• Anjali Khandelwal

The healthcare industry is completely different from other industries. It is a high-priority department where people expect the highest levels of care and service, regardless of cost. Even if it consumes a lot of budget, it does not meet social expectations. Most medical data is interpreted by medical experts. In the image interpretation of human experts, it is very limited due to its subjectivity, complexity of images, wide differences between different interpreters and fatigue. After deep learning in other practical applications, it also provides an exciting solution with good medical imaging accuracy and is considered a key method for future health sector applications. In this chapter, we discuss the most advanced deep learning architecture and its optimization for medical image segmentation and classification. In the previous section, we discussed the challenges of medical imaging and open research based on deep learning. Automated detection of diabetic retinopathy is critical because it is the leading cause of irreversible vision loss in working-age populations in developed countries. The early detection of the occurrence of diabetic retinopathy is very helpful for clinical treatment; although several different feature extraction methods have been proposed, even for those trained clinicians, the classification task of retinal images is still tedious. Recently, deep convolutional neural networks have shown superior performance in image classification compared to previous feature-based image classification methods based on handcrafting. Therefore, in this study, we explored the use of deep convolutional neural network methods to automatically classify diabetic retinopathy using color fundus images to obtain high precision in our datasets, superior to those obtained using classical methods.

Orthogonal frequency division multiplexing (OFDM), Peak-to-average power ratio (PAPR), constant modulus algorithm (CMA).Complementary cumulative distribution function (CCDF).

"Design and Development of Deep Learning Based Fundus Image Diabetic Retinopathy ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 12, page no.36-39, December-2018, Available :http://www.jetir.org/papers/JETIR1812506.pdf

"Design and Development of Deep Learning Based Fundus Image Diabetic Retinopathy ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 12, page no. pp36-39, December-2018, Available at : http://www.jetir.org/papers/JETIR1812506.pdf