scholarly journals Diagnosing Chronic Glaucoma Using Watershed and Convolutional Neural Network

2021 ◽  
Vol 9 (9) ◽  
pp. 272-283
Author(s):  
Naureen Fathima
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Convolutional Neural Network ◽  
Language Processing ◽  
Three Dimensional ◽  
Image Plane ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Computational Technique ◽  
Fundus Imaging

Abstract: Glaucoma is a disease that relates to the vision of human eye,Glaucoma is a disease that affects the human eye's vision. This sickness is regarded as an irreversible condition that causes eyesight degeneration. One of the most common causes of lifelong blindness is glaucoma in persons over the age of 40. Because of its trade-off between portability, size, and cost, fundus imaging is the most often utilised screening tool for glaucoma detection. Fundus imaging is a two-dimensional (2D) depiction of the three-dimensional (3D), semitransparent retinal tissues projected on to the imaging plane using reflected light. The idea plane that depicts the physical display screen through which a user perceives a virtual 3D scene is referred to as the "image plane”. The bulk of current algorithms for autonomous glaucoma assessment using fundus images rely on handcrafted segmentation-based features, which are influenced by the segmentation method used and the retrieved features. Convolutional neural networks (CNNs) are known for, among other things, their ability to learn highly discriminative features from raw pixel intensities. This work describes a computational technique for detecting glaucoma automatically. The major goal is to use a "image processing technique" to diagnose glaucoma using a fundus image as input. It trains datasets using a convolutional neural network (CNN). The Watershed algorithm is used for segmentation and is the most widely used technique in image processing. The following image processing processes are performed: region of interest, morphological procedures, and segmentation. This technique can be used to determine whether or not a person has Glaucoma. Keywords: Recommender system, item-based collaborative filtering, Natural Language Processing, Deep learning.

scholarly journals Pneumonia Classification of Thorax Images using Convolutional Neural Networks

Jurnal INFORM ◽  
2020 ◽  
Vol 5 (2) ◽  
pp. 62
Author(s):  
Mahmud Suyuti ◽  
Endang Setyati
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Convolutional Neural Network ◽  
Technology Development ◽  
Image Data ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Computing Technology ◽  
Correction Technique ◽  
High Level

The digital image processing technique is a product of computing technology development. Medical image data processing based on a computer is a product of computing technology development that can help a doctor to diagnose and observe a patient. This study aimed to perform classification on the image of the thorax by using Convolutional Neural Network (CNN).  The data used in this study is lung thorax images that have previously been diagnosed by a doctor with two classes, namely normal and pneumonia. The amount of data is 2.200, 1.760 for training, and 440 for testing. Three stages are used in image processing, namely scaling, gray scaling, and scratching. This study used Convolutional Neural Network (CNN) method with architecture ResNet-50. In the field of object recognition, CNN is the best method because it has the advantage of being able to find its features of the object image by conducting the convolution process during training. CNN has several models or architectures; one of them is ResNet-50 or Residual Network. The selection of ResNet-50 architecture in this study aimed to reduce the loss of gradients at certain network-level depths during training because the object is a chest image of X-Ray that has a high level of visual similarity between some pathology. Moreover, several visual factors also affect the image so that to produce good accuracy requires a certain level of depth on the CNN network. Optimization during training used Adaptive Momentum (Adam) because it had a bias correction technique that provided better approximations to improve accuracy. The results of this study indicated the thorax image classification with an accuracy of 97.73%.

scholarly journals Pneumonia Classification of Thorax Images using Convolutional Neural Networks

Jurnal INFORM ◽  
2020 ◽  
Vol 5 (2) ◽  
pp. 62-68
Author(s):  
Mahmud Suyuti ◽  
Endang Setyati
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Convolutional Neural Network ◽  
Technology Development ◽  
Image Data ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Computing Technology ◽  
Correction Technique ◽  
High Level

The digital image processing technique is a product of computing technology development. Medical image data processing based on a computer is a product of computing technology development that can help a doctor to diagnose and observe a patient. This study aimed to perform classification on the image of the thorax by using Convolutional Neural Network (CNN).  The data used in this study is lung thorax images that have previously been diagnosed by a doctor with two classes, namely normal and pneumonia. The amount of data is 2.200, 1.760 for training, and 440 for testing. Three stages are used in image processing, namely scaling, gray scaling, and scratching. This study used Convolutional Neural Network (CNN) method with architecture ResNet-50. In the field of object recognition, CNN is the best method because it has the advantage of being able to find its features of the object image by conducting the convolution process during training. CNN has several models or architectures; one of them is ResNet-50 or Residual Network. The selection of ResNet-50 architecture in this study aimed to reduce the loss of gradients at certain network-level depths during training because the object is a chest image of X-Ray that has a high level of visual similarity between some pathology. Moreover, several visual factors also affect the image so that to produce good accuracy requires a certain level of depth on the CNN network. Optimization during training used Adaptive Momentum (Adam) because it had a bias correction technique that provided better approximations to improve accuracy. The results of this study indicated the thorax image classification with an accuracy of 97.73%.

Analysis of Insole Geometry and Deformity by Using a Three-Dimensional Image Processing Technique: A Preliminary Study

2019 ◽  
Vol 109 (2) ◽  
pp. 98-107
Author(s):  
Kit-lun Yick ◽  
Wai-ting Lo ◽  
Sun-pui Ng ◽  
Joanne Yip ◽  
Hung-hei Kwan ◽  
...  
Keyword(s):  
Image Processing ◽  
Cross Sections ◽  
Plantar Pressure ◽  
Three Dimensional ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Diabetic Patients ◽  
Clinical Practices ◽  
Cross Sectional ◽  
Orthotic Treatment

Background: Accurate representation of the insole geometry is crucial for the development and performance evaluation of foot orthoses designed to redistribute plantar pressure, especially for diabetic patients. Methods: Considering the limitations in the type of equipment and space available in clinical practices, this study adopted a simple portable three-dimensional (3-D) desktop scanner to evaluate the 3-D geometry of an orthotic insole and the corresponding deformities after the insole has been worn. The shape of the insole structure along horizontal cross sections is defined with 3-D scanning and image processing. Accompanied by an in-shoe pressure measurement system, plantar pressure distribution in four foot regions (hallux, metatarsal heads, midfoot, and heel) is analyzed and evaluated for insole deformity. Results: Insole deformities are quantified across the four foot regions. The hallux region tends to show the greatest changes in shape geometry (17%–50%) compared with the other foot regions after 2 months of insole wear. As a result of insole deformities, plantar peak pressures change considerably (–4.3% to +69.5%) during the course of treatment. Conclusions: Changes in shape geometry of the insoles could be objectively quantified with 3-D scanning techniques and image processing. This investigation finds that, in general, the design of orthotic insoles may not be adequate for diabetic individuals with similar foot problems. The drastic changes in the insole shape geometry and cross-sectional areas during orthotic treatment may reduce insole fit and conformity. An inadequate insole design may also affect plantar pressure reduction. The approach proposed herein, therefore, allows for objective quantification of insole shape geometry, which results in effective and optimal orthotic treatment.

Cloud-based application for rice moisture content measurement using image processing technique and perceptron neural network

2017 ◽  
Author(s):  
Febus Reidj G. Cruz ◽  
Dionis A. Padilla ◽  
Carlos C. Hortinela ◽  
Krissel C. Bucog ◽  
Mildred C. Sarto ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Moisture Content ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Content Measurement ◽  
Moisture Content Measurement

Compact Disc Visual Inspection Using Neural Network

2012 ◽  
Vol 433-440 ◽  
pp. 727-732
Author(s):  
Anton Satria Prabuwono ◽  
Siti Rahayu Zulkipli ◽  
Doli Anggia Harahap ◽  
Wendi Usino ◽  
A. Hasniaty
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Visual Inspection ◽  
Processing Technique ◽  
Compact Disc ◽  
Image Processing Technique ◽  
Accuracy Rate ◽  
Network Training ◽  
Fields Of Study

Image processing is widely used in various fields of study including manufacturing as product inspection. In compact disc manufacturing, image processing has been implemented to recognize defect products. In this research, we implemented image processing technique as pre-processing processes. The aim is to acquire simple image to be processed and analyzed. In order to express the object from the image, the features were extracted using Invariant Moment (IM). Afterward, neural network was used to train the input from IM’s results. Thus, decision can be made whether the compact disc is accepted or rejected based on the training. Two experiments have been done in this research to evaluate 40 datasets of good and defective images of compact discs. The result shows that accuracy rate increased and can identify the quality of compact discs based on neural network training.

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