Deep Classifier for Conjunctivitis – A Three-Fold Binary Approach

PDF (431KB), PP.46-54

Views: 0 Downloads: 0

Author(s)

Subhash Mondal 1,* Suharta Banerjee 1 Subinoy Mukherjee 1 Ankur Ganguly 1 Diganta Sengupta 1

1. Meghnad Saha Institute of Technology, Kolkata, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijmsc.2022.02.05

Received: 4 Oct. 2021 / Revised: 2 Dec. 2021 / Accepted: 9 Jan. 2022 / Published: 8 Jun. 2022

Index Terms

Ocular Diseases, Deep Learning, Deep Classifiers, VGG19, Resnet50, Inception V3, Conjunctivitis Classification.

Abstract

Alterations in environmental and demographic equations have resulted in phenomenal rise of human centric diseases, ocular being one of them. Technological advancements have witnessed early diagnosis of much of the previously un-ciphered diseases. This paper addresses two research questions (RQs) with the study being focused on conjunctivitis (the most prevalent eye ailment in adults as well as minors). The motive of both the RQs rests in implementing three state-of-the art deep learning framework for classification of the ocular disease and validation of the frameworks. Validation of the frameworks is seconded by improvised proposals for enhancements. RQ1 establishes and validates whether the three off the shelf Deep Learning frameworks VGG19, ResNet50, and Inception V3 properly classify the disease or not. RQ2 analyses the effectiveness of each classifier with further enhancement proposals. The algorithms were implemented on 210 images and generated an accuracy of 87.3%, 93.6%, and 95.2% for VGG19, ResNet50, and Inception V3 using Adam optimizer, with slightly variant results when applying Adadelta optimizer. These results were typical of the classification frameworks with enhancements. With pervasive penetration of Artificial Intelligence in healthcare, this paper presents the efficacy of Deep Learning Frameworks in conjunctivitis classification.

Cite This Paper

Subhash Mondal, Suharta Banerjee, Subinoy Mukherjee, Ankur Ganguly, Diganta Sengupta," Deep Classifier for Conjunctivitis – A Three-Fold Binary Approach ", International Journal of Mathematical Sciences and Computing(IJMSC), Vol.8, No.2, pp. 46-54, 2022. DOI: 10.5815/ijmsc.2022.02.05

Reference

[1]G An et al., "Comparison of machine-learning classification models for glaucoma management.," in 2018 J. Healthc. Eng. , 2018.

[2]K Saito and R Nakano, "Medical diagnostic expert system based on PDP model," in IEEE International Conference on Neural Networks, San Diego, CA, USA, 24–27 July, pp. 255–262.

[3]M.L Huang and H.Y Chen, "Development and comparison of automated classifiers for glaucoma diagnosis," in 2005 Investig. Ophthalmol. Vis. Sci. , 2005.

[4]Poplin R, Varadarajan AV, and Blumer K, "Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning," in Nat Biomed Eng 2018, 2018, pp. 2:158-64.

[5]Ting DSW, Peng L, and Varadarajan AV, "Deep learning in ophthalmology : The technical and clinical considerations.," in Prog Retin Eye Res 2019, 2019.

[6]Doreen L Teoh, Sally Md, and Md Reynolds, "Diagnosis and management of paediatric conjunctivitis," in Paediatric Emergency Care, vol. 19, 2003, pp. 48-55.

[7](2020, Jun) Pink eye (conjunctivitis). [Online]. https://www.mayoclinic.org/diseases-conditions/pink-eye/symptoms-causes/syc-20376355

[8]Md R Khan and A S M Shafi, "Statistical Texture Features Based Automatic Detection and Classification of Diabetic Retinopathy," I.J. Image, Graphics and Signal Processing, no. 2, pp. 53-61, April 2021.

[9]L Dharmanna, S Chandrappa, T C Manjunath, and G Pavithra, "A Novel Approach for Diagnosis of Glaucoma through Optic Nerve Head (ONH) Analysis using Fractal Dimension Technique," I.J. Modern Education and Computer Science, no. 1, pp. 55-61, Jan 2016.

[10]N N Rather, C O Patel, and S A Khan, "Using Deep Learning Towards Biomedical Knowledge Disovery," I. J. Mathematical Sciences and Computing, no. 2, pp. 1-10, April 2017.

[11]Rahul Kapoor, Benjamin T Whigham, and Lama A, "Artificial Intelligence and Optical Coherence," Asia-Pacific Academy of Ophthalmology, vol. 8, Feb 2019.

[12]Paisan Ruamviboonsuk, Carol Y Cheung, Xiulan Zhang, Rajiv Raman, and Sang Jun Park, "Artificial Intelligence in Ophthalmology: Evolutions in Asia," Asia-Pacific Journal of Ophthalmology, vol. 9, 2020.

[13]Lua Ngo, Jaepyeong Cha, and Jae-Ho Han, "Deep Neural Network Regression for Automated Retinal Layer Segmentation in Optical Coherence Tomography Images," IEEE Transactions on Image Processing, vol. 29, pp. 303 - 312, Aug 2019.

[14]Bashir Isa Dodo, Yongmin Li, Djibril Kaba, and Xiaohui Liu, "Retinal Layer Segmentation in Optical Coherence Tomography Images," Data-Enabled Intelligence for Digital Health, vol. 7, pp. 152388 - 152398, 2019.

[15]Darren Ting, Valencia HX Foo, Lily Yang, and Josh Sia, "Artificial intelligence for anterior segment diseases: Emerging applications in ophthalmology," British Journal of Ophthalmology, Jun 2020.

[16]H Masumoto and H Tabuchi, "Objective evaluation of allergic conjunctival disease (with a focus on the application of artificial intelligence technology),".

[17]N Kanwal and S Malik, "Data Driven Approach for Eye Disease Classification with Machine Learning," Computing and Artifical Intelligence, 2019.

[18]W Xiaohang et al., "Application of artificial intelligence in anterior segment ophthalmic diseases: diversity and standardization," vol. 8, no. 11, 2020.

[19]S Sundararajan and P Saravana, "Detection of Conjunctivitis with Deep Learning Algorithm in Medical Image Processing," in 2019 Third International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), Palladam, India , 2019.

[20]G Melih, G Evgin, and D Taner, "Automated Detection of Adenoviral Conjunctivitis Disease from Facial Images using Machine Learning," in 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA), Miami, FL, USA, 2015.

[21]T Joydeep, J Aishwarya, V. D Aaishwarya, B Anupama, and K.M Dutta, "An image processing based method to identify and grade conjunctivitis infected eye according to its types and intensity," in 2015 Eighth International Conference on Contemporary Computing (IC3), Noida, India , 2015.

[22]K Simonyan and A Zisserman, "Very Deep Convolutional Networks for Large-Scale Image Recognition," in International Conference on Learning Representations 2015, 2015.

[23]k He, X Zhang, S Ren, and J Sun, "Deep Residual Learning for Image Recognition," , Las Vegas, NV, USA, 2016.

[24]C Szegedy, V Vanhoucke, S Ioffe, J Shlens, and Z Wojna, "Rethinking the Inception Architecture for Computer Vision," in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.

[25](2020, Oct) Kaggle. [Online]. https://www.kaggle.com/dangern00dle/eye-diseases-virus-dateset-vol-1 

[26](2021, Jan) DBSCAN Clustering Algorithm in Machine Learning. [Online]. https://www.kdnuggets.com/2020/04/dbscan-clustering-algorithm-machine-learning.html 

[27]AUC-ROC Curve in Machine Learning. [Online]. https://www.analyticsvidhya.com/blog/2020/06/auc-roc-curve-machine-learning/