Exudate detection for diabetic retinopathy with circular Hough transformation and convolutional neural networks

Adem, Kemal

doi:10.1016/j.eswa.2018.07.053

Cited by 94 publications

(42 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Therefore, to improve the CNN's performance, it is important to use appropriate parameters [18,19]. In the max pooling layer, which is the subsampling layer, the maximum value of the subregions is taken according to the predetermined scale (pool size) value of 3.…”

Section: Detection Of Stem-calyx Anatomic Regions Using the Cnn Modelmentioning

confidence: 99%

See 1 more Smart Citation

A hybrid model based on the convolutional neural network model and artificial bee colony or particle swarm optimization-based iterative thresholding for the detection of bruised apples

Hekim¹,

Cömert²,

Adem³

2020

Turk J Elec Eng & Comp Sci

Self Cite

View full text Add to dashboard Cite

In this study, apple images taken with near-infrared (NIR) cameras were classified as bruised and healthy objects using iterative thresholding approaches based on artificial bee colony (ABC) and particle swarm optimization (PSO) algorithms supported by a convolutional neural network (CNN) deep learning model. The proposed model includes the following stages: image acquisition, image preprocessing, the segmentation of anatomical regions (stemcalyx regions) to be discarded, the detection of bruised areas on the apple images, and their classification. For this aim, by using the image acquisition platform with a NIR camera, a total of 1200 images at 6 different angles were taken from 200 apples, of which 100 were bruised and 100 healthy. In order to increase the success of detection and classification, adaptive histogram equalization (AHE), edge detection, and morphological operations were applied to the images in the preprocessing stage, respectively. First, in order to segment and discard the stem-calyx anatomical regions of the images, the CNN model was trained by using the preprocessed images. Second, the threshold value was determined by means of the ABC/PSO-based iterative thresholding approach on the images whose stem-calyx regions were discarded, and then the bruised areas on the images with no stem-calyx anatomical regions were detected by using the determined threshold value. Finally, the apple images were classified as bruised and healthy objects by using this threshold value. In order to illustrate the classification success of our approaches, the same classification experiments were reimplemented by directly using the CNN model alone on the preprocessed images with no ABC and PSO approaches. Experimental results showed that the hybrid model proposed in this paper was more successful than the CNN model in which ABCand PSO-based iterative threshold approaches were not used.

show abstract

Section: Detection Of Stem-calyx Anatomic Regions Using the Cnn Modelmentioning

confidence: 99%

“…Step 7: If the maximum iteration number is reached, calculate the average of the brightness values at the end positions of the particles and select this value as the iterative threshold value [19].…”

Section: Pso-based Iterative Thresholding Approachmentioning

confidence: 99%

A hybrid model based on the convolutional neural network model and artificial bee colony or particle swarm optimization-based iterative thresholding for the detection of bruised apples

Hekim¹,

Cömert²,

Adem³

2020

Turk J Elec Eng & Comp Sci

Self Cite

View full text Add to dashboard Cite

show abstract

“…The model in [13] will be useful to isolate the sternness of DR images. In [14] a joint approach of CHT and Convolutional Neural Network (CNN) was offered for spotting EXs. This method is trained and tested in three different datasets and achieved 100%, 98.41%, sensitivity and specificity respectively in the DiaretDB0 data set, 99.2%, 97.97% sensitivity and specificity respectively in the DiaretDB1 dataset and 100%, 98.44% sensitivity and specificity respectively in the DrimDB data set.…”

Section: Literature Reviewmentioning

confidence: 99%

Classification of Diabetic Retinopathy Features using Bag of Feature Model

K.R*,

K.I,

2019

IJITEE

View full text Add to dashboard Cite

Deep learning (DL) as well as feature learning by unsupervised methods have made tremendous consideration in the past decades because of its great and dynamic capacity to change input data into high level depictions by means of various machine learning (ML) methods and approaches. Therefore these interests have also showed a fast and steady growth in the arena of medical image analysis, especially in Diabetic Retinopathy (DR) classification. On contradiction, manual interpretation involves excessive processing time, large amount of expertise and work. Sternness of the DR is analyzed relative to the existence of Microaneurysms (MAs), Exudates (EXs) and Hemorrhages(HEs). Spotting of DR in its early stage is crucial and important to avoid blindness. This paper proposes an algorithm to build an automated system to extract the above mentioned DR features which are the elemental and initial signs of diabetic retinopathy. Initial step in this algorithm is preprocessing of the original image. The next step in this features extraction algorithms is elimination of optic disc (OD) and blood vessels which have similar characteristic with these features. Blood vessels are segmented using Multi-Level Adaptive Thresholding. OD is segmented using morphological operations. Feature extraction and classification is achieved by using deep Bag of Feature (BoF) model which uses Speeded Up Robust Features Our method achieved 100% acuuracy in DRIVE database and over 90% accuracy for e-OPTHA database. Thus, the proposed methodology represents a track towards precise and highly automated DR diagnosis on a large substantial scale along with better sensitivity and specificity.

show abstract

“…Since the controls are made manually and visually by expert machine operators, the human factor can lead to the in machine learning algorithms [3]. In deep artificial neural networks, a convolutional artificial neural network (CNN) model can be created without the need for preprocessing and classifications can be made faster and more accurate than other machine learning methods [4].…”

Section: Introductionmentioning

confidence: 99%

Defect detection of seals in multilayer aseptic packages using deep learning

Adem¹,

Közkurt²

2019

Turk J Elec Eng & Comp Sci

Self Cite

View full text Add to dashboard Cite

Sealing in aseptic packages, one of the healthiest and cheapest technologies to protect food from parasites in the liquid food industry, requires a detailed and careful control process. Since the controls are made manually and visually by expert machine operators, the human factor can lead to the failure to detect defects, resulting in high cost and food safety risks. Therefore, this study aims to perform a leak test in aseptic package seals by a system that makes decisions using independent deep learning methods. The proposed Faster R-CNN and the Updated Faster R-CNN deep learning models were subjected to training and testing with a total of 400 images taken from a real production environment, resulting in a correct classification rate of 99.25%. As a result, it can be said that the study is the second study that performs a computer-aided quality control process with promising results, having distinctive features such as being the first study that conducts analysis using the deep learning method.

show abstract

Exudate detection for diabetic retinopathy with circular Hough transformation and convolutional neural networks

Cited by 94 publications

References 26 publications

A hybrid model based on the convolutional neural network model and artificial bee colony or particle swarm optimization-based iterative thresholding for the detection of bruised apples

A hybrid model based on the convolutional neural network model and artificial bee colony or particle swarm optimization-based iterative thresholding for the detection of bruised apples

Classification of Diabetic Retinopathy Features using Bag of Feature Model

Defect detection of seals in multilayer aseptic packages using deep learning

Contact Info

Product

Resources

About