TY - GEN
T1 - Detection and Classification of Hard Exudates with Fundus Images Complements and Neural Networks
AU - Hussain, Muhammad Altaf
AU - Islam, Syed Osama Bin
AU - Tiwana, M. I.
AU - Ubaid-Ur-Rehman,
AU - Qureshi, W. S.
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/4
Y1 - 2019/4
N2 - Diabetic Retinopathy (DR) is an eye disorder that progressively leads to vision loss due to high glucose causing impairment of retinal blood vessels (BVs). 'Retinal Bright Lesions' such as 'Hard Exudates' (HEs) are plasma leakages from rapture retinal capillaries. HEs appear as hard, waxy, yellowish deposits from tiny spots to fat patches and signify moderate-severe Non-Proliferative Diabetic Retinopathy (NPDR). This paper proposes a simple, compact and computationally inexpensive technique for detection and classification of HEs using Digital Image Processing Techniques on digital fundus images complements and Artificial Neural Networks (ANN). The proposed technique unfolds through five stages i.e. Pre-processing, coarse detection, optimization, features detection extraction followed by classification. 'Speed Up Robust Features' (SURF) algorithm has been used for features detection extraction while 'Feed-Forward Back-propagation' (FFBP) ANN has been used for classification. The proposed technique has yielded 98.7% 'Sensitivity' (SE), 97.5% 'Specificity' (SP) and 97.7% 'Accuracy' (AC) on 'DIARETDB1' fundus images.
AB - Diabetic Retinopathy (DR) is an eye disorder that progressively leads to vision loss due to high glucose causing impairment of retinal blood vessels (BVs). 'Retinal Bright Lesions' such as 'Hard Exudates' (HEs) are plasma leakages from rapture retinal capillaries. HEs appear as hard, waxy, yellowish deposits from tiny spots to fat patches and signify moderate-severe Non-Proliferative Diabetic Retinopathy (NPDR). This paper proposes a simple, compact and computationally inexpensive technique for detection and classification of HEs using Digital Image Processing Techniques on digital fundus images complements and Artificial Neural Networks (ANN). The proposed technique unfolds through five stages i.e. Pre-processing, coarse detection, optimization, features detection extraction followed by classification. 'Speed Up Robust Features' (SURF) algorithm has been used for features detection extraction while 'Feed-Forward Back-propagation' (FFBP) ANN has been used for classification. The proposed technique has yielded 98.7% 'Sensitivity' (SE), 97.5% 'Specificity' (SP) and 97.7% 'Accuracy' (AC) on 'DIARETDB1' fundus images.
KW - artificial neural networks
KW - back-propagation
KW - confusion matrix
KW - diabetic retinopathy
KW - green channel
KW - hard exudates
KW - image complements
KW - optimization
KW - thresholding
UR - http://www.scopus.com/inward/record.url?scp=85071098638&partnerID=8YFLogxK
U2 - 10.1109/ICCAR.2019.8813469
DO - 10.1109/ICCAR.2019.8813469
M3 - Conference contribution
AN - SCOPUS:85071098638
T3 - 2019 5th International Conference on Control, Automation and Robotics, ICCAR 2019
SP - 206
EP - 211
BT - 2019 5th International Conference on Control, Automation and Robotics, ICCAR 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th International Conference on Control, Automation and Robotics, ICCAR 2019
Y2 - 19 April 2019 through 22 April 2019
ER -