Comput Methods Programs Biomed - An automated decision-support system for non-proliferative diabetic retinopathy disease based on MAs and HAs detection.

Tópicos

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Resumo

Diabetic retinopathy (DR) has become a serious threat in our society, which causes 45% of the legal blindness in diabetes patients. Early detection as well as the periodic screening of DR helps in reducing the progress of this disease and in preventing the subsequent loss of visual capability. This paper provides an automated diagnosis system for DR integrated with a user-friendly interface. The grading of the severity level of DR is based on detecting and analyzing the early clinical signs associated with the disease, such as microaneurysms (MAs) and hemorrhages (HAs). The system extracts some retinal features, such as optic disc, fovea, and retinal tissue for easier segmentation of dark spot lesions in the fundus images. That is followed by the classification of the correctly segmented spots into MAs and HAs. Based on the number and location of MAs and HAs, the system quantifies the severity level of DR. A database of 98 color images is used in order to evaluate the performance of the developed system. From the experimental results, it is found that the proposed system achieves 84.31% and 87.53% values in terms of sensitivity for the detection of MAs and HAs respectively. In terms of specificity, the system achieves 93.63% and 95.08% values for the detection of MAs and HAs respectively. Also, the proposed system achieves 68.98% and 74.91% values in terms of kappa coefficient for the detection of MAs and HAs respectively. Moreover, the system yields sensitivity and specificity values of 89.47% and 95.65% for the classification of DR versus normal.

Resumo Limpo

diabet retinopathi dr becom serious threat societi caus legal blind diabet patient earli detect well period screen dr help reduc progress diseas prevent subsequ loss visual capabl paper provid autom diagnosi system dr integr userfriend interfac grade sever level dr base detect analyz earli clinic sign associ diseas microaneurysm mas hemorrhag system extract retin featur optic disc fovea retin tissu easier segment dark spot lesion fundus imag follow classif correct segment spot mas base number locat mas system quantifi sever level dr databas color imag use order evalu perform develop system experiment result found propos system achiev valu term sensit detect mas respect term specif system achiev valu detect mas respect also propos system achiev valu term kappa coeffici detect mas respect moreov system yield sensit specif valu classif dr versus normal

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