TY - GEN
T1 - Fundus Image Classification
T2 - 2022 International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2022
AU - Appati, Justice Kwame
AU - Armah, Beatrice
AU - Owusu, Ebenezer
AU - Soli, Michael Agbo Tettey
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Lately, many diabetic patients are experiencing diabetic retinopathy resulting in a loss of their sight. Even though the urgency and threat posed by this condition, there is insufficient data source to engage appropriate computational intelligence tools. The few that exist happen to be imbalanced. Leveraging on this imbalanced dataset, several activities have been carried out to propose improved detection and classification descriptors. Although some works have been done in this domain, the issue of accuracy still persists in the administration of an effective diagnosis. This paper harnessed the benefits of Gabor filters and the multi-resolution property of Discrete Wavelet Transforms (DWTs) to construct appropriate fundus feature descriptors. These discriminant features are fed into some selected but predominant classical machine learning classifiers. Numerical evaluation of the study gave a perfect (100%) average score for the fundus image classification using Gradient Boosting and Logistic Regression classifiers over Accuracy, F1-score, Precision and Recall evaluation metric. The tie in performance is further broken using their computation time, suggesting that Logistic Regression is more appropriate with 9min 32sec over Gradient Boosting or 1hr 10min 32sec.
AB - Lately, many diabetic patients are experiencing diabetic retinopathy resulting in a loss of their sight. Even though the urgency and threat posed by this condition, there is insufficient data source to engage appropriate computational intelligence tools. The few that exist happen to be imbalanced. Leveraging on this imbalanced dataset, several activities have been carried out to propose improved detection and classification descriptors. Although some works have been done in this domain, the issue of accuracy still persists in the administration of an effective diagnosis. This paper harnessed the benefits of Gabor filters and the multi-resolution property of Discrete Wavelet Transforms (DWTs) to construct appropriate fundus feature descriptors. These discriminant features are fed into some selected but predominant classical machine learning classifiers. Numerical evaluation of the study gave a perfect (100%) average score for the fundus image classification using Gradient Boosting and Logistic Regression classifiers over Accuracy, F1-score, Precision and Recall evaluation metric. The tie in performance is further broken using their computation time, suggesting that Logistic Regression is more appropriate with 9min 32sec over Gradient Boosting or 1hr 10min 32sec.
KW - Diabetic Retinopathy
KW - Discrete Wavelet Transform
KW - Gabor feature extraction
KW - Gradient Boosting
KW - Logistic Regression
UR - http://www.scopus.com/inward/record.url?scp=85154530990&partnerID=8YFLogxK
U2 - 10.1109/ASSIC55218.2022.10088415
DO - 10.1109/ASSIC55218.2022.10088415
M3 - Conference contribution
AN - SCOPUS:85154530990
T3 - ASSIC 2022 - Proceedings: International Conference on Advancements in Smart, Secure and Intelligent Computing
BT - ASSIC 2022 - Proceedings
A2 - Mohanty, Jnyana Ranjan
A2 - Tripathy, Hrudaya Kumar
A2 - Mishra, Sambit Kumar
A2 - Mishra, Sushruta
A2 - Sahoo, Kshira Sagar
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 19 November 2022 through 20 November 2022
ER -
