Intelligent eye Tumour Detection System

Dimililer, Kamil; Ever, Yoney Kirsal; Ratemi, Haithm

doi:10.1016/j.procs.2016.09.408

Cited by 28 publications

(13 citation statements)

References 5 publications

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“…Considering the proposed application, similar to existing studies [16][17][18][19], results of the system are obtained from a virtual machine that has a configuration of 2012R2 64 bits OS. It is connected to a super computer with 1 Gbits speed.…”

Section: Resultsmentioning

confidence: 99%

Effect of DCT Image Compression on Eye Gaze Direction Detection

et al. 2018

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Abstract. The prediction of eye direction detection is the one of the popular research topic in human computer interaction area. This paper defines eye gaze detection by using Discrete Cosine Transform. Actually, determining the position of eyes is difficult to estimate the location of gaze which is more challenging. The database of the suggested research is organized as gaze directions of right, left and centre. The database is created with varied ages of images. In this paper, Discrete Cosine Transform has been applied to the image database and the effect of image compression is tested by using back propagation neural networks.

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Section: Resultsmentioning

confidence: 99%

Effect of DCT Image Compression on Eye Gaze Direction Detection

et al. 2018

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“…Machine learning algorithms have been used for several challenging tasks, such as brain tumor segmentation with magnetic resonance (MR) imaging [4], age-related eye diseases [5], eye tumor detection [6], skin disease detection [7], and automated diabetic retinopathy testing [8]. Digital image processing and machine learning techniques are also being applied in eye disease recognition [5,6,[8][9][10]. It may potentially be able to detect abnormal changes in the cells, sclera, cornea, iris, pupil, and blood vessels.…”

Section: Introductionmentioning

confidence: 99%

An automated eye disease recognition system from visual content of facial imagesusing machine learning techniques

Akram¹,

Debnath²

2020

Turk J Elec Eng & Comp Sci

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Many eye diseases like cataracts, trachoma, or corneal ulcer can cause vision problems. Progression of these eye diseases can only be prevented if they are recognized accurately at the early stage. Visually observable symptoms differ a lot among these eye diseases. However, a wide variety of symptoms is necessary to be analyzed for the accurate detection of eye diseases. In this paper, we propose a novel approach to provide an automated eye disease recognition system using visually observable symptoms applying digital image processing techniques and machine learning techniques such as deep convolution neural network (DCNN) and support vector machine (SVM). We apply the principal component analysis and t-distributed stochastic neighbor embedding methods for better feature selection. The proposed system automatically divides the facial components from the frontal facial image and extracts the eye part. The proposed method analyzes and classifies seven eye diseases including cataracts, trachoma, conjunctivitis, corneal ulcer, ectropion, periorbital cellulitis, and Bitot's spot of vitamin A deficiency. From the experimental results, we see that the DCNN model outperforms SVM models. We also compare our method with some other existing methods. Our method shows improved accuracy compared to other methods. The average accuracy rate of our DCNN model is 98.79% with sensitivity of 97% and specificity of 99%.

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“…Different image processing techniques are applied to images in order to highlight the edges and prepare them for the back propagation neural networks. The images are prepared for simulation and testing purposes [7]. Therefore, three backpropagation neural networks are used.…”

Section: Introductionmentioning

confidence: 99%

Planning pesticides usage for herbal and animal pests based on intelligent classification system with image processing and neural networks

Dimililer

Ever

2018

ITM Web Conf.

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Abstract. Pests are divided into two as herbal and animal pests in agriculture, and detection and use of minimum pesticides are quite challenging task. Last three decades, researchers have been improving their studies on these manners. Therefore, effective, efficient, and as well as intelligent systems are designed and modelled. In this paper, an intelligent classification system is designed for detecting pests as herbal or animal to use of proper pesticides accordingly. The designed system suggests two main stages. Firstly, images are processed using different image processing techniques that images have specific distinguishing geometric patterns. The second stage is neural network phase for classification. A backpropagation neural network is used for training and testing with processed images. System is tested, and experiment results show efficiency and effective classification rate. Autonomy and time efficiency within the pesticide usage are also discussed.

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Intelligent eye Tumour Detection System

Cited by 28 publications

References 5 publications

Effect of DCT Image Compression on Eye Gaze Direction Detection

Effect of DCT Image Compression on Eye Gaze Direction Detection

An automated eye disease recognition system from visual content of facial imagesusing machine learning techniques

Planning pesticides usage for herbal and animal pests based on intelligent classification system with image processing and neural networks

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