GlaucoVIZ: Assisting System for Early Glaucoma Detection Using Mask R-CNN

Palakvangsa-Na-Ayudhya, Srisupa; Sapthamrong, Thanan; Sunthornwutthikrai, Kritsada; Sakiyalak, Darin

doi:10.1109/ecti-con49241.2020.9158128

Cited by 9 publications

(2 citation statements)

References 7 publications

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“…They got .822 and .882 values for the ORIGA and SCES data set respectively. In paper [22] Palakvangsa-Na-Ayudhya et al proposed an automated system using Mask Regional -Convolutional Neural Network [32]. It is an advancement of Faster R-CNN by joining a branch for predicting segmented masks on each ROI along with the existing branch for classify an object and bounding box regression.…”

Section: Problem Statementmentioning

confidence: 99%

Glaucoma Detection Using Inception Convolutional Neural Network V3

Afroze

Akther

Chowdhury

et al. 2021

Communications in Computer and Information Science

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Glaucoma detection is an important research area in intelligent system and it plays an important role to medical field. Glaucoma can give rise to an irreversible blindness due to lack of proper diagnosis. Doctors need to perform many tests to diagnosis this threatening disease. It requires a lot of time and expense. Sometime affected people may not have any vision loss, at the early stage of glaucoma. For detecting glaucoma, we have built a model to lessen the time and cost. Our work introduces a CNN based Inception V3 model. We used total 6072 images. Among this image 2336 were glaucomatous and 3736 were normal fundus image. For training our model we took 5460 images and for testing we took 612 images. After that we obtained an accuracy of 0.8529 and a value of 0.9387 for AUC. For comparison, we used DenseNet121 and ResNet50 algorithm and got an accuracy of 0.8153 and 0.7761 respectively.

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Section: Problem Statementmentioning

confidence: 99%

Glaucoma Detection Using Inception Convolutional Neural Network V3

Afroze

Akther

Chowdhury

et al. 2021

Communications in Computer and Information Science

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“…This proposed approach adopts real-time modified Glaucoma disease dataset with the presence of multiple OCT image patterns with the association of several classes and the respective labels bind to the classes [ 11 , 12 ]. Every class label indicates different types of Glaucoma disease combination and the Glaucoma constraint category can easily be identified with proper prediction principles.…”

Section: Introductionmentioning

confidence: 99%

Energetic Glaucoma Segmentation and Classification Strategies Using Depth Optimized Machine Learning Strategies

Jesi

Aslam

Ramkumar

et al. 2021

Contrast Media & Molecular Imaging

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Glaucoma is a major threatening cause, in which it affects the optical nerve to lead to a permanent blindness to individuals. The major causes of Glaucoma are high pressure to eyes, family history, irregular sleeping habits, and so on. These kinds of causes lead to Glaucoma easily, and the effect of such disease leads to heavy damage to the internal optic nervous system and the affected person will get permanent blindness within few months. The major problem with this disease is that it is incurable; however, the affection stages can be reduced and the same level of effect as that for the long period can be maintained but this is possible only in the earlier stages of identification. This Glaucoma causes structural effect to the eye ball and it is complex to estimate the cause during regular diagnosis. In medical terms, the Cup to Disc Ratio (CDR) is minimized to the Glaucoma patients suddenly and leads to harmful damage to one’s eye in severe manner. The general way to identify the Glaucoma is to take Optical Coherence Tomography (OCT) test, in which it captures the uncovered portion of eye ball (backside) and it is an efficient way to visualize diverse portions of eyes with optical nerve visibility shown clearly. The OCT images are mainly used to identify the diseases like Glaucoma with proper and robust accuracy levels. In this work, a new methodology is introduced to identify the Glaucoma in earlier stages, called Depth Optimized Machine Learning Strategy (DOMLS), in which it adapts the new optimization logic called Modified K-Means Optimization Logic (MkMOL) to provide best accuracy in results, and the proposed approach assures the accuracy level of more than 96.2% with least error rate of 0.002%. This paper focuses on the identification of early stage of Glaucoma and provides an efficient solution to people in case of effect by such disease using OCT images. The exact position pointed out is handled by using Region of Interest- (ROI-) based optical region selection, in which it is easy to point the optical cup (OC) and optical disc (OD). The proposed algorithm of DOMLS proves the accuracy levels in estimation of Glaucoma and the practical proofs are shown in the Result and Discussions section in a clear manner.

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