Detection of Diabetic Retinopathy With Mobile Application Using Deep Learning

High glucose level disrupts the structure of the retinal layer in the eyes and causes diabetic retinopathy which is characterized with new pathologic blood vessels in the eyes. Although diabetic retinopathy is not clear at the beginning of the disease, it is the most common problem in people who have diabetes and causes blindness or cloudy vision if it is not diagnosed at the beginning of the disease. For early diagnosis of diabetic retinopathy, regular fundus controls and examination of the edema in the vessels of the retina are made periodically by ophthalmologists. With in the scope of this study, it is made possible to provide the early diagnosis and the level of diabetic retinopathy by using deep learning, image processing methods, and convolutional neural networks of the retina. In order to provide ease and rapid of diagnosis of the diabetic retinopathy in daily life, the diagnosis protocol has been turned into a mobile application. With the mobile application, both the diagnosis and more regular results of the diabetic retinopathy can be obtained easily and practically.

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Deep Learning Solution for Diabetic Retinopathy Diagnosis based on Convolutional Neural Networks and Image Processing Algorithms

10.1109/ehb52898.2021.9657666 ◽

2021 ◽

Author(s):

Tomozei Cosmin Ion ◽

Nechita Elena ◽

Dorian Lazar

Keyword(s):

Neural Networks ◽

Image Processing ◽

Diabetic Retinopathy ◽

Deep Learning ◽

Convolutional Neural Networks ◽

Image Processing Algorithms ◽

Processing Algorithms

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Convolutional Neural Network for the Semantic Segmentation of Remote Sensing Images

Mobile Networks and Applications ◽

10.1007/s11036-020-01703-3 ◽

2021 ◽

Vol 26 (1) ◽

pp. 200-215

Author(s):

Muhammad Alam ◽

Jian-Feng Wang ◽

Cong Guangpei ◽

LV Yunrong ◽

Yuanfang Chen

Keyword(s):

Neural Network ◽

Remote Sensing ◽

Neural Networks ◽

Image Processing ◽

Deep Learning ◽

Semantic Segmentation ◽

Natural Scene ◽

Remote Sensing Images ◽

Advantages And Disadvantages ◽

Target Segmentation

AbstractIn recent years, the success of deep learning in natural scene image processing boosted its application in the analysis of remote sensing images. In this paper, we applied Convolutional Neural Networks (CNN) on the semantic segmentation of remote sensing images. We improve the Encoder- Decoder CNN structure SegNet with index pooling and U-net to make them suitable for multi-targets semantic segmentation of remote sensing images. The results show that these two models have their own advantages and disadvantages on the segmentation of different objects. In addition, we propose an integrated algorithm that integrates these two models. Experimental results show that the presented integrated algorithm can exploite the advantages of both the models for multi-target segmentation and achieve a better segmentation compared to these two models.

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Needs/Requirements and Design of Retinal Imaging and Image Processing Methods for Diabetic Retinopathy in the Indian Context

Ophthalmological Imaging and Applications ◽

10.1201/b17026-11 ◽

2014 ◽

pp. 146-153

Keyword(s):

Image Processing ◽

Diabetic Retinopathy ◽

Retinal Imaging ◽

Processing Methods ◽

Indian Context

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Image classification using Deep learning

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i2.7.10892 ◽

2018 ◽

Vol 7 (2.7) ◽

pp. 614 ◽

Cited By ~ 5

Author(s):

M Manoj krishna ◽

M Neelima ◽

M Harshali ◽

M Venu Gopala Rao

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Image Processing ◽

Computer Vision ◽

Deep Learning ◽

Image Classification ◽

Convolutional Neural Networks ◽

Classical Problem

The image classification is a classical problem of image processing, computer vision and machine learning fields. In this paper we study the image classification using deep learning. We use AlexNet architecture with convolutional neural networks for this purpose. Four test images are selected from the ImageNet database for the classification purpose. We cropped the images for various portion areas and conducted experiments. The results show the effectiveness of deep learning based image classification using AlexNet.

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Bio-Medical Image Processing

Examining Fractal Image Processing and Analysis - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-7998-0066-8.ch007 ◽

2020 ◽

pp. 158-169

Author(s):

Rasmita Lenka ◽

Koustav Dutta ◽

Ashimananda Khandual ◽

Soumya Ranjan Nayak

Keyword(s):

Neural Network ◽

Neural Networks ◽

Image Processing ◽

Deep Learning ◽

Early Detection ◽

Digital Image Processing ◽

Medical Science ◽

Medical Image Processing ◽

The World ◽

Medical Reports

The chapter focuses on application of digital image processing and deep learning for analyzing the occurrence of malaria from the medical reports. This approach is helpful in quick identification of the disease from the preliminary tests which are carried out in a person affected by malaria. The combination of deep learning has made the process much advanced as the convolutional neural network is able to gain deeper insights from the medical images of the person. Since traditional methods are not able to detect malaria properly and quickly, by means of convolutional neural networks, the early detection of malaria has been possible, and thus, this process will open a new door in the world of medical science.

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Optimizing Convolution Neural Network on the TI C6678 multicore DSP

MATEC Web of Conferences ◽

10.1051/matecconf/201824603044 ◽

2018 ◽

Vol 246 ◽

pp. 03044 ◽

Cited By ~ 1

Author(s):

Guozhao Zeng ◽

Xiao Hu ◽

Yueyue Chen

Keyword(s):

Neural Network ◽

Neural Networks ◽

Image Processing ◽

Deep Learning ◽

Digital Signal Processor ◽

High Performance ◽

Digital Signal ◽

Automatic Translation ◽

Convolution Operation ◽

Multicore Dsp

Convolutional Neural Networks (CNNs) have become the most advanced algorithms for deep learning. They are widely used in image processing, object detection and automatic translation. As the demand for CNNs continues to increase, the platforms on which they are deployed continue to expand. As an excellent low-power, high-performance, embedded solution, Digital Signal Processor (DSP) is used frequently in many key areas. This paper attempts to deploy the CNN to Texas Instruments (TI)’s TMS320C6678 multi-core DSP and optimize the main operations (convolution) to accommodate the DSP structure. The efficiency of the improved convolution operation has increased by tens of times.

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Recognizing Ancient Characters from Tamil Palm Leaf Manuscripts using Convolution Based Deep Learning

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.c5842.098319 ◽

2019 ◽

Vol 8 (3) ◽

pp. 6873-6880

Keyword(s):

Neural Network ◽

Neural Networks ◽

Image Processing ◽

Pattern Recognition ◽

Deep Learning ◽

Convolutional Neural Networks ◽

Character Recognition ◽

Optical Character Recognition ◽

Convolutional Network ◽

Palm Leaf

Palm leaf manuscripts has been one of the ancient writing methods but the palm leaf manuscripts content requires to be inscribed in a new set of leaves. This study has provided a solution to save the contents in palm leaf manuscripts by recognizing the handwritten Tamil characters in manuscripts and storing them digitally. Character recognition is one of the most essential fields of pattern recognition and image processing. Generally Optical character recognition is the method of e-translation of typewritten text or handwritten images into machine editable text. The handwritten Tamil character recognition has been one of the challenging and active areas of research in the field of pattern recognition and image processing. In this study a trial was made to identify Tamil handwritten characters without extraction of feature using convolutional neural networks. This study uses convolutional neural networks for recognizing and classifying the Tamil palm leaf manuscripts of characters from separated character images. The convolutional neural network is a deep learning approach for which it does not need to retrieve features and also a rapid approach for character recognition. In the proposed system every character is expanded to needed pixels. The expanded characters have predetermined pixels and these pixels are considered as characteristics for neural network training. The trained network is employed for recognition and classification. Convolutional Network Model development contains convolution layer, Relu layer, pooling layer, fully connected layer. The ancient Tamil character dataset of 60 varying class has been created. The outputs reveal that the proposed approach generates better rates of recognition than that of schemes based on feature extraction for handwritten character recognition. The accuracy of the proposed approach has been identified as 97% which shows that the proposed approach is effective in terms of recognition of ancient characters.

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Secure UAV-Based System to Detect Small Boats Using Neural Networks

Complexity ◽

10.1155/2019/7206096 ◽

2019 ◽

Vol 2019 ◽

pp. 1-11 ◽

Cited By ~ 2

Author(s):

Moisés Lodeiro-Santiago ◽

Pino Caballero-Gil ◽

Ricardo Aguasca-Colomo ◽

Cándido Caballero-Gil

Keyword(s):

Artificial Intelligence ◽

Neural Networks ◽

Image Processing ◽

Deep Learning ◽

Unmanned Aerial Vehicles ◽

High Sensitivity ◽

Search And Rescue ◽

Detection Systems ◽

Aerial Vehicles ◽

Current Detection

This work presents a system to detect small boats (pateras) to help tackle the problem of this type of perilous immigration. The proposal makes extensive use of emerging technologies like Unmanned Aerial Vehicles (UAV) combined with a top-performing algorithm from the field of artificial intelligence known as Deep Learning through Convolutional Neural Networks. The use of this algorithm improves current detection systems based on image processing through the application of filters thanks to the fact that the network learns to distinguish the aforementioned objects through patterns without depending on where they are located. The main result of the proposal has been a classifier that works in real time, allowing the detection of pateras and people (who may need to be rescued), kilometres away from the coast. This could be very useful for Search and Rescue teams in order to plan a rescue before an emergency occurs. Given the high sensitivity of the managed information, the proposed system includes cryptographic protocols to protect the security of communications.

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