.2023.8.5.059.
A. Panesar, Machine learning and AI for healthcare. Springer, 2019.
Kaur, K. Guleria, and N. K. Trivedi, “A Deep Learning Based Model for Rice Leaf Disease Detection,” in 2022 10th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions)(ICRITO), 2022, pp. 1–5.
F. Jiang et al., “Artificial intelligence in healthcare: past, present and future,” Stroke Vasc Neurol, vol. 2, no. 4, 2017.
S. Gulati, K. Guleria, and N. Goyal, “Classification and Detection of Coronary Heart Disease using Machine Learning,” in 2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), 2022, pp. 1728–1732. doi: 10.1109/ICACITE53722.2022.9823547.
S. Sharma and K. Guleria, “A comprehensive review on federated learning based models for healthcare applications,” Artif Intell Med, vol. 146, p. 102691, 2023, doi: https://doi.org/10.1016/j.artmed.2023.102691.
S. Gulati, K. Guleria, and N. Goyal, “Classification of Diabetic Retinopathy using pre-trained Deep Learning Model- DenseNet 121,” in 2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT), 2023, pp. 1–6. doi: 10.1109/ICCCNT56998.2023.10308181.
R. Sharma, V. Kukreja, R. K. Kaushal, A. Bansal, and A. Kaur, “Rice Leaf blight Disease detection using multi-classification deep learning model,” 2022 10th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions), ICRITO 2022, 2022, doi: 10.1109/ICRITO56286.2022.9964644.
S. Gulati, K. Guleria, and N. Goyal, “Classification and detection of diabetic eye diseases using deep learning: A review and comparative analysis,” AIP Conf Proc, vol. 2916, no. 1, p. 20005, Jan. 2023, doi: 10.1063/5.0177682.
S. Shamas, S. N. Panda, and I. Sharma, “Review on lung nodule segmentation-based lung cancer classification using machine learning approaches,” in Artificial Intelligence on Medical Data: Proceedings of International Symposium, ISCMM 2021, 2022, pp. 277–286.
D. Rastogi, P. Johri, and V. Tiwari, “Brain Tumor Detection and Localization: An Inception V3-Based Classification Followed By RESUNET-Based Segmentation Approach,” Article in International Journal of Mathematical Engineering and Management Sciences, vol. 8, no. 2, pp. 336–352, 2023, doi: 10.33889/IJMEMS.2023.8.2.020.
Singla, R. Kaur, J. Singh, N. Nisha, A. K. Singh, and T. Singh, “Fine Tuned Pre-Trained Deep Neural Network for Automatic Detection of Diabetic Retinopathy Using Fundus Images,” International Journal of Intelligent Systems and Applications in Engineering, vol. 11, no. 9s, pp. 735–742, 2023.
S. Sotoudeh-Paima, A. Jodeiri, F. Hajizadeh, and H. Soltanian-Zadeh, “Multi-scale convolutional neural network for automated AMD classification using retinal OCT images,” Comput Biol Med, vol. 144, p. 105368, 2022, doi: https://doi.org/10.1016/j.compbiomed.2022.105368
S. D. Z. D. J. Martin, Z. S. Wolfa, and R. Sznitmanb, “Evaluation of an Artificial Intelligence-Based Detector of Sub-and Intraretinal Fluid on a Large Set of Optical Coherence Tomography Volumes in Age-Related Macular Degeneration and Diabetic Macular Edema,” Ophthalmologica, vol. 245, pp. 516–527, 2022.
A. M. Alqudah, “AOCT-NET: a convolutional network automated classification of multiclass retinal diseases using spectral-domain optical coherence tomography images,” Med Biol Eng Comput, vol. 58, no. 1, pp. 41–53, 2020, doi: 10.1007/s11517-019-02066-y.
H. S. Sandhu et al., “Automated Diagnosis of Diabetic Retinopathy Using Clinical Biomarkers, Optical Coherence Tomography, and Optical Coherence Tomography Angiography,” Am J Ophthalmol, vol. 216, pp. 201–206, 2020, doi: https://doi.org/10.1016/j.ajo.2020.01.016.
Y. Rong et al., “Surrogate-assisted retinal OCT image classification based on convolutional neural networks,” IEEE J Biomed Health Inform, vol. 23, no. 1, pp. 253–263, 2018.
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