CATARACT CLASSIFICATION USING MOBILENETV2-BASED MODEL
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Published:
Sep 6, 2024
Keywords:
cataract classification, channelwise attention, mobileNetV2
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Abstract
Cataract occurs when the lens of the eyes, normally transparent, becomes cloudy. Clouded vision resulting from cataracts can pose challenges in activities such as reading, nighttime driving, and discerning facial expressions of acquaintances. Ensuring quality of vision now requires early detection of cataracts. This study aims to create a deep-learning classification system capable of distinguishing between healthy eyes and those affected by cataracts. To achieve this, modifications such as skip connections and channel-wise attention have been integrated into the pre-trained MobileNetV2 model to formulate the proposed model. Furthermore, augmentation technique and unsharp masking filter are implemented in the pre-processing dataset to augment the image count and improve image quality. The findings indicate that the model achieved an accuracy rate of 98,80% for ODIR-2019 in 2 categories: cataract and normal.
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1.
Nguyen K-D, Nguyen T-S, Sun M-T. CATARACT CLASSIFICATION USING MOBILENETV2-BASED MODEL. journal [Internet]. 6Sep.2024 [cited 18Oct.2024];14(3). Available from: https://journal.tvu.edu.vn/index.php/journal/article/view/4361
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References
[1] Gayathri S, Saran S, Kumar PS, Singh IP.
Cataract disease classification using convolutional
neural network architectures. In: 2023 Second
International Conference on Electronics and
Renewable Systems (ICEARS). 02nd – 04th March
2023; Tuticorin, India. IEEE; 2023. p.992–998.
http://dx.doi.org/10.1109/ICEARS56392.2023.10085502.
[2] Steinmetz JD, Bourne RR, Briant PS, Flaxman SR,
Taylor HR, Jonas JB, et al. Causes of blindness and
vision impairment in 2020 and trends over 30 years,
and prevalence of avoidable blindness in relation to
vision 2020: the right to sight: an analysis for the
global burden of disease study. The Lancet Global
Health. 2021;9(2): 144–160.
[3] Gao Z, Gorenflo M, Kaelber DC, Monnier VM,
Xu R. Drug repurposing for reducing the risk
of cataract extraction in patients with diabetes
mellitus: integration of artificial intelligencebased drug prediction and clinical corroboration.
Frontiers in Pharmacology. 2023;14: 1181711.
https://doi.org/10.3389/fphar.2023.1181711.
[4] Yadav S, Yadav JKPS. Automatic cataract severity detection and grading using deep learning. Journal of Sensors. 2023;2023(1): 2973836.
https://doi.org/10.1155/2023/2973836.
[5] Funde K, Joshi J, Castelino K, Patil P,
Mulla N. Cataract detection by leveraging
VGG-19 classification model on retinal
images. In: 13th International Conference on
Computing Communication and Networking
Technologies (ICCCNT). 03rd – 05th October
2022; Kharagpur, India. IEEE; 2022. p.1–6.
https://doi.org/10.1109/ICCCNT54827.2022.9984433.
[6] Fan W, Shen R, Zhang Q, Yang JJ, Li J.
Principal component analysis based cataract
grading and classification. In: 17th International
Conference on E-health Networking, Application
& Services (HealthCom). 4th – 17th October
2015; Boston, MA. IEEE; 2015. p.459–462.
https://doi.org/10.1109/HealthCom.2015.7454545.
[7] Dong Y, Wang Q, Zhang Q, Yang J. Classification of
cataract fundus image based on retinal vascular information. In: Smart Health: International Conference
(ICSH 2016). 24th – 25th December 2016. Springer;
2017. p.166–173. https://doi.org/10.1007/978-3-319-
59858-1_16.
[8] Kaur G, Sharma N, Chauhan R, Kukreti S,
Gupta R. Eye disease classification using
resnet-18 deep learning architecture. In: 2nd
International Conference on Futuristic Technologies
(INCOFT). 24th – 26th November 2023;
Belagavi, Karnataka, India. IEEE; 2023. p.1–5.
https://doi.org/10.1109/INCOFT60753.2023.10425690.
[9] Zhang L, Li J, Zhang, Han H, Liu B, Yang
J, Wang Q. Automatic cataract detection and
grading using deep convolutional neural network.
In: 2017 IEEE 14th International Conference on
Networking, Sensing and Control (ICNSC). 03rd
August 2017; Calabria. IEEE; 2017. p.60–65.
https://doi.org/10.1109/ICNSC.2017.8000068.
[10] Singh G, Guleria K, Sharma S. A pre-trained vgg16
model for cataract eye disease prediction. In: 3rd
International Conference on Smart Generation
Computing, Communication and Networking
(SMART GENCON). 29th – 31st December
2023; Bangalore, India. IEEE; 2023. p.1–6.
https://doi.org/10.1109/SMARTGENCON60755.2023.10442647.
[11] Yang S, Xiao W, Zhang M, Guo S, Zhao J,
Shen F. Image data augmentation for deep
learning: A survey. arXiv [Preprint] 2022.
https://doi.org/10.48550/arXiv.2204.08610.
[12] Awaluddin BA, Chao CT, Chiou JS. Investigating
effective geometric transformation for image
augmentation to improve static hand gestures
with a pre-trained convolutional neural
network. Mathematics. 2023;11(23): 4783.
https://doi.org/10.3390/math11234783.
[13] Maharana K, Mondal S, Nemade B. A review: Data
pre-processing and data augmentation techniques.
Global Transitions Proceedings. 2022;3(1): 91–99.
[14] Kwok N, Shi H. Design of unsharp masking filter kernel and gain using particle swarm optimization. In: 7th International Congress on Image and Signal Processing. 14th – 16th October 2014; Dalian, China. IEEE; 2014. p.217–222.
https://doi.org/10.1109/CISP.2014.7003780.
[15] Song Y, Li C, Xiao S, Xiao H, Guo B. Unsharp masking image enhancement the parallel algorithm based
on cross-platform. Scientific Reports. 2022;12(1):
20175. https://doi.org/10.1038/s41598-022-21745-9.
[16] Venkateswarlu IB, Kakarla J, Prakash S. Face mask
detection using MobileNet and global pooling block.
In: IEEE 4th Conference on Information Communication Technology (CICT). 03rd – 05th December 2020; Chennai, India. IEEE; 2020. p.1–5.
https://doi.org/10.1109/CICT51604.2020.9312083.
[17] Khan A, Sohail A, Zahoora U, Qureshi AS. A survey
of the recent architectures of deep convolutional neural networks. Artificial intelligence review. 2020;53:
5455–5516.
[18] Shafiq M, Gu Z. Deep residual learning for image
recognition: A survey. Applied Sciences. 2022;12(18):
8972. https://doi.org/10.3390/app12188972.
[19] Hu J, Shen L, Albanie S, Sun G , Wu E. Squeezeand-excitation networks. In: Proceedings of the IEEE
conference on computer vision and pattern recognition. 18th – 23rd June 2018; Salt Lake City, UT, USA.
IEEE; 2018. p.7132–7141.
[20] Howard AG, Zhu M, Chen B, Kalenichenko D,
Wang W, Weyand T, et al. Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv [Preprint] 2017.
https://doi.org/10.48550/arXiv.1704.04861.
[21] [21] Ubaidah IDWS, Fu’Adah Y, Sa’Idah S, Magdalena R, Wiratama AB, Simanjuntak RBJ. Classification of glaucoma in fundus images using convolutional neural network with mobilenet architecture. In:
1st International Conference on Information System
Information Technology (ICISIT). 27th – 28th July
2022; Yogyakarta, Indonesia. IEEE; 2022. p.198–203.
https://doi.org/10.1109/ICISIT54091.2022.9872945.
[22] Ioffe S, Szegedy C. Batch normalization: Accelerating deep network training by reducing internal
covariate shift. In: ICML15: Proceedings of the 32nd
International Conference on International Conference
on Machine Learning. 06th – 11th July 2015; Lille,
France. ICML; 2015. p.448–456.
[23] Sandler M, Howard A, Zhu M, Zhmoginov A, Chen
LC. Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on
computer vision and pattern recognition. 18th – 22nd
June 2018; Salt Lake City, Utah, USA. IEEE; 2018.
p.4510–4520.
[24] National Institute of Health Data Science at Peking
University (NIHDS-PKU). ODIR-2019 Challenge.
https://odir2019.grand-challenge.org/dataset/
[Accessed 1st May 2024].
[25] Larxel. Ocular disease recognition.
https://www.kaggle.com/datasets/andrewmvd/oculardisease-recognition-odir5k [Accessed 2nd May
2024]
[26] Mahmood SS, Chaabouni S, Fakhfakh A. A new
technique for cataract eye disease diagnosis in deep
learning. Periodicals of Engineering and Natural Sciences. 2023;11(6): 14–26.
Cataract disease classification using convolutional
neural network architectures. In: 2023 Second
International Conference on Electronics and
Renewable Systems (ICEARS). 02nd – 04th March
2023; Tuticorin, India. IEEE; 2023. p.992–998.
http://dx.doi.org/10.1109/ICEARS56392.2023.10085502.
[2] Steinmetz JD, Bourne RR, Briant PS, Flaxman SR,
Taylor HR, Jonas JB, et al. Causes of blindness and
vision impairment in 2020 and trends over 30 years,
and prevalence of avoidable blindness in relation to
vision 2020: the right to sight: an analysis for the
global burden of disease study. The Lancet Global
Health. 2021;9(2): 144–160.
[3] Gao Z, Gorenflo M, Kaelber DC, Monnier VM,
Xu R. Drug repurposing for reducing the risk
of cataract extraction in patients with diabetes
mellitus: integration of artificial intelligencebased drug prediction and clinical corroboration.
Frontiers in Pharmacology. 2023;14: 1181711.
https://doi.org/10.3389/fphar.2023.1181711.
[4] Yadav S, Yadav JKPS. Automatic cataract severity detection and grading using deep learning. Journal of Sensors. 2023;2023(1): 2973836.
https://doi.org/10.1155/2023/2973836.
[5] Funde K, Joshi J, Castelino K, Patil P,
Mulla N. Cataract detection by leveraging
VGG-19 classification model on retinal
images. In: 13th International Conference on
Computing Communication and Networking
Technologies (ICCCNT). 03rd – 05th October
2022; Kharagpur, India. IEEE; 2022. p.1–6.
https://doi.org/10.1109/ICCCNT54827.2022.9984433.
[6] Fan W, Shen R, Zhang Q, Yang JJ, Li J.
Principal component analysis based cataract
grading and classification. In: 17th International
Conference on E-health Networking, Application
& Services (HealthCom). 4th – 17th October
2015; Boston, MA. IEEE; 2015. p.459–462.
https://doi.org/10.1109/HealthCom.2015.7454545.
[7] Dong Y, Wang Q, Zhang Q, Yang J. Classification of
cataract fundus image based on retinal vascular information. In: Smart Health: International Conference
(ICSH 2016). 24th – 25th December 2016. Springer;
2017. p.166–173. https://doi.org/10.1007/978-3-319-
59858-1_16.
[8] Kaur G, Sharma N, Chauhan R, Kukreti S,
Gupta R. Eye disease classification using
resnet-18 deep learning architecture. In: 2nd
International Conference on Futuristic Technologies
(INCOFT). 24th – 26th November 2023;
Belagavi, Karnataka, India. IEEE; 2023. p.1–5.
https://doi.org/10.1109/INCOFT60753.2023.10425690.
[9] Zhang L, Li J, Zhang, Han H, Liu B, Yang
J, Wang Q. Automatic cataract detection and
grading using deep convolutional neural network.
In: 2017 IEEE 14th International Conference on
Networking, Sensing and Control (ICNSC). 03rd
August 2017; Calabria. IEEE; 2017. p.60–65.
https://doi.org/10.1109/ICNSC.2017.8000068.
[10] Singh G, Guleria K, Sharma S. A pre-trained vgg16
model for cataract eye disease prediction. In: 3rd
International Conference on Smart Generation
Computing, Communication and Networking
(SMART GENCON). 29th – 31st December
2023; Bangalore, India. IEEE; 2023. p.1–6.
https://doi.org/10.1109/SMARTGENCON60755.2023.10442647.
[11] Yang S, Xiao W, Zhang M, Guo S, Zhao J,
Shen F. Image data augmentation for deep
learning: A survey. arXiv [Preprint] 2022.
https://doi.org/10.48550/arXiv.2204.08610.
[12] Awaluddin BA, Chao CT, Chiou JS. Investigating
effective geometric transformation for image
augmentation to improve static hand gestures
with a pre-trained convolutional neural
network. Mathematics. 2023;11(23): 4783.
https://doi.org/10.3390/math11234783.
[13] Maharana K, Mondal S, Nemade B. A review: Data
pre-processing and data augmentation techniques.
Global Transitions Proceedings. 2022;3(1): 91–99.
[14] Kwok N, Shi H. Design of unsharp masking filter kernel and gain using particle swarm optimization. In: 7th International Congress on Image and Signal Processing. 14th – 16th October 2014; Dalian, China. IEEE; 2014. p.217–222.
https://doi.org/10.1109/CISP.2014.7003780.
[15] Song Y, Li C, Xiao S, Xiao H, Guo B. Unsharp masking image enhancement the parallel algorithm based
on cross-platform. Scientific Reports. 2022;12(1):
20175. https://doi.org/10.1038/s41598-022-21745-9.
[16] Venkateswarlu IB, Kakarla J, Prakash S. Face mask
detection using MobileNet and global pooling block.
In: IEEE 4th Conference on Information Communication Technology (CICT). 03rd – 05th December 2020; Chennai, India. IEEE; 2020. p.1–5.
https://doi.org/10.1109/CICT51604.2020.9312083.
[17] Khan A, Sohail A, Zahoora U, Qureshi AS. A survey
of the recent architectures of deep convolutional neural networks. Artificial intelligence review. 2020;53:
5455–5516.
[18] Shafiq M, Gu Z. Deep residual learning for image
recognition: A survey. Applied Sciences. 2022;12(18):
8972. https://doi.org/10.3390/app12188972.
[19] Hu J, Shen L, Albanie S, Sun G , Wu E. Squeezeand-excitation networks. In: Proceedings of the IEEE
conference on computer vision and pattern recognition. 18th – 23rd June 2018; Salt Lake City, UT, USA.
IEEE; 2018. p.7132–7141.
[20] Howard AG, Zhu M, Chen B, Kalenichenko D,
Wang W, Weyand T, et al. Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv [Preprint] 2017.
https://doi.org/10.48550/arXiv.1704.04861.
[21] [21] Ubaidah IDWS, Fu’Adah Y, Sa’Idah S, Magdalena R, Wiratama AB, Simanjuntak RBJ. Classification of glaucoma in fundus images using convolutional neural network with mobilenet architecture. In:
1st International Conference on Information System
Information Technology (ICISIT). 27th – 28th July
2022; Yogyakarta, Indonesia. IEEE; 2022. p.198–203.
https://doi.org/10.1109/ICISIT54091.2022.9872945.
[22] Ioffe S, Szegedy C. Batch normalization: Accelerating deep network training by reducing internal
covariate shift. In: ICML15: Proceedings of the 32nd
International Conference on International Conference
on Machine Learning. 06th – 11th July 2015; Lille,
France. ICML; 2015. p.448–456.
[23] Sandler M, Howard A, Zhu M, Zhmoginov A, Chen
LC. Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on
computer vision and pattern recognition. 18th – 22nd
June 2018; Salt Lake City, Utah, USA. IEEE; 2018.
p.4510–4520.
[24] National Institute of Health Data Science at Peking
University (NIHDS-PKU). ODIR-2019 Challenge.
https://odir2019.grand-challenge.org/dataset/
[Accessed 1st May 2024].
[25] Larxel. Ocular disease recognition.
https://www.kaggle.com/datasets/andrewmvd/oculardisease-recognition-odir5k [Accessed 2nd May
2024]
[26] Mahmood SS, Chaabouni S, Fakhfakh A. A new
technique for cataract eye disease diagnosis in deep
learning. Periodicals of Engineering and Natural Sciences. 2023;11(6): 14–26.