Description

Glaucoma Detection using Deep Learning

In this project we have implemented Glaucoma detection system for medical applications. This system was implemented to detect the various stages of glaucoma .The proposed DL architecture contains six learned layers: four convolutional layers and two fully-connected layers. Dropout and data augmentation strategies are adopted to further boost the performance of glaucoma diagnosis.We have used deep neural networks for training .The proposed algorithm works well and compared with other algorithms in terms of accuracy.

Block Diagram

           

Tool used :  Matlab 2018 b

Toolbox required : Deep Learning Toolbox

 Demo Video

Conclusion

Glaucoma is one of the leading causes of blindness. A large number of populations across the globe is affected by it. The development of CAD tool will assist the ophthalmologists in detecting glaucoma even at mild stage. Our method obtained the highest accuracy, sensitivity, and specificity of 98.13 %, 98%, and 98.30% respectively for the maximum number of images. The developed CNN model can efficiently detect the class (normal or glaucoma) of unknown image. The main advantage of CNN is, it will take entire image instead of defected part and filtered outputs are pooled together which avoids the sophisticated design of hand-crafted features. Thus, it avoids the segmentations and provides the powerful features to describe normal and glaucoma subjects. In future, our developed model can be used to detect the glaucoma at an early stage and help in advising early treatment for patients. 

 

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