In human eye, the state of the blood vessel is a crucial diagnostic factor. The segmentation of blood vessel
from the fundus image is difficult due to the spatial complexity, adjacency, overlapping and variability of
blood vessel. The detection of ophthalmic pathologies like hypertensive disorders, diabetic retinopathy and
cardiovascular diseases are remain challenging task due to the wide-ranging distribution of blood vessels.
In this paper, Stacked Autoencoder and CNN (Convolutional Neural Network) technique is proposed to
extract the blood vessel from the fundus image. Based on the experiments conducted using the Stacked
Autoencoder and Convolutional Neural Network gives 90% & 95% accuracy for segmentation.
Microscopy images segmentation algorithm based on shearlet neural networkjournalBEEI
Microscopic images are becoming important and need to be studied to know the details and how-to quantitatively evaluate decellularization. Most of the existing research focuses on deep learning-based techniques that lack simplification for decellularization. A new computational method for the segmentation microscopy images based on the shearlet neural network (SNN) has been introduced. The proposal is to link the concept of shearlets transform and neural networks into a single unit. The method contains a feed-forward neural network and uses a single hidden layer. The activation functions are depending on the standard shearlet transform. The proposed SNN is a powerful technology for segmenting an electron microscopic image that is trained without relying on the pre-information of the data. The shearlet neural networks capture the features of full accuracy and contextual information, respectively. The expected value for specific inputs is estimated by learning the functional configuration of a network for the sequence of observed value. Experimental results on the segmentation of two-dimensional microscopy images are promising and confirm the benefits of the proposed approach. Lastly, we investigate on a challenging datasets ISBI 2012 that our method (SNN) achieves superior outcomes when compared to classical and deep learning-based methods.
CONTRAST OF RESNET AND DENSENET BASED ON THE RECOGNITION OF SIMPLE FRUIT DATA...rinzindorjej
In this paper, a fruit image data set is used to compare the efficiency and accuracy of two widely used
Convolutional Neural Network, namely the ResNet and the DenseNet, for the recognition of 50 different
kinds of fruits. In the experiment, the structure of ResNet-34 and DenseNet_BC-121 (with bottleneck layer)
are used. The mathematic principle, experiment detail and the experiment result will be explained through
comparison.
In this paper, a fruit image data set is used to compare the efficiency and accuracy of two widely used Convolutional Neural Network, namely the ResNet and the DenseNet, for the recognition of 50 different kinds of fruits. In the experiment, the structure of ResNet-34 and DenseNet_BC-121 (with bottleneck layer) are used. The mathematic principle, experiment detail and the experiment result will be explained through comparison.
CONTRAST OF RESNET AND DENSENET BASED ON THE RECOGNITION OF SIMPLE FRUIT DATA...rinzindorjej
In this paper, a fruit image data set is used to compare the efficiency and accuracy of two widely used Convolutional Neural Network, namely the ResNet and the DenseNet, for the recognition of 50 different kinds of fruits. In the experiment, the structure of ResNet-34 and DenseNet_BC-121 (with bottleneck layer)
are used. The mathematic principle, experiment detail and the experiment result will be explained through comparison.
Modeling of neural image compression using gradient decent technologytheijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
Theoretical work submitted to the Journal should be original in its motivation or modeling structure. Empirical analysis should be based on a theoretical framework and should be capable of replication. It is expected that all materials required for replication (including computer programs and data sets) should be available upon request to the authors.
The International Journal of Engineering & Science would take much care in making your article published without much delay with your kind cooperation
Optimized Neural Network for Classification of Multispectral ImagesIDES Editor
The proposed work involves the multiobjective PSO
based optimization of artificial neural network structure for
the classification of multispectral satellite images. The neural
network is used to classify each image pixel in various land
cove types like vegetations, waterways, man-made structures
and road network. It is per pixel supervised classification using
spectral bands (original feature space). Use of neural network
for classification requires selection of most discriminative
spectral bands and determination of optimal number of nodes
in hidden layer. We propose new methodology based on
multiobjective particle swarm optimization (MOPSO) to
determine discriminative spectral bands and the number of
hidden layer node simultaneously. The result obtained using
such optimized neural network is compared with that of
traditional classifiers like MLC and Euclidean classifier. The
performance of all classifiers is evaluated quantitatively using
Xie-Beni and â indexes. The result shows the superiority of
the proposed method.
Microscopy images segmentation algorithm based on shearlet neural networkjournalBEEI
Microscopic images are becoming important and need to be studied to know the details and how-to quantitatively evaluate decellularization. Most of the existing research focuses on deep learning-based techniques that lack simplification for decellularization. A new computational method for the segmentation microscopy images based on the shearlet neural network (SNN) has been introduced. The proposal is to link the concept of shearlets transform and neural networks into a single unit. The method contains a feed-forward neural network and uses a single hidden layer. The activation functions are depending on the standard shearlet transform. The proposed SNN is a powerful technology for segmenting an electron microscopic image that is trained without relying on the pre-information of the data. The shearlet neural networks capture the features of full accuracy and contextual information, respectively. The expected value for specific inputs is estimated by learning the functional configuration of a network for the sequence of observed value. Experimental results on the segmentation of two-dimensional microscopy images are promising and confirm the benefits of the proposed approach. Lastly, we investigate on a challenging datasets ISBI 2012 that our method (SNN) achieves superior outcomes when compared to classical and deep learning-based methods.
CONTRAST OF RESNET AND DENSENET BASED ON THE RECOGNITION OF SIMPLE FRUIT DATA...rinzindorjej
In this paper, a fruit image data set is used to compare the efficiency and accuracy of two widely used
Convolutional Neural Network, namely the ResNet and the DenseNet, for the recognition of 50 different
kinds of fruits. In the experiment, the structure of ResNet-34 and DenseNet_BC-121 (with bottleneck layer)
are used. The mathematic principle, experiment detail and the experiment result will be explained through
comparison.
In this paper, a fruit image data set is used to compare the efficiency and accuracy of two widely used Convolutional Neural Network, namely the ResNet and the DenseNet, for the recognition of 50 different kinds of fruits. In the experiment, the structure of ResNet-34 and DenseNet_BC-121 (with bottleneck layer) are used. The mathematic principle, experiment detail and the experiment result will be explained through comparison.
CONTRAST OF RESNET AND DENSENET BASED ON THE RECOGNITION OF SIMPLE FRUIT DATA...rinzindorjej
In this paper, a fruit image data set is used to compare the efficiency and accuracy of two widely used Convolutional Neural Network, namely the ResNet and the DenseNet, for the recognition of 50 different kinds of fruits. In the experiment, the structure of ResNet-34 and DenseNet_BC-121 (with bottleneck layer)
are used. The mathematic principle, experiment detail and the experiment result will be explained through comparison.
Modeling of neural image compression using gradient decent technologytheijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
Theoretical work submitted to the Journal should be original in its motivation or modeling structure. Empirical analysis should be based on a theoretical framework and should be capable of replication. It is expected that all materials required for replication (including computer programs and data sets) should be available upon request to the authors.
The International Journal of Engineering & Science would take much care in making your article published without much delay with your kind cooperation
Optimized Neural Network for Classification of Multispectral ImagesIDES Editor
The proposed work involves the multiobjective PSO
based optimization of artificial neural network structure for
the classification of multispectral satellite images. The neural
network is used to classify each image pixel in various land
cove types like vegetations, waterways, man-made structures
and road network. It is per pixel supervised classification using
spectral bands (original feature space). Use of neural network
for classification requires selection of most discriminative
spectral bands and determination of optimal number of nodes
in hidden layer. We propose new methodology based on
multiobjective particle swarm optimization (MOPSO) to
determine discriminative spectral bands and the number of
hidden layer node simultaneously. The result obtained using
such optimized neural network is compared with that of
traditional classifiers like MLC and Euclidean classifier. The
performance of all classifiers is evaluated quantitatively using
Xie-Beni and â indexes. The result shows the superiority of
the proposed method.
In the present day automation, the researchers have been using microcomputers and its allies to carryout processing of physical quantities and detection of Cholesterol in blood and bio-medical Images. The latest trend is to use FPGA counter parts, as these devices offer many advantages in comparison with Programmable devices. These devices are very fast and involve hardwired logic. FPGA are dedicated hardware for processing logic and do not have an operating system. That means that speeds can be very fast and multiple control loops can run on a single FPGA device at different rates. In this paper, an attempt is being made to develop a prototype system to sense the Cholesterol portion in MRI image using modified Set Partitioning in Hierarchical Trees (SHIPT) wavelets transformation and Radial Basis Function (RBF). An each stage of Cholesterol detection are displayed on LCD monitor for clear view of improved version of MRI image and to find Cholesterol area. The performance parameters have been measured in terms of Peak Signal to Noise Ratio (PSNR), and Mean Square Error (MSE).
AN IMPROVED METHOD FOR IDENTIFYING WELL-TEST INTERPRETATION MODEL BASED ON AG...IAEME Publication
This paper presents an approach based on applying an aggregated predictor formed by multiple versions of a multilayer neural network with a back-propagation optimization algorithm for helping the engineer to get a list of the most appropriate well-test interpretation models for a given set of pressure/ production data. The proposed method consists of three stages: (1) data decorrelation through principal component analysis to reduce the covariance between the variables and the dimension of the input layer in the artificial neural network, (2) bootstrap replicates of the learning set where the data is repeatedly sampled with a random split of the data into train sets and using these as new learning sets, and (3) automatic reservoir model identification through aggregated predictor formed by a plurality vote when predicting a new class. This method is described in detail to ensure successful replication of results. The required training and test dataset were generated by using analytical solution models. In our case, there were used 600 samples: 300 for training, 100 for cross-validation, and 200 for testing. Different network structures were tested during this study to arrive at optimum network design. We notice that the single net methodology always brings about confusion in selecting the correct model even though the training results for the constructed networks are close to 1. We notice also that the principal component analysis is an effective strategy in reducing the number of input features, simplifying the network structure, and lowering the training time of the ANN. The results obtained show that the proposed model provides better performance when predicting new data with a coefficient of correlation approximately equal to 95% Compared to a previous approach 80%, the combination of the PCA and ANN is more stable and determine the more accurate results with lesser computational complexity than was feasible previously. Clearly, the aggregated predictor is more stable and shows less bad classes compared to the previous approach.
Application of Hybrid Genetic Algorithm Using Artificial Neural Network in Da...IOSRjournaljce
The main purpose of data mining is to extract knowledge from large amount of data. Artificial Neural network (ANN) has already been applied in a variety of domains with remarkable success. This paper presents the application of hybrid model for stroke disease that integrates Genetic algorithm and back propagation algorithm. Selecting a good subset of features, without sacrificing accuracy, is of great importance for neural networks to be successfully applied to the area. In addition the hybrid model that leads to further improvised categorization, accuracy compared to the result produced by genetic algorithm alone. In this study, a new hybrid model of Neural Networks and Genetic Algorithm (GA) to initialize and optimize the connection weights of ANN so as to improve the performance of the ANN and the same has been applied in a medical problem of predicting stroke disease for verification of the results.
Ensemble learning approach for multi-class classification of Alzheimer’s stag...TELKOMNIKA JOURNAL
Alzheimer’s disease (AD) is a gradually progressing neurodegenerative irreversible disorder. Mild cognitive impairment convertible (MCIc) is the clinical forerunner of AD. Precise diagnosis of MCIc is essential for effective treatments to reduce the progressing rate of the disease. The other cognitive states included in this study are mild cognitive impairment non-convertible (MCInc) and cognitively normal (CN). MCInc is a stage in which aged people suffer from memory problems, but the stage will not lead to AD. The classification between MCIc and MCInc is crucial for the early detection of AD. In this work, an algorithm is proposed which concatenates the output layers of Xception, InceptionV3, and MobileNet pre-trained models. The algorithm is tested on the baseline T1-weighted structural magnetic resonance imaging (MRI) images obtained from Alzheimer’s disease neuroimaging initiative database. The proposed algorithm provided multi-class classification accuracy of 85%. Also, the proposed algorithm gave an accuracy of 85% for classifying MCIc vs MCInc, an accuracy of 94% for classifying AD vs CN, and an accuracy of 92% for classifying MCIc vs CN. The results exhibit that the proposed algorithm outruns other state-of-the-art methods for the multi-class classification and classification between MCIc and MCInc.
Mobile Network Coverage Determination at 900MHz for Abuja Rural Areas using A...ijtsrd
This study proposes Artificial Neural Network ANN based field strength prediction models for the rural areas of Abuja, the federal capital territory of Nigeria. The ANN based models were created on bases of the Generalized Regression Neural network GRNN and the Multi Layer Perceptron Neural Network MLP NN . These networks were created, trained and tested for field strength prediction using received power data recorded at 900MHz from multiple Base Transceiver Stations BTSs distributed across the rural areas. Results indicate that the GRNN and MLP NN based models with Root Mean Squared Error RMSE values of 4.78dBm and 5.56dBm respectively, offer significant improvement over the empirical Hata Okumura counterpart, which overestimates the signal strength by an RMSE value of 20.17dBm. Deme C. Abraham ""Mobile Network Coverage Determination at 900MHz for Abuja Rural Areas using Artificial Neural Networks"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-2 , February 2020,
URL: https://www.ijtsrd.com/papers/ijtsrd30228.pdf
Paper Url : https://www.ijtsrd.com/computer-science/artificial-intelligence/30228/mobile-network-coverage-determination-at-900mhz-for-abuja-rural-areas-using-artificial-neural-networks/deme-c-abraham
Haemorrhage Detection and Classification: A ReviewIJERA Editor
In Indian population, the count of diabetic peoples gets increasing day by day. Due to improper balance of insulin in the human body causes Diabetic. The most common symptom of the person with diabetes is diabetic retinopathy, which leads to blindness. The effect due to DR can reduce by early detection of Haemorrhages and treated at an early stage. In recent year, there is an increased interest in the field of medical image processing. Many researchers have developed advanced algorithms for Haemorrhage detection using fundus images. In proposed paper, we discuss various methods for Haemorrhage detection and classification.
DETECTION OF HUMAN BLADDER CANCER CELLS USING IMAGE PROCESSINGprj_publication
Bladder cancer presents a spectrum of different diatheses. A precise assessment for
individualized treatment depends on the accuracy of the initial diagnosis. In this method the
performance of the level set segmentation is subject to appropriate initialization and optimal
configuration of controlling parameters, which require substantial manual intervention. A
new fuzzy level set algorithm is proposed in this paper to facilitate medical image
segmentation. It is able to directly evolve from the initial segmentation by spatial fuzzy
clustering. The Spatial induced fuzzy c-means using pixel classification and level set
methods are utilizing dynamic variational boundaries for image segmentation. The
controlling parameters of level set evolution are also estimated from the results of clustering.
The fuzzy level set algorithm is enhanced with locally regularized evolution. Such
improvements facilitate level set manipulation and lead to more robust segmentation.
Performance evaluation of the proposed algorithm was carried on medical images
Melanoma Cell Detection in Lymph Nodes Histopathological Images using Deep Le...sipij
Histopathological images are widely used to diagnose diseases including skin cancer. As digital
histopathological images are typically of very large size, in the order of several billion pixels, automated
identification of all abnormal cell nuclei and their distribution within multiple tissue sections would assist
rapid comprehensive diagnostic assessment. In this paper, we propose a technique, using deep learning
algorithms, to segment the cell nuclei in Hematoxylin and Eosin (H&E) stained images and detect the
abnormal melanocytes within histopathological images. The Nuclear segmentation is done by using a
Convolutional Neural Network (CNN) and hand-crafted features are extracted for each nucleus. The
segmented nuclei are then classified into normal and abnormal nuclei using a Support Vector Machine
classifier. Experimental results show that the CNN can segment the nuclei with more than 90% accuracy.
The proposed technique has a low computational complexity.
Articles -Signal & Image Processing: An International Journal (SIPIJ)sipij
Histopathological images are widely used to diagnose diseases including skin cancer. As digital
histopathological images are typically of very large size, in the order of several billion pixels, automated
identification of all abnormal cell nuclei and their distribution within multiple tissue sections would assist
rapid comprehensive diagnostic assessment. In this paper, we propose a technique, using deep learning
algorithms, to segment the cell nuclei in Hematoxylin and Eosin (H&E) stained images and detect the
abnormal melanocytes within histopathological images. The Nuclear segmentation is done by using a
Convolutional Neural Network (CNN) and hand-crafted features are extracted for each nucleus. The
segmented nuclei are then classified into normal and abnormal nuclei using a Support Vector Machine
classifier. Experimental results show that the CNN can segment the nuclei with more than 90% accuracy.
The proposed technique has a low computational complexity.
MELANOMA CELL DETECTION IN LYMPH NODES HISTOPATHOLOGICAL IMAGES USING DEEP LE...sipij
Histopathological images are widely used to diagnose diseases including skin cancer. As digital histopathological images are typically of very large size, in the order of several billion pixels, automated identification of all abnormal cell nuclei and their distribution within multiple tissue sections would assist
rapid comprehensive diagnostic assessment. In this paper, we propose a technique, using deep learning algorithms, to segment the cell nuclei in Hematoxylin and Eosin (H&E) stained images and detect the abnormal melanocytes within histopathological images. The Nuclear segmentation is done by using a Convolutional Neural Network (CNN) and hand-crafted features are extracted for each nucleus. The segmented nuclei are then classified into normal and abnormal nuclei using a Support Vector Machine classifier. Experimental results show that the CNN can segment the nuclei with more than 90% accuracy. The proposed technique has a low computational complexity.
IOT SOLUTIONS FOR SMART PARKING- SIGFOX TECHNOLOGYCSEIJJournal
Sigfox technology has emerged as a competitive product in the communication service provider market for
approximately a decade. Widely implemented for smart parking solutions across various European
countries, it has now gained traction in Germany as well. The technology's successful track record and
reputation in the market demonstrate its effectiveness and reliability in addressing the communication
needs of IoT applications, particularly in the context of vehicle parking systems. This is noted in terms of a
city like Berlin-Germany, for on which the study is conducted. The major challenge being on how to relate
the parking techniques in a more user friendly, cost effective and less energy consumpmti0n mode where
the questions had at the beginning of the paper, relatively at the end the answers are sought to it via Sigfox
and its comparison with other related technologies like LoRA WAN and weightless. But more so future
areas of research study is also pointed out on areas which are not clearly identified in this particular
research area.
This paper entails the pros, cons adaptive, emerging and existing technology study in terms of cloud, big
data, Data analytics are all discussed in tandem to Sigfox.
Reliability Improvement with PSP of Web-Based Software ApplicationsCSEIJJournal
In diverse industrial and academic environments, the quality of the software has been evaluated using
different analytic studies. The contribution of the present work is focused on the development of a
methodology in order to improve the evaluation and analysis of the reliability of web-based software
applications. The Personal Software Process (PSP) was introduced in our methodology for improving the
quality of the process and the product. The Evaluation + Improvement (Ei) process is performed in our
methodology to evaluate and improve the quality of the software system. We tested our methodology in a
web-based software system and used statistical modeling theory for the analysis and evaluation of the
reliability. The behavior of the system under ideal conditions was evaluated and compared against the
operation of the system executing under real conditions. The results obtained demonstrated the
effectiveness and applicability of our methodology
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In the present day automation, the researchers have been using microcomputers and its allies to carryout processing of physical quantities and detection of Cholesterol in blood and bio-medical Images. The latest trend is to use FPGA counter parts, as these devices offer many advantages in comparison with Programmable devices. These devices are very fast and involve hardwired logic. FPGA are dedicated hardware for processing logic and do not have an operating system. That means that speeds can be very fast and multiple control loops can run on a single FPGA device at different rates. In this paper, an attempt is being made to develop a prototype system to sense the Cholesterol portion in MRI image using modified Set Partitioning in Hierarchical Trees (SHIPT) wavelets transformation and Radial Basis Function (RBF). An each stage of Cholesterol detection are displayed on LCD monitor for clear view of improved version of MRI image and to find Cholesterol area. The performance parameters have been measured in terms of Peak Signal to Noise Ratio (PSNR), and Mean Square Error (MSE).
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This paper presents an approach based on applying an aggregated predictor formed by multiple versions of a multilayer neural network with a back-propagation optimization algorithm for helping the engineer to get a list of the most appropriate well-test interpretation models for a given set of pressure/ production data. The proposed method consists of three stages: (1) data decorrelation through principal component analysis to reduce the covariance between the variables and the dimension of the input layer in the artificial neural network, (2) bootstrap replicates of the learning set where the data is repeatedly sampled with a random split of the data into train sets and using these as new learning sets, and (3) automatic reservoir model identification through aggregated predictor formed by a plurality vote when predicting a new class. This method is described in detail to ensure successful replication of results. The required training and test dataset were generated by using analytical solution models. In our case, there were used 600 samples: 300 for training, 100 for cross-validation, and 200 for testing. Different network structures were tested during this study to arrive at optimum network design. We notice that the single net methodology always brings about confusion in selecting the correct model even though the training results for the constructed networks are close to 1. We notice also that the principal component analysis is an effective strategy in reducing the number of input features, simplifying the network structure, and lowering the training time of the ANN. The results obtained show that the proposed model provides better performance when predicting new data with a coefficient of correlation approximately equal to 95% Compared to a previous approach 80%, the combination of the PCA and ANN is more stable and determine the more accurate results with lesser computational complexity than was feasible previously. Clearly, the aggregated predictor is more stable and shows less bad classes compared to the previous approach.
Application of Hybrid Genetic Algorithm Using Artificial Neural Network in Da...IOSRjournaljce
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Alzheimer’s disease (AD) is a gradually progressing neurodegenerative irreversible disorder. Mild cognitive impairment convertible (MCIc) is the clinical forerunner of AD. Precise diagnosis of MCIc is essential for effective treatments to reduce the progressing rate of the disease. The other cognitive states included in this study are mild cognitive impairment non-convertible (MCInc) and cognitively normal (CN). MCInc is a stage in which aged people suffer from memory problems, but the stage will not lead to AD. The classification between MCIc and MCInc is crucial for the early detection of AD. In this work, an algorithm is proposed which concatenates the output layers of Xception, InceptionV3, and MobileNet pre-trained models. The algorithm is tested on the baseline T1-weighted structural magnetic resonance imaging (MRI) images obtained from Alzheimer’s disease neuroimaging initiative database. The proposed algorithm provided multi-class classification accuracy of 85%. Also, the proposed algorithm gave an accuracy of 85% for classifying MCIc vs MCInc, an accuracy of 94% for classifying AD vs CN, and an accuracy of 92% for classifying MCIc vs CN. The results exhibit that the proposed algorithm outruns other state-of-the-art methods for the multi-class classification and classification between MCIc and MCInc.
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performance of the level set segmentation is subject to appropriate initialization and optimal
configuration of controlling parameters, which require substantial manual intervention. A
new fuzzy level set algorithm is proposed in this paper to facilitate medical image
segmentation. It is able to directly evolve from the initial segmentation by spatial fuzzy
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methods are utilizing dynamic variational boundaries for image segmentation. The
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histopathological images are typically of very large size, in the order of several billion pixels, automated
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rapid comprehensive diagnostic assessment. In this paper, we propose a technique, using deep learning
algorithms, to segment the cell nuclei in Hematoxylin and Eosin (H&E) stained images and detect the
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rapid comprehensive diagnostic assessment. In this paper, we propose a technique, using deep learning
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segmented nuclei are then classified into normal and abnormal nuclei using a Support Vector Machine
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Sigfox technology has emerged as a competitive product in the communication service provider market for
approximately a decade. Widely implemented for smart parking solutions across various European
countries, it has now gained traction in Germany as well. The technology's successful track record and
reputation in the market demonstrate its effectiveness and reliability in addressing the communication
needs of IoT applications, particularly in the context of vehicle parking systems. This is noted in terms of a
city like Berlin-Germany, for on which the study is conducted. The major challenge being on how to relate
the parking techniques in a more user friendly, cost effective and less energy consumpmti0n mode where
the questions had at the beginning of the paper, relatively at the end the answers are sought to it via Sigfox
and its comparison with other related technologies like LoRA WAN and weightless. But more so future
areas of research study is also pointed out on areas which are not clearly identified in this particular
research area.
This paper entails the pros, cons adaptive, emerging and existing technology study in terms of cloud, big
data, Data analytics are all discussed in tandem to Sigfox.
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In diverse industrial and academic environments, the quality of the software has been evaluated using
different analytic studies. The contribution of the present work is focused on the development of a
methodology in order to improve the evaluation and analysis of the reliability of web-based software
applications. The Personal Software Process (PSP) was introduced in our methodology for improving the
quality of the process and the product. The Evaluation + Improvement (Ei) process is performed in our
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web-based software system and used statistical modeling theory for the analysis and evaluation of the
reliability. The behavior of the system under ideal conditions was evaluated and compared against the
operation of the system executing under real conditions. The results obtained demonstrated the
effectiveness and applicability of our methodology
DATA MINING FOR STUDENTS’ EMPLOYABILITY PREDICTIONCSEIJJournal
This study has been undertaken to predict the student employability.Assessing student employability
provides a method of integrating student abilities and employer business requirements, which is becoming
an increasingly important concern for academic institutions. Improving student evaluation techniques for
employability can help students to have a better understanding of business organizations and find the right
one for them. The data for the training classification models is gathered through a survey in which students
are asked to fill out a questionnaire in which they may indicate their abilities and academic achievement.
This information may be used to determine their competency in a variety of skill categories, including soft
skills, problem-solving skills and technical abilities and so on.The goal of this research is to use data
mining to predict student employability by considering different factors such as skills that the students have
gained during their diploma level and time duration with respect to the knowledge they have captured
when they expect the placement at the end of graduation. Further during this research most specific skills
with relevant to each job category also was identified. In this research for the prediction of the student
employability different data mining models such as such as KNN, Naive Bayer’s, and Decision Tree were
evaluated and out of that best model also was identified for this institute's student’s employability
prediction.So, in this research classification and association techniques were used and evaluated.
Call for Articles - Computer Science & Engineering: An International Journal ...CSEIJJournal
Computer Science & Engineering: An International Journal (CSEIJ) is a bi-monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Computer Science & Computer Engineering. The journal is devoted to the publication of high quality papers on theoretical and practical aspects of computer science and computer Engineering.
A Complexity Based Regression Test Selection StrategyCSEIJJournal
Software is unequivocally the foremost and indispensable entity in this technologically driven world.
Therefore quality assurance, and in particular, software testing is a crucial step in the software
development cycle. This paper presents an effective test selection strategy that uses a Spectrum of
Complexity Metrics (SCM). Our aim in this paper is to increase the efficiency of the testing process by
significantly reducing the number of test cases without having a significant drop in test effectiveness. The
strategy makes use of a comprehensive taxonomy of complexity metrics based on the product level (class,
method, statement) and its characteristics.We use a series of experiments based on three applications with
a significant number of mutants to demonstrate the effectiveness of our selection strategy.For further
evaluation, we compareour approach to boundary value analysis. The results show the capability of our
approach to detect mutants as well as the seeded errors.
XML Encryption and Signature for Securing Web ServicesCSEIJJournal
In this research, we have focused on the most challenging issue that Web Services face, i.e. how to secure
their information. Web Services security could be guaranteed by employing security standards, which is the
main focus of this search. Every suggested model related to security design should put in the account the
securities' objectives; integrity, confidentiality, non- repudiation, authentication, and authorization. The
proposed model describes SOAP messages and the way to secure their contents. Due to the reason that
SOAP message is the core of the exchanging information in Web Services, this research has developed a
security model needed to ensure e-business security. The essence of our model depends on XML encryption
and XML signature to encrypt and sign SOAP message. The proposed model looks forward to achieve a
high speed of transaction and a strong level of security without jeopardizing the performance of
transmission information.
Performance Comparison of PCA,DWT-PCA And LWT-PCA for Face Image RetrievalCSEIJJournal
This paper compares the performance of face image retrieval system based on discrete wavelet transforms
and Lifting wavelet transforms with principal component analysis (PCA). These techniques are
implemented and their performances are investigated using frontal facial images from the ORL database.
The Discrete Wavelet Transform is effective in representing image features and is suitable in Face image
retrieval, it still encounters problems especially in implementation; e.g. Floating point operation and
decomposition speed. We use the advantages of lifting scheme, a spatial approach for constructing wavelet
filters, which provides feasible alternative for problems facing its classical counterpart. Lifting scheme has
such intriguing properties as convenient construction, simple structure, integer-to-integer transform, low
computational complexity as well as flexible adaptivity, revealing its potentials in Face image retrieval.
Comparing to PCA and DWT with PCA, Lifting wavelet transform with PCA gives less computation and
DWT-PCA gives high retrieval rate..
Call for Papers - Computer Science & Engineering: An International Journal (C...CSEIJJournal
Computer Science & Engineering: An International Journal (CSEIJ) is a bi-monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Computer Science & Computer Engineering. The journal is devoted to the publication of high quality papers on theoretical and practical aspects of computer science and computer Engineering.
Paper Submission - Computer Science & Engineering: An International Journal (...CSEIJJournal
Computer Science & Engineering: An International Journal (CSEIJ) is a bi-monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Computer Science & Computer Engineering. The journal is devoted to the publication of high quality papers on theoretical and practical aspects of computer science and computer Engineering.
Performance Comparison of PCA,DWT-PCA And LWT-PCA for Face Image RetrievalCSEIJJournal
This paper compares the performance of face image retrieval system based on discrete wavelet transforms
and Lifting wavelet transforms with principal component analysis (PCA). These techniques are
implemented and their performances are investigated using frontal facial images from the ORL database.
The Discrete Wavelet Transform is effective in representing image features and is suitable in Face image
retrieval, it still encounters problems especially in implementation; e.g. Floating point operation and
decomposition speed. We use the advantages of lifting scheme, a spatial approach for constructing wavelet
filters, which provides feasible alternative for problems facing its classical counterpart. Lifting scheme has
such intriguing properties as convenient construction, simple structure, integer-to-integer transform, low
computational complexity as well as flexible adaptivity, revealing its potentials in Face image retrieval.
Comparing to PCA and DWT with PCA, Lifting wavelet transform with PCA gives less computation and
DWT-PCA gives high retrieval rate..
Call for Papers - Computer Science & Engineering: An International Journal (C...CSEIJJournal
Computer Science & Engineering: An International Journal (CSEIJ) is a bi-monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Computer Science & Computer Engineering. The journal is devoted to the publication of high quality papers on theoretical and practical aspects of computer science and computer Engineering.
Data security and privacy are important to prevent the re-
veal, modification and unauthorized usage of sensitive information. The
introduction of using critical power devices for internet of things (IoTs),
e-commerce, e-payment, and wireless sensor networks (WSNs) has brought
a new challenge of security due to the low computation capability of sen-
sors. Therefore, the lightweight authenticated key agreement protocols
are important to protect their security and privacy. Several researches
have been published about authenticated key agreement. However, there
is a need of lightweight schemes that can fit with critical capability de-
vices. Addition to that, a malicious key generation center (KGC) can
become a threat to watch other users, i.e impersonate user by causing
the key escrow problem
Call for Papers - Computer Science & Engineering: An International Journal (C...CSEIJJournal
Computer Science & Engineering: An International Journal (CSEIJ) is a bi-monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Computer Science & Computer Engineering. The journal is devoted to the publication of high quality papers on theoretical and practical aspects of computer science and computer Engineering.
Recommendation System for Information Services Adapted, Over Terrestrial Digi...CSEIJJournal
The development of digital television in Colombia has grown in last year’s, specially the digital terrestrial
television (DTT), which is an essential part to the projects of National Minister of ICT, thanks to the big
distribution and use of the television network and Internet in the country. This article explains how joining
different technologies like social networks, information adaptation and DTT, to get an application that
offers information services to users, based on their data, preferences, inclinations, use and interaction with
others users and groups inside the network.
Reconfiguration Strategies for Online Hardware Multitasking in Embedded SystemsCSEIJJournal
An intensive use of reconfigurable hardware is expected in future embedded systems. This means that the
system has to decide which tasks are more suitable for hardware execution. In order to make an efficient
use of the FPGA it is convenient to choose one that allows hardware multitasking, which is implemented by
using partial dynamic reconfiguration. One of the challenges for hardware multitasking in embedded
systems is the online management of the only reconfiguration port of present FPGA devices. This paper
presents different online reconfiguration scheduling strategies which assign the reconfiguration interface
resource using different criteria: workload distribution or task’ deadline. The online scheduling strategies
presented take efficient and fast decisions based on the information available at each moment. Experiments
have been made in order to analyze the performance and convenience of these reconfiguration strategies.
Performance Comparison and Analysis of Mobile Ad Hoc Routing ProtocolsCSEIJJournal
A mobile ad hoc network (MANET) is a wireless network that uses multi-hop peer-to-peer routing instead
of static network infrastructure to provide network connectivity. MANETs have applications in rapidly
deployed and dynamic military and civilian systems. The network topology in a MANET usually changes
with time. Therefore, there are new challenges for routing protocols in MANETs since traditional routing
protocols may not be suitable for MANETs. Researchers are designing new MANET routing protocols
and comparing and improving existing MANET routing protocols before any routing protocols are
standardized using simulations. However, the simulation results from different research groups are not
consistent with each other. This is because of a lack of consistency in MANET routing protocol models
and application environments, including networking and user traffic profiles. Therefore, the simulation
scenarios are not equitable for all protocols and conclusions cannot be generalized. Furthermore, it is
difficult for one to choose a proper routing protocol for a given MANET application. According to the
aforementioned issues, this paper focuses on MANET routing protocols. Specifically, my contribution
includes the characterization of different routing protocols and compare and analyze the performance of
different routing protocols.
Adaptive Stabilization and Synchronization of Hyperchaotic QI SystemCSEIJJournal
The hyperchaotic Qi system (Chen, Yang, Qi and Yuan, 2007) is one of the important models of four-
dimensional hyperchaotic systems. This paper investigates the adaptive stabilization and synchronization
of hyperchaotic Qi system with unknown parameters. First, adaptive control laws are designed to
stabilize the hyperchaotic Qi system to its equilibrium point at the origin based on the adaptive control
theory and Lyapunov stability theory. Then adaptive control laws are derived to achieve global chaos
synchronization of identical hyperchaotic Qi systems with unknown parameters. Numerical simulations
are shown to demonstrate the effectiveness of the proposed adaptive stabilization and synchronization
schemes.
An Energy Efficient Data Secrecy Scheme For Wireless Body Sensor NetworksCSEIJJournal
Data secrecy is one of the key concerns for wireless body sensor networks (WBSNs). Usually, a data
secrecy scheme should accomplish two tasks: key establishment and encryption. WBSNs generally face
more serious limitations than general wireless networks in terms of energy supply. To address this, in this
paper, we propose an energy efficient data secrecy scheme for WBSNs. On one hand, the proposed key
establishment protocol integrates node IDs, seed value and nonce seamlessly for security, then
establishes a session key between two nodes based on one-way hash algorithm SHA-1. On the other hand,
a low-complexity threshold selective encryption technology is proposed. Also, we design a security
selection patter exchange method with low-complexity for the threshold selection encryption. Then, we
evaluate the energy consumption of the proposed scheme. Our scheme shows the great advantage over
the other existing schemes in terms of low energy consumption.
To improve the QoS in MANETs through analysis between reactive and proactive ...CSEIJJournal
A Mobile Ad hoc NETwork (MANET), is a self-configuring infra structure less network of mobile devices
connected by wireless links. ad hoc is Latin and means "for this purpose". Each device in a MANET is free
to move independently in any direction, and will therefore change its links to other devices frequently. Each
must forward traffic unrelated to its own use, and therefore be a router. The primary challenge in building
a MANET is equipping each device to continuously maintain the information required to properly route
traffic. QOS is defined as a set of service requirements to be met by the network while transporting a
packet stream from source to destination. Intrinsic to the notion of QOS is an agreement or a guarantee by
the network to provide a set of measurable pre-specified service attributes to the user in terms of delay,
jitter, available bandwidth, packet loss, and so on. The analysis is mainly between proactive or table-driven
protocols like OLSR (Optimized Link State Routing) viz DSDV (Destination Sequenced Distance Vector) &
CGSR (Cluster Head Gateway Switch Routing) and reactive or source initiated routing protocols viz
AODV (Ad hoc on Demand distance Vector) & DSR (Dynamic Source Routing). The QoS analysis of the
above said protocols is simulated on NS2 and results are shown thereby.
This paper introduces Topic Tracking for Punjabi language. Text mining is a field that automatically
extracts previously unknown and useful information from unstructured textual data. It has strong
connections with natural language processing. NLP has produced technologies that teach computers
natural language so that they may analyze, understand and even generate text. Topic tracking is one of the
technologies that has been developed and can be used in the text mining process. The main purpose of topic
tracking is to identify and follow events presented in multiple news sources, including newswires, radio and
TV broadcasts. It collects dispersed information together and makes it easy for user to get a general
understanding. Not much work has been done in Topic tracking for Indian Languages in general and
Punjabi in particular. First we survey various approaches available for Topic Tracking, then represent our
approach for Punjabi. The experimental results are shown.
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Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
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COLLEGE BUS MANAGEMENT SYSTEM PROJECT REPORT.pdfKamal Acharya
The College Bus Management system is completely developed by Visual Basic .NET Version. The application is connect with most secured database language MS SQL Server. The application is develop by using best combination of front-end and back-end languages. The application is totally design like flat user interface. This flat user interface is more attractive user interface in 2017. The application is gives more important to the system functionality. The application is to manage the student’s details, driver’s details, bus details, bus route details, bus fees details and more. The application has only one unit for admin. The admin can manage the entire application. The admin can login into the application by using username and password of the admin. The application is develop for big and small colleges. It is more user friendly for non-computer person. Even they can easily learn how to manage the application within hours. The application is more secure by the admin. The system will give an effective output for the VB.Net and SQL Server given as input to the system. The compiled java program given as input to the system, after scanning the program will generate different reports. The application generates the report for users. The admin can view and download the report of the data. The application deliver the excel format reports. Because, excel formatted reports is very easy to understand the income and expense of the college bus. This application is mainly develop for windows operating system users. In 2017, 73% of people enterprises are using windows operating system. So the application will easily install for all the windows operating system users. The application-developed size is very low. The application consumes very low space in disk. Therefore, the user can allocate very minimum local disk space for this application.
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Event Management System Vb Net Project Report.pdfKamal Acharya
In present era, the scopes of information technology growing with a very fast .We do not see any are untouched from this industry. The scope of information technology has become wider includes: Business and industry. Household Business, Communication, Education, Entertainment, Science, Medicine, Engineering, Distance Learning, Weather Forecasting. Carrier Searching and so on.
My project named “Event Management System” is software that store and maintained all events coordinated in college. It also helpful to print related reports. My project will help to record the events coordinated by faculties with their Name, Event subject, date & details in an efficient & effective ways.
In my system we have to make a system by which a user can record all events coordinated by a particular faculty. In our proposed system some more featured are added which differs it from the existing system such as security.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
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Courier management system project report.pdfKamal Acharya
It is now-a-days very important for the people to send or receive articles like imported furniture, electronic items, gifts, business goods and the like. People depend vastly on different transport systems which mostly use the manual way of receiving and delivering the articles. There is no way to track the articles till they are received and there is no way to let the customer know what happened in transit, once he booked some articles. In such a situation, we need a system which completely computerizes the cargo activities including time to time tracking of the articles sent. This need is fulfilled by Courier Management System software which is online software for the cargo management people that enables them to receive the goods from a source and send them to a required destination and track their status from time to time.
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CONVOLUTIONAL NEURAL NETWORK BASED RETINAL VESSEL SEGMENTATION
1. Computer Science & Engineering: An International Journal (CSEIJ), Vol 12, No 6, December 2022
DOI:10.5121/cseij.2022.12613 135
CONVOLUTIONAL NEURAL NETWORK BASED
RETINAL VESSEL SEGMENTATION
Savithadevi M
Research Scholar, National Institute of Technology, Tiruchirappalli
ABSTRACT
In human eye, the state of the blood vessel is a crucial diagnostic factor. The segmentation of blood vessel
from the fundus image is difficult due to the spatial complexity, adjacency, overlapping and variability of
blood vessel. The detection of ophthalmic pathologies like hypertensive disorders, diabetic retinopathy and
cardiovascular diseases are remain challenging task due to the wide-ranging distribution of blood vessels.
In this paper, Stacked Autoencoder and CNN (Convolutional Neural Network) technique is proposed to
extract the blood vessel from the fundus image. Based on the experiments conducted using the Stacked
Autoencoder and Convolutional Neural Network gives 90% & 95% accuracy for segmentation.
KEYWORDS
Stacked Autoencoder, Convolutional Neural Network (CNN), retinopathy, blood vessel, supervised
learning method.
1. INTRODUCTION
The primary goals of supervised learning methods are to retrieve typically important features
from labeled data, identify and eliminate input redundancies, and preserve only the most
important aspects of the information in robust and exclusionary representations. Many scientific
and commercial applications have also routinely utilized supervised methods. [1]. For developing
a robust screening system for diabetic retinopathy, the automatic analysis of retinal vessel
topography is used [2]. A Convolutional Neural Network (CNN) is a standard multi-layer neural
network that consists of one or even more convolution layers, a pooling layer, and optionally one
or more fully connected layers. Another advantage of CNN is it consists of a small number of
units compared to the other deep networks which consist of many hidden layers. Autoencoder is a
supervised technique where the number of inputs and targets might be given. It is trained to copy
its input to its output and it also consists of hidden layers. Autoencoder is a network that mainly
consists of two parts namely an encoder function and a decoder function. The main application of
the autoencoder is dimensionality reduction, classification, and information retrieval.
2. LITERATURE REVIEW
In 2010, quinmu et al, presented a method to locate the vessel’s central lines using the radial
projection method. For the extraction of the major structure of blood vessels, the supervised
classification method is used. Marin et al (2011), proposed a supervised neural network-based
technique. For training and classification, a neural network with multilayer feed-forward is
utilized. In different conditions with multiple images, this method improves the robustness.
Holbura.et.al (2012), presented a new method by combining Support Vector Machine (SVM) and
neural network technique over the same feature set. The weighted decision fusion is used to
improve classification accuracy. In 2014 Mehrotra et al, presented a method in which they use
2. Computer Science & Engineering: An International Journal (CSEIJ), Vol 12, No 6, December 2022
136
morphological methods such as top and bottom hat transformations to highlight the blood vessels
in the retinal image.Tan.et.al., (2017) proposed a method of supervised learning that occurs
spontaneously, segments, and effusion the hemorrhages and micro-aneurysms, which uses
ground truth data for segmenting vessels. Lin.et.al., (2018) present a work in which a deep
learning method with conditional random field and holistically-nested edge detection is added to
perform the vessel segmentation in the retinal image.
Literature says that still there is a hope for improving accuracy.
3. PROPOSED METHOD
In this work, propose Convolutional Neural Network and a Stacked Autoencoder based
supervised learning method to extract the blood vessel from the fundus image. In this let’s
consider several pre-processing techniques to accurately recognize the blood artery in the fundus
image. The proposed method can outperform the existing methods based on the accuracy of
classification. Figure 1 depicts the suggested method's architecture.
Fig.1. Architecture of the overall proposed system
3.1. Dataset Description
The images for the DRIVE database come from a screening test in the Netherlands for
diabetes mellitus. 400 diabetic patients aged 25 to 90 make up the screening
population. A total of 40 images were chosen at random, from which 33 showed no
symptoms of diabetes mellitus and 7 showed mild initial symptoms. JPEG has been
employed to compress each image.
3.2. Patch Extraction
From the original image (Fig 2), the patch size of b x b (where b=27) is being extracted
to process the image. The patch can be extracted in random and sequential ways in the
DRIVE database. For training, it generates randomly 900 patches from the training
image. While for testing, 400 patches from the test image are generated sequentially.
Using binary classification, every pixel is assigned with a positive value of 1 (for vessel
pixel) and a negative value (for non-vessel pixel). The pixel centered can be identified
and based on that center the pixel around it can be extracted from the fundus image of
3. Computer Science & Engineering: An International Journal (CSEIJ), Vol 12, No 6, December 2022
137
size 27X27. Since it is an RGB image it has 3 channels. The same process is performed
in all the channels in the image and extracts 27X27X3 size of the patch.
3.3. Global Contrast Normalization
It can be seen that brightness varies amongst the images in the collection by looking at
the image in the DRIVE database. Global contrast normalization is employed to get
around problem. This means that the standard deviation of each patch's components is
divided by the mean, which isthen removed from the result.
3.4. Zero phase Component Analysis (zca whitening)
Input is typically "whitened" to reduce repetition. The neighboring pixel in the input
image are almost correlated to each other. So, in order to remove the redundancy in the
input image, ZCA whitening is applied in the image. In this an epsilon value of 0.001 is
assigned in order to avoid the data elements to be divided by zero.
Let the centered pixel is stored in the data matrix A with data points in row and features in
columns. Then A has the same number of rows as samples and columns as 3 X 27 X 27=2187 is
equal to features. The covariance matrix the diagonal of ᴧ has eigen values and
the columns of N has eigenvectors, so that is represented as,
Σ = 𝑁Λ𝑁𝑇. Then N be an orthogonal matrix (rotation/reflection) and 𝑁𝑇 provides a
rotation necessary to decorrelate the data.
The general PCA whitening is given by
𝑋𝑃𝐶𝐴=⋀−1/2
𝑁𝑇 ------------------- (1)
The ZCA whitening is 𝑋𝑃𝐶𝐴 = 𝑁 𝑋𝑃𝐶A then it can be represented as
𝑋𝑍𝐶𝐴=𝑁 ⋀−1/2
𝑁𝑇 = Σ−1/2 -------------------- (2)
3.5. Model Construction
3.5.1. Convolutional Neural Network (cnn)
The convolutional neural network is divided into two stages with various layers that generate low
and generally high features. In contrast to traditional convolutional neural networks, layer-
skipping in proposed network. (i.e., the classifier is inputted by the output of the two stages), This
allows the classifier to use both high-level global and low-level local features. The relatively high
features generate holistic explanations of the blood vessel, while the low-level features aim to
accurately recognize the blood vessel. Both of them are advantageous for Retinal image
classification.
Semi-supervised convolutional neural networks are proposed. Sparse Laplacian filter studying is
used to learn the network's filters with such a great number of unsupervised patches in order to
obtain deep as well as distinct data on blood vessels. The output layer is the soft-max classifier
layer, which was trained using multi-task learning with a small number of labelled patches. The
network can automatically learn good features to classify the blood vessel type for a given retinal
image. By choosing the label with greatest chance, the category of patch could be predicted.
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Fig.2. CNN Architecture
3.5.2. CNN architecture for vessel identification
Convolution is a mathematical term that describes the method of repeatedly applying one
function across the output of another function. It means applying a 'filter' to that of an image
throughout all feasible offsets in this context. A filter is composed of a layer of weight vectors,
with the input resembling a length of 2 patches and thus the output resembling a single unit.
Since this filter is used repeatedly, the probable results in connectivity look like a sequence of
interlinking receptive fields that map to a matrix of filter outputs.
3.5.3. Local Connectivity
It is impractical to connect neurons in the previous quantity to all neurons once dealing with
multi-dimensional inputs such as images. Rather than, each neuron is only attached to a subset of
input volume. The spatial extent of this connectivity is a hyperparameter known as the neuron's
receptive field.
3.5.4. Max Pooling Layer
The stride size, pool size, adding, and layer name contain the maximum pooling layer. Down
sampling is carried out by max pooling layer by splitting the input into rectangular pooling areas
and estimating the maximum of every region.
3.5.5. Fully Connected Layer
All neurons connected to the fully connected layer connect all neurons in the previous layer
(whether pooling, fully connected, or convolutional) to each neuron it has. The class scores will
be calculated by the fully-connected layer, resulting in values assigned to a class score.
3.5.6. Softmax Layer
The output layer of the convolutional neural network is the softmax classifier. The softmax
classification layer receives the feature vector obtained by the previous layers as input and then
outputs the based-on probability vector. The final feature vector is fed to the softmax layer,
which differentiates between vessel and non-vessel patches.
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Function BACK-PROPAGATION-UPDATE (Network(N), examples, a) returns a network with
modified weights
Autoencoder
3.5.7. Training Single Layer Autoencoder
From the DRIVE database, 900 patches are extracted from each training image of 20
images. Thus train an autoencoder with 18,000 patches of size 27 x 27 x 3 given to the
input layer of an autoencoder as a row vector. The encoding and decoding function is
used to rebuild the input image with minimized reconstruction error which can be
calculated by cross entropy using Stochastic Gradient Descent (SGD). For encoding,
the logistic sigmoid function is used and for decoding, purelin function is used. In
autoencoder the input and target value must be the same.
If an autoencoder obtains a vector x ∈ 𝑅𝐷x , as input, the encoder routes the vector x
with another vector Z ∈ 𝑅𝐷(1)
as follows:
Z(1)
= ℎ (1)
(𝑊(1)
𝑥 + 𝑏(1)
) ----------------------------- (3)
The first layer is noted by the superscript (1). ℎ (1)
: 𝑅𝐷(1)
→ 𝑅𝐷(1)
is a transfer function
for the encoder, 𝑊(1)
∈ 𝑅𝐷(1)𝑋 𝐷(𝑥)
is a weight matrix, and 𝑏(1)
∈ 𝑅𝐷(1)
is a bias vector. The
decoder then maps the encoded representation z back into an approximate of the
original image vector, x, as follows:
𝑥
̂ = ℎ (2)
(𝑤(2)
𝑥 + 𝑏(2)
) --------------------------------------- (4)
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The superscript (2) denotes the second layer
ℎ (2)
: 𝑅𝐷𝑥→ 𝑅𝐷𝑥 is the decoder's transfer function,
W(1)
∈ 𝑅𝐷𝑥× 𝐷(1)
is a weight matrix, and
b(2)
∈ 𝑅𝐷𝑥 is a bias vector.
Fig.3. Stacked Autoencoder
3.5.8. Training the Second Layer Autoencoder
The stacked autoencoder can be obtained by serially arranging the layers such as the first layer's
output is fed into the second layer's input, and so on. Thus, the latent characteristics from the
initial autoencoder are fed through into second layer as input. This process will go on for the
subsequent hidden layers. Finally, the last layer, latent feature is considered as a final feature
vector for the input image.
Testing
Following DRIVE database training, the test image is given as an input to the autoencoder
network for testing and then the accuracy of correct prediction of the vessel and non-vessel
patches from the test image is found. The accuracy rate can be predicted using a confusion
matrix.
The test image is divided into patches. The image can be divided into patches sequentially in
order for testing. The tested image result is constructed as a matrix. A value 1 for is assigned for
the patch that is correctly classified as vessel and a value 0 for the patch that is not classified as
vessel patch. Likewise, a 20 X 20 matrix for the whole image is obtained. After this construct a
mask for the value 1 and value 0. For the value 0, multiply the corresponding image patch with
zeros and for value 1, multiply the corresponding image patch with ones.
4. PERFORMANCE EVALUATION
The performance can be measured by using
True positive: A true positive test result is one that detects a condition when it exists.
True negative: A true negative test result is one that does not detect the condition when it is not
present.
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False positive: A false positive test result is one that detects a condition that does not exist.
False negative: A false negative test result is one that fails to detect a condition when it exists.
The performance of networks tested on benchmark-specific test sets in terms of area under
accuracy. (Acc), sensitivity (Sens), specificity (Spec), defined as
4.1. Experimental Results and Discussion
In matlab 2016a, experiments are conducted by implementing the algorithm using the following
functions softmax, conv, pool, encoder and decoder functions.
A sample image from DRIVE database in which patch extraction, GCN, ZCA is performed is
shown in Fig.4.
Fig.4. Sample image from DRIVE database Fig.5. Training patches extracted from the DRIVE
Database
Patches of size 27 x 27 extracted from the sample image in DRIVE database is shown in the
Fig.5.
Fig.6. Training patches after applying GCN transformation Fig.7. Training patches after applying
ZCN whitening Transformation
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Experiments are carried out by altering the hidden units during training, with the results
summarized in Table 1. The results demonstrate that raising the hidden units size increases the
accuracy to a certain level, but after enhancing the hidden unit’s size to 10, the accuracy
decreases. So, the hidden layer size is fixed as 7.
Table 1. Performance of training and testing with various hidden layers using Autoencode
Hidden
layer
Best training
epoch
Training data
classification accuracy
Testing data classification
accuracy
HL=5 Epoch=203
Correctly classified
=70.7%
Correctly classified
=55.0%
HL=7 Epoch=520
Correctly classified
=90.7% Correctly classified =90.0%
HL=10 Epoch=520
Correctly classified
=90.7%
Correctly classified
=57.0%
HL=15 Epoch=388
Correctly classified
=90.7% Correctly classified =70.0%
Experiments are carried out in the network by changing the number of both training and testing
patches, and the results are presented in Table 2. The accuracy will increase when the training
samples is increased and the accuracy will decrease when the training patches get decreased.
Table 2. Performance of training and testing with various patches using Autoencoder
Training images
represented in
patches
Best training
epoch
Training data classification
accuracy
Testing data
classification
accuracy
70% -training
30%-testing
Epoch=96 Correctly classified =99.0% Correctly classified
=81.8%
60%-training
40%-testing
Epoch=92 Correctly classified =99.0% Correctly classified
=53.0%
80%-training
20%-testing
Epoch=1000 Correctly classified =90.7% Correctly classified
=75.0%
Table 3. CNN architecture
Name Size Kernel size
convolution 25x25x5 (3,3,3,5)
Max Pooling 24x24x5 2 x 2
Convolution 20x20x10 (5,5,5,10)
Max Pooling 19x19x10 2 x 2
Convolution 18x18x2 (2,2,10,2)
Softmax 18x18x2 ------
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Table 4. Performance of training and testing with various patches using CNN
No. of training
images
Minimum
error
epoch
Training data
classification
accuracy
Testing data
classification
accuracy
70% -
training30%-
testing
Epoch=70 Correctly
classified
=99.0%
Correctly
classified
=88.8%
60%-
training40%-
testing
Epoch=70 Correctly
classified
=99.0%
Correctly
classified
=73.0%
80%-
training20%-
testing
Epoch=70 Correctly
classified
=99.0%
Correctly
classified
=95.0%
The proposed method performance is compared with existing literature and it is tabulated in
Table 5.
After constructing model test images are divided into patches and vessel patches are identified for
model construction.
Then, the mask is multiplied with test image and the vessel are extracted from it. Finally, the
extracted vessel from the original image can be obtained and shown in Fig 8 & 9.
Autoencoder model is constructed using 1800 patches and tested with 400 patches. Accuracy
obtained is 90% by varying the hidden layers. Convolutional Neural Network model is also
constructed using 1800 patches and tested with 400 patches. Accuracy obtained is 95% by
varying the training patches. In future more, number of patches can be trained to improve
accuracy.
Fig.8. Segmented blood vessel from the test image using Autoencoder
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Fig.9. Segmented blood vessel from the test image using CNN
5. CONCLUSION AND FUTURE WORK
The segmentation of blood vessels from image data is a difficult issue in medical imaging. The
learned features of the network are discriminative enough just to perform well even in complex
background. It should be observed that the proposed model outperformed other methods despite
the absence of blood vessels. Basically, CNN requires higher processing systems since it extracts
a greater number of features which is very useful in image classification, whereas Autoencoder
does not require such high-end systems. The performance of extraction task was examined using
Convolutional Neural Network technique and stacked auto encoder technique and achieved the
accuracy of 95% & 90%. Young radiologist can use this as a tool for cross reference their initial
prediction. In future several deep networks concept can be applied to improve the accuracy.
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