Abstract
In this paper, an attempt has been made to summarize the multi-resolution transformation and the different classifiers useful to
analyze the brain tumor using MRI. X-ray, MRI, Ultrasound etc. are different techniques used to scan brain tumor images.
Radiologist prefers MRI to get detail information about tumor to help him diagnoses. In this paper we have used MRI of brain
tumor for analysis. We have used Digital image processing tool for detection of the tumor. The identification, detection and
classification of brain tumor have been done by extracting features from MRI with the help of wavelet transformation. The MRI of
brain tumor is classified into two categories normal and abnormal brain. In this work Digital image processing has been used as
a tool for getting clear and exact details about tumor in earlier stages. This helps the physicians and practitioners for diagnoses.
Key word – Brain tumor, Wavelet transform, segmentation.
details about brain tumor
literature survey on many reference papers related to brain tumor detection using various techniques
our proposed novel methodology for brain tumor detection
Performance analysis of automated brain tumor detection from MR imaging and CT scan using basic image processing techniques based on various hard and soft computing has been performed in our work. Moreover, we applied six traditional classifiers to detect brain tumor in the images. Then we applied CNN for brain tumor detection to include deep learning method in our work. We compared the result of the traditional one having the best accuracy (SVM) with the result of CNN. Furthermore, our work presents a generic method of tumor detection and extraction of its various features.
BRAIN TUMOR MRI IMAGE SEGMENTATION AND DETECTION IN IMAGE PROCESSINGDharshika Shreeganesh
Image processing is an active research area in which medical image processing is a highly challenging field. Medical imaging
techniques are used to image the inner portions of the human body for medical diagnosis. Brain tumor is a serious life altering
disease condition. Image segmentation plays a significant role in image processing as it helps in the extraction of suspicious regions
from the medical images. In this paper we have proposed segmentation of brain MRI image using K-means clustering algorithm
followed by morphological filtering which avoids the misclustered regions that can inevitably be formed after segmentation of the brain MRI image for detection of tumor location.
details about brain tumor
literature survey on many reference papers related to brain tumor detection using various techniques
our proposed novel methodology for brain tumor detection
Performance analysis of automated brain tumor detection from MR imaging and CT scan using basic image processing techniques based on various hard and soft computing has been performed in our work. Moreover, we applied six traditional classifiers to detect brain tumor in the images. Then we applied CNN for brain tumor detection to include deep learning method in our work. We compared the result of the traditional one having the best accuracy (SVM) with the result of CNN. Furthermore, our work presents a generic method of tumor detection and extraction of its various features.
BRAIN TUMOR MRI IMAGE SEGMENTATION AND DETECTION IN IMAGE PROCESSINGDharshika Shreeganesh
Image processing is an active research area in which medical image processing is a highly challenging field. Medical imaging
techniques are used to image the inner portions of the human body for medical diagnosis. Brain tumor is a serious life altering
disease condition. Image segmentation plays a significant role in image processing as it helps in the extraction of suspicious regions
from the medical images. In this paper we have proposed segmentation of brain MRI image using K-means clustering algorithm
followed by morphological filtering which avoids the misclustered regions that can inevitably be formed after segmentation of the brain MRI image for detection of tumor location.
Brain Tumor Segmentation using Enhanced U-Net Model with Empirical AnalysisMD Abdullah Al Nasim
Cancer of the brain is deadly and requires careful surgical segmentation. The brain tumors were segmented using U-Net using a Convolutional Neural Network (CNN). When looking for overlaps of necrotic, edematous, growing, and healthy tissue, it might be hard to get relevant information from the images. The 2D U-Net network was improved and trained with the BraTS datasets to find these four areas. U-Net can set up many encoder and decoder routes that can be used to get information from images that can be used in different ways. To reduce computational time, we use image segmentation to exclude insignificant background details. Experiments on the BraTS datasets show that our proposed model for segmenting brain tumors from MRI (MRI) works well. In this study, we demonstrate that the BraTS datasets for 2017, 2018, 2019, and 2020 do not significantly differ from the BraTS 2019 dataset's attained dice scores of 0.8717 (necrotic), 0.9506 (edema), and 0.9427 (enhancing).
deep learning applications in medical image analysis brain tumorVenkat Projects
The tremendous success of machine learning algorithms at image recognition tasks in recent years intersects with a time of dramatically increased use of electronic medical records and diagnostic imaging. This review introduces the machine learning algorithms as applied to medical image analysis, focusing on convolutional neural networks, and emphasizing clinical aspects of the _eld. The advantage of machine learning in an era of medical big data is that signi_cant hierarchal relationships within the data can be discovered algorithmically without laborious hand-crafting of features. We cover key research areas and applications of medical image classi_cation, localization, detection, segmentation, and registration. We conclude by discussing research obstacles, emerging trends, and possible future directions.
Applying Deep Learning to Transform Breast Cancer DiagnosisCognizant
Deep convolutional neural networks can assist pathologists in breast cancer diagnosis by automatically filtering benign tissue biopsies, identifying malignant regions and labeling important cellular features like nuclei for further analysis. Automatic detection of diagnostically relevant regions-of-interest and nuclei segmentation reduces the pathologist’s workload, while ensuring that no critical region is overlooked, rendering breast cancer diagnosis more reliable, efficient and cost-effective.
Brain tumor detection with the mri image and 54900 image Performance analysis of automated brain tumor detection from MR imaging and CT scan using basic image processing techniques based on various hard and soft .This repo is of segmentation and morphological operations which are the basic concepts of image processing. Detection and extraction of tumor from MRI scan images of the brain is done using python.
Adaptive K-Means Clustering Algorithm for MR Breast Image Segmentation
3D Brain Tumor Segmentation Scheme using K-mean Clustering and Connected Component Labeling Algorithms
Volume Identification and Estimation of MRI Brain Tumor
MRI Breast cancer diagnosis hybrid approach using adaptive Ant-based segmentation and Multilayer Perceptron NN classifier
This talk will cover various medical applications of deep learning including tumor segmentation in histology slides, MRI, CT, and X-Ray data. Also, more complicated tasks such as cell counting where the challenge is to count how many objects are in an image. It will also cover generative adversarial networks and how they can be used for medical applications. This presentation is accessible to non-doctors and non-computer scientists.
http://imatge-upc.github.io/telecombcn-2016-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.
Prospects of Deep Learning in Medical ImagingGodswll Egegwu
A SEMINAR Presentation on the Prospects of Deep Learning in Medical Imaging Presented to the Department of Computer Science, Nasarawa State Polytechnic, Lafia.
BY:
EGEGWU, GODSWILL
08166643792
http://facebook.com/godswill.egegwu
http://egegwugodswill.name.ng
Talk @ ACM SF Bayarea Chapter on Deep Learning for medical imaging space.
The talk covers use cases, special challenges and solutions for Deep Learning for Medical Image Analysis using Tensorflow+Keras. You will learn about:
- Use cases for Deep Learning in Medical Image Analysis
- Different DNN architectures used for Medical Image Analysis
- Special purpose compute / accelerators for Deep Learning (in the Cloud / On-prem)
- How to parallelize your models for faster training of models and serving for inferenceing.
- Optimization techniques to get the best performance from your cluster (like Kubernetes/ Apache Mesos / Spark)
- How to build an efficient Data Pipeline for Medical Image Analysis using Deep Learning
- Resources to jump start your journey - like public data sets, common models used in Medical Image Analysis
Intro to Deep Learning for Medical Image Analysis, with Dan Lee from Dentuit AISeth Grimes
Dan Lee from Dentuit AI presented an Intro to Deep Learning for Medical Image Analysis at the Maryland AI meetup (https://www.meetup.com/Maryland-AI), May 27, 2020. Visit https://www.youtube.com/watch?v=xl8i7CGDQi0 for video.
Brain Tumor Detection Using Image ProcessingSinbad Konick
The process of brain tumor detection using various filters and finding out the best possible approach. Processing the image and using other filters and find out the result.
Machine Learning - Breast Cancer DiagnosisPramod Sharma
Machine learning is helping in making smart decisions faster. In this presentation measurements carried out on FNAC was analysed. The results were validated using 20 percent of the data. The data used for POC is from UCI Repository/
Brain Tumor Segmentation using Enhanced U-Net Model with Empirical AnalysisMD Abdullah Al Nasim
Cancer of the brain is deadly and requires careful surgical segmentation. The brain tumors were segmented using U-Net using a Convolutional Neural Network (CNN). When looking for overlaps of necrotic, edematous, growing, and healthy tissue, it might be hard to get relevant information from the images. The 2D U-Net network was improved and trained with the BraTS datasets to find these four areas. U-Net can set up many encoder and decoder routes that can be used to get information from images that can be used in different ways. To reduce computational time, we use image segmentation to exclude insignificant background details. Experiments on the BraTS datasets show that our proposed model for segmenting brain tumors from MRI (MRI) works well. In this study, we demonstrate that the BraTS datasets for 2017, 2018, 2019, and 2020 do not significantly differ from the BraTS 2019 dataset's attained dice scores of 0.8717 (necrotic), 0.9506 (edema), and 0.9427 (enhancing).
deep learning applications in medical image analysis brain tumorVenkat Projects
The tremendous success of machine learning algorithms at image recognition tasks in recent years intersects with a time of dramatically increased use of electronic medical records and diagnostic imaging. This review introduces the machine learning algorithms as applied to medical image analysis, focusing on convolutional neural networks, and emphasizing clinical aspects of the _eld. The advantage of machine learning in an era of medical big data is that signi_cant hierarchal relationships within the data can be discovered algorithmically without laborious hand-crafting of features. We cover key research areas and applications of medical image classi_cation, localization, detection, segmentation, and registration. We conclude by discussing research obstacles, emerging trends, and possible future directions.
Applying Deep Learning to Transform Breast Cancer DiagnosisCognizant
Deep convolutional neural networks can assist pathologists in breast cancer diagnosis by automatically filtering benign tissue biopsies, identifying malignant regions and labeling important cellular features like nuclei for further analysis. Automatic detection of diagnostically relevant regions-of-interest and nuclei segmentation reduces the pathologist’s workload, while ensuring that no critical region is overlooked, rendering breast cancer diagnosis more reliable, efficient and cost-effective.
Brain tumor detection with the mri image and 54900 image Performance analysis of automated brain tumor detection from MR imaging and CT scan using basic image processing techniques based on various hard and soft .This repo is of segmentation and morphological operations which are the basic concepts of image processing. Detection and extraction of tumor from MRI scan images of the brain is done using python.
Adaptive K-Means Clustering Algorithm for MR Breast Image Segmentation
3D Brain Tumor Segmentation Scheme using K-mean Clustering and Connected Component Labeling Algorithms
Volume Identification and Estimation of MRI Brain Tumor
MRI Breast cancer diagnosis hybrid approach using adaptive Ant-based segmentation and Multilayer Perceptron NN classifier
This talk will cover various medical applications of deep learning including tumor segmentation in histology slides, MRI, CT, and X-Ray data. Also, more complicated tasks such as cell counting where the challenge is to count how many objects are in an image. It will also cover generative adversarial networks and how they can be used for medical applications. This presentation is accessible to non-doctors and non-computer scientists.
http://imatge-upc.github.io/telecombcn-2016-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.
Prospects of Deep Learning in Medical ImagingGodswll Egegwu
A SEMINAR Presentation on the Prospects of Deep Learning in Medical Imaging Presented to the Department of Computer Science, Nasarawa State Polytechnic, Lafia.
BY:
EGEGWU, GODSWILL
08166643792
http://facebook.com/godswill.egegwu
http://egegwugodswill.name.ng
Talk @ ACM SF Bayarea Chapter on Deep Learning for medical imaging space.
The talk covers use cases, special challenges and solutions for Deep Learning for Medical Image Analysis using Tensorflow+Keras. You will learn about:
- Use cases for Deep Learning in Medical Image Analysis
- Different DNN architectures used for Medical Image Analysis
- Special purpose compute / accelerators for Deep Learning (in the Cloud / On-prem)
- How to parallelize your models for faster training of models and serving for inferenceing.
- Optimization techniques to get the best performance from your cluster (like Kubernetes/ Apache Mesos / Spark)
- How to build an efficient Data Pipeline for Medical Image Analysis using Deep Learning
- Resources to jump start your journey - like public data sets, common models used in Medical Image Analysis
Intro to Deep Learning for Medical Image Analysis, with Dan Lee from Dentuit AISeth Grimes
Dan Lee from Dentuit AI presented an Intro to Deep Learning for Medical Image Analysis at the Maryland AI meetup (https://www.meetup.com/Maryland-AI), May 27, 2020. Visit https://www.youtube.com/watch?v=xl8i7CGDQi0 for video.
Brain Tumor Detection Using Image ProcessingSinbad Konick
The process of brain tumor detection using various filters and finding out the best possible approach. Processing the image and using other filters and find out the result.
Machine Learning - Breast Cancer DiagnosisPramod Sharma
Machine learning is helping in making smart decisions faster. In this presentation measurements carried out on FNAC was analysed. The results were validated using 20 percent of the data. The data used for POC is from UCI Repository/
3D Segmentation of Brain Tumor ImagingIJAEMSJORNAL
A brain tumor is a collection of anomalous cells that grow in or around the brain. Brain tumors affect the humans badly, it can disrupt proper brain function and be life-threatening. In this project, we have proposed a system to detect, segment, and classify the tumors present in the brain. Once the brain tumor is identified at the very beginning, proper treatments can be done and it may be cured.
A Survey on Segmentation Techniques Used For Brain Tumor DetectionEditor IJMTER
In recent years Brain tumor is one of the most commonly found causes for death among
children and adults. Early detection of tumor is a must in order to reduce the death rate. For tumor
detection various image techniques can be used. In this paper we mainly concentrate on the images
obtained from MRI scans. In MRI images, the tumor may appear clearly, but for further treatment
the physician need to be a qualified and well experienced person. In order to help the radiologist in
detection computer-aided diagnosis was developed. The generation of a CAD system consists of
several processes and among them segmentation is considered to the most important process. Image
Segmentation is a process of partitioning an image into multiple segments. The main objective of
segmentation is to represent the image into a simplified form so as to increase the efficiency and
accuracy of the system. Therefore the segmentation of brain tumor can be considered as an important
role in the medical image process. Hence in this paper we concentrate on the recently used
segmentation techniques for the detection of tumor using MRI images.
An Efficient Brain Tumor Detection Algorithm based on Segmentation for MRI Sy...ijtsrd
A collection, or mass, of abnormal cells in the brain is called as Brain Tumor . The skull, which encloses your brain, is very rigid. Growth inside such a restricted space can cause problems. Brain tumors can be malignant or benign. Segmentation in magnetic resonance imaging (MRI) was an emergent research area in the field of medical imaging system. In this an efficient algorithm is proposed for tumor detection based on segmentation and morphological operators. Quality of scanned image is enhanced and then morphological operators are applied to detect the tumor in the scanned image. Merlin Asha. M | G. Naveen Balaji | S. Mythili | A. Karthikeyan | N. Thillaiarasu"An Efficient Brain Tumor Detection Algorithm based on Segmentation for MRI System" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-2 , February 2018, URL: http://www.ijtsrd.com/papers/ijtsrd9667.pdf http://www.ijtsrd.com/engineering/electronics-and-communication-engineering/9667/an-efficient-brain-tumor-detection-algorithm-based-on-segmentation-for-mri-system/merlin-asha-m
Comparitive study of brain tumor detection using morphological operatorseSAT Journals
Abstract
Segmentation divides an image into foreground object and the background object. In our case foreground object is brain tumor and background is CSF, white matter, and grey matter. Aim of our study is to detect the tumor and remove the background completely and compare the morphological operations that can be used for this purpose. Segmentation remains a challenging area for researchers since many segmentation methods results in over segmentation or under segmentation and hence, leads to the false interpretation of the results. The proposed work is the comparative study of the morphological segmentation methods for segmenting brain tumor from MRI images. Before segmentation, filtration process is carried out using two method, Non Local mean filter and median filter and their results are compared using MSE and PSNR. NL mean filter preserves sharp edges and fine details in an image hence, preferred over median filter. Also tumor location is identified, to get an approximate idea about the position of the tumor in the brain i.e. in which part the brain tumor is located. The tumor is identified by using different algorithms which are based on morphology such as watershed segmentation, morphological erosion, and hole filling algorithm and comparison between them is carried out based on parameters like accuracy, sensitivity and elapsed time. Each of the segmentation results are compared with the tumor obtained using interactive tool present in MATLAB R2013b.
Keywords: Brain tumor, MRI images, Image segmentation, Morphology, Erosion, Thresholding, Hole filling, Watershed segmentation
Brain tumor detection and segmentation using watershed segmentation and morph...eSAT Journals
Abstract In the field of medical image processing, detection of brain tumor from magnetic resonance image (MRI) brain scan has become one of the most active research. Detection of the tumor is the main objective of the system. Detection plays a critical role in biomedical imaging. In this paper, MRI brain image is used to tumor detection process. This system includes test the brain image process, image filtering, skull stripping, segmentation, morphological operation, calculation of the tumor area and determination of the tumor location. In this system, morphological operation of erosion algorithm is applied to detect the tumor. The detailed procedures are implemented using MATLAB. The proposed method extracts the tumor region accurately from the MRI brain image. The experimental results indicate that the proposed method efficiently detected the tumor region from the brain image. And then, the equation of the tumor region in this system is effectively applied in any shape of the tumor region. Key Words: Magnetic resonance image, skull stripping, segmentation, morphological operation, detection
Brain tumor detection and segmentation using watershed segmentation and morph...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
A Review on Brain Disorder Segmentation in MR ImagesIJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
International Journal of Modern Engineering Research (IJMER) covers all the fields of engineering and science: Electrical Engineering, Mechanical Engineering, Civil Engineering, Chemical Engineering, Computer Engineering, Agricultural Engineering, Aerospace Engineering, Thermodynamics, Structural Engineering, Control Engineering, Robotics, Mechatronics, Fluid Mechanics, Nanotechnology, Simulators, Web-based Learning, Remote Laboratories, Engineering Design Methods, Education Research, Students' Satisfaction and Motivation, Global Projects, and Assessment…. And many more.
Efficient Brain Tumor Detection Using Wavelet TransformIJERA Editor
Brain tumor detection is a challenging task and its very important to analyze the structure of the tumor correctly so a automatic method is used now a days for the detection of the tumor. This method saves time as well as it reduces the error which occurs in the method of manual detection. In this paper the tumor is detected using wavelet transform. MRI is an important tool used in many fields of medicine and is capable of generating a detailed image of any part of the human body. The tumor is segmented from the MRI images, features are extracted and then the area of the tumor is determined. PNN can successfully handle the process of brain tumor classification
Mechanical properties of hybrid fiber reinforced concrete for pavementseSAT Journals
Abstract
The effect of addition of mono fibers and hybrid fibers on the mechanical properties of concrete mixture is studied in the present
investigation. Steel fibers of 1% and polypropylene fibers 0.036% were added individually to the concrete mixture as mono fibers and
then they were added together to form a hybrid fiber reinforced concrete. Mechanical properties such as compressive, split tensile and
flexural strength were determined. The results show that hybrid fibers improve the compressive strength marginally as compared to
mono fibers. Whereas, hybridization improves split tensile strength and flexural strength noticeably.
Keywords:-Hybridization, mono fibers, steel fiber, polypropylene fiber, Improvement in mechanical properties.
Material management in construction – a case studyeSAT Journals
Abstract
The objective of the present study is to understand about all the problems occurring in the company because of improper application
of material management. In construction project operation, often there is a project cost variance in terms of the material, equipments,
manpower, subcontractor, overhead cost, and general condition. Material is the main component in construction projects. Therefore,
if the material management is not properly managed it will create a project cost variance. Project cost can be controlled by taking
corrective actions towards the cost variance. Therefore a methodology is used to diagnose and evaluate the procurement process
involved in material management and launch a continuous improvement was developed and applied. A thorough study was carried
out along with study of cases, surveys and interviews to professionals involved in this area. As a result, a methodology for diagnosis
and improvement was proposed and tested in selected projects. The results obtained show that the main problem of procurement is
related to schedule delays and lack of specified quality for the project. To prevent this situation it is often necessary to dedicate
important resources like money, personnel, time, etc. To monitor and control the process. A great potential for improvement was
detected if state of the art technologies such as, electronic mail, electronic data interchange (EDI), and analysis were applied to the
procurement process. These helped to eliminate the root causes for many types of problems that were detected.
Managing drought short term strategies in semi arid regions a case studyeSAT Journals
Abstract
Drought management needs multidisciplinary action. Interdisciplinary efforts among the experts in various fields of the droughts
prone areas are helpful to achieve tangible and permanent solution for this recurring problem. The Gulbarga district having the total
area around 16, 240 sq.km, and accounts 8.45 per cent of the Karnataka state area. The district has been situated with latitude 17º 19'
60" North and longitude of 76 º 49' 60" east. The district is situated entirely on the Deccan plateau positioned at a height of 300 to
750 m above MSL. Sub-tropical, semi-arid type is one among the drought prone districts of Karnataka State. The drought
management is very important for a district like Gulbarga. In this paper various short term strategies are discussed to mitigate the
drought condition in the district.
Keywords: Drought, South-West monsoon, Semi-Arid, Rainfall, Strategies etc.
Life cycle cost analysis of overlay for an urban road in bangaloreeSAT Journals
Abstract
Pavements are subjected to severe condition of stresses and weathering effects from the day they are constructed and opened to traffic
mainly due to its fatigue behavior and environmental effects. Therefore, pavement rehabilitation is one of the most important
components of entire road systems. This paper highlights the design of concrete pavement with added mono fibers like polypropylene,
steel and hybrid fibres for a widened portion of existing concrete pavement and various overlay alternatives for an existing
bituminous pavement in an urban road in Bangalore. Along with this, Life cycle cost analyses at these sections are done by Net
Present Value (NPV) method to identify the most feasible option. The results show that though the initial cost of construction of
concrete overlay is high, over a period of time it prove to be better than the bituminous overlay considering the whole life cycle cost.
The economic analysis also indicates that, out of the three fibre options, hybrid reinforced concrete would be economical without
compromising the performance of the pavement.
Keywords: - Fatigue, Life cycle cost analysis, Net Present Value method, Overlay, Rehabilitation
Laboratory studies of dense bituminous mixes ii with reclaimed asphalt materialseSAT Journals
Abstract
The issue of growing demand on our nation’s roadways over that past couple of decades, decreasing budgetary funds, and the need to
provide a safe, efficient, and cost effective roadway system has led to a dramatic increase in the need to rehabilitate our existing
pavements and the issue of building sustainable road infrastructure in India. With these emergency of the mentioned needs and this
are today’s burning issue and has become the purpose of the study.
In the present study, the samples of existing bituminous layer materials were collected from NH-48(Devahalli to Hassan) site.The
mixtures were designed by Marshall Method as per Asphalt institute (MS-II) at 20% and 30% Reclaimed Asphalt Pavement (RAP).
RAP material was blended with virgin aggregate such that all specimens tested for the, Dense Bituminous Macadam-II (DBM-II)
gradation as per Ministry of Roads, Transport, and Highways (MoRT&H) and cost analysis were carried out to know the economics.
Laboratory results and analysis showed the use of recycled materials showed significant variability in Marshall Stability, and the
variability increased with the increase in RAP content. The saving can be realized from utilization of recycled materials as per the
methodology, the reduction in the total cost is 19%, 30%, comparing with the virgin mixes.
Keywords: Reclaimed Asphalt Pavement, Marshall Stability, MS-II, Dense Bituminous Macadam-II
Laboratory investigation of expansive soil stabilized with natural inorganic ...eSAT Journals
Abstract
Soil stabilization has proven to be one of the oldest techniques to improve the soil properties. Literature review conducted revealed
that uses of natural inorganic stabilizers are found to be one of the best options for soil stabilization. In this regard an attempt has
been made to evaluate the influence of RBI-81 stabilizer on properties of black cotton soil through laboratory investigations. Black
cotton soil with varying percentages of RBI-81 viz., 0, 0.5, 1, 1.5, 2, and 2.5 percent were studied for moisture density relationships
and strength behaviour of soils. Also the effect of curing period was evaluated as literature review clearly emphasized the strength
gain of soils stabilized with RBI-81 over a period of time. The results obtained shows that the unconfined compressive strength of
specimens treated with RBI-81 increased approximately by 250% for a curing period of 28 days as compared to virgin soil. Further
the CBR value improved approximately by 400%. The studies indicated an increasing trend for soil strength behaviour with
increasing percentage of RBI-81 suggesting its potential applications in soil stabilization.
Influence of reinforcement on the behavior of hollow concrete block masonry p...eSAT Journals
Abstract
Reinforced masonry was developed to exploit the strength potential of masonry and to solve its lack of tensile strength. Experimental
and analytical studies have been carried out to investigate the effect of reinforcement on the behavior of hollow concrete block
masonry prisms under compression and to predict ultimate failure compressive strength. In the numerical program, three dimensional
non-linear finite elements (FE) model based on the micro-modeling approach is developed for both unreinforced and reinforced
masonry prisms using ANSYS (14.5). The proposed FE model uses multi-linear stress-strain relationships to model the non-linear
behavior of hollow concrete block, mortar, and grout. Willam-Warnke’s five parameter failure theory has been adopted to model the
failure of masonry materials. The comparison of the numerical and experimental results indicates that the FE models can successfully
capture the highly nonlinear behavior of the physical specimens and accurately predict their strength and failure mechanisms.
Keywords: Structural masonry, Hollow concrete block prism, grout, Compression failure, Finite element method,
Numerical modeling.
Influence of compaction energy on soil stabilized with chemical stabilizereSAT Journals
Abstract
Increase in traffic along with heavier magnitude of wheel loads cause rapid deterioration in pavements. There is a need to improve
density, strength of soil subgrade and other pavement layers. In this study an attempt is made to improve the properties of locally
available loamy soil using twin approaches viz., i) increasing the compaction of soil and ii) treating the soil with chemical stabilizer.
Laboratory studies are carried out on both untreated and treated soil samples compacted by different compaction efforts. Studies
show that increase in compaction effort results in increase in density of soil. However in soil treated with chemical stabilizer, rate of
increase in density is not significant. The soil treated with chemical stabilizer exhibits improvement in both strength and performance
properties.
Keywords: compaction, density, subgradestabilization, resilient modulus
Geographical information system (gis) for water resources managementeSAT Journals
Abstract
Water resources projects are inherited with overlapping and at times conflicting objectives. These projects are often of varied sizes
ranging from major projects with command areas of millions of hectares to very small projects implemented at the local level. Thus,
in all these projects there is seldom proper coordination which is essential for ensuring collective sustainability.
Integrated watershed development and management is the accepted answer but in turn requires a comprehensive framework that can
enable planning process involving all the stakeholders at different levels and scales is compulsory. Such a unified hydrological
framework is essential to evaluate the cause and effect of all the proposed actions within the drainage basins.
The present paper describes a hydrological framework developed in the form of a Hydrologic Information System (HIS) which is
intended to meet the specific information needs of the various line departments of a typical State connected with water related aspects.
The HIS consist of a hydrologic information database coupled with tools for collating primary and secondary data and tools for
analyzing and visualizing the data and information. The HIS also incorporates hydrological model base for indirect assessment of
various entities of water balance in space and time. The framework would be maintained and updated to reflect fully the most
accurate ground truth data and the infrastructure requirements for planning and management.
Keywords: Hydrological Information System (HIS); WebGIS; Data Model; Web Mapping Services
Forest type mapping of bidar forest division, karnataka using geoinformatics ...eSAT Journals
Abstract
The study demonstrate the potentiality of satellite remote sensing technique for the generation of baseline information on forest types
including tree plantation details in Bidar forest division, Karnataka covering an area of 5814.60Sq.Kms. The Total Area of Bidar
forest division is 5814Sq.Kms analysis of the satellite data in the study area reveals that about 84% of the total area is Covered by
crop land, 1.778% of the area is covered by dry deciduous forest, 1.38 % of mixed plantation, which is very threatening to the
environmental stability of the forest, future plantation site has been mapped. With the use of latest Geo-informatics technology proper
and exact condition of the trees can be observed and necessary precautions can be taken for future plantation works in an appropriate
manner
Keywords:-RS, GIS, GPS, Forest Type, Tree Plantation
Factors influencing compressive strength of geopolymer concreteeSAT Journals
Abstract
To study effects of several factors on the properties of fly ash based geopolymer concrete on the compressive strength and also the
cost comparison with the normal concrete. The test variables were molarities of sodium hydroxide(NaOH) 8M,14M and 16M, ratio of
NaOH to sodium silicate (Na2SiO3) 1, 1.5, 2 and 2.5, alkaline liquid to fly ash ratio 0.35 and 0.40 and replacement of water in
Na2SiO3 solution by 10%, 20% and 30% were used in the present study. The test results indicated that the highest compressive
strength 54 MPa was observed for 16M of NaOH, ratio of NaOH to Na2SiO3 2.5 and alkaline liquid to fly ash ratio of 0.35. Lowest
compressive strength of 27 MPa was observed for 8M of NaOH, ratio of NaOH to Na2SiO3 is 1 and alkaline liquid to fly ash ratio of
0.40. Alkaline liquid to fly ash ratio of 0.35, water replacement of 10% and 30% for 8 and 16 molarity of NaOH and has resulted in
compressive strength of 36 MPa and 20 MPa respectively. Superplasticiser dosage of 2 % by weight of fly ash has given higher
strength in all cases.
Keywords: compressive strength, alkaline liquid, fly ash
Experimental investigation on circular hollow steel columns in filled with li...eSAT Journals
Abstract
Composite Circular hollow Steel tubes with and without GFRP infill for three different grades of Light weight concrete are tested for
ultimate load capacity and axial shortening , under Cyclic loading. Steel tubes are compared for different lengths, cross sections and
thickness. Specimens were tested separately after adopting Taguchi’s L9 (Latin Squares) Orthogonal array in order to save the initial
experimental cost on number of specimens and experimental duration. Analysis was carried out using ANN (Artificial Neural
Network) technique with the assistance of Mini Tab- a statistical soft tool. Comparison for predicted, experimental & ANN output is
obtained from linear regression plots. From this research study, it can be concluded that *Cross sectional area of steel tube has most
significant effect on ultimate load carrying capacity, *as length of steel tube increased- load carrying capacity decreased & *ANN
modeling predicted acceptable results. Thus ANN tool can be utilized for predicting ultimate load carrying capacity for composite
columns.
Keywords: Light weight concrete, GFRP, Artificial Neural Network, Linear Regression, Back propagation, orthogonal
Array, Latin Squares
Experimental behavior of circular hsscfrc filled steel tubular columns under ...eSAT Journals
Abstract
This paper presents an outlook on experimental behavior and a comparison with predicted formula on the behaviour of circular
concentrically loaded self-consolidating fibre reinforced concrete filled steel tube columns (HSSCFRC). Forty-five specimens were
tested. The main parameters varied in the tests are: (1) percentage of fiber (2) tube diameter or width to wall thickness ratio (D/t
from 15 to 25) (3) L/d ratio from 2.97 to 7.04 the results from these predictions were compared with the experimental data. The
experimental results) were also validated in this study.
Keywords: Self-compacting concrete; Concrete-filled steel tube; axial load behavior; Ultimate capacity.
Evaluation of punching shear in flat slabseSAT Journals
Abstract
Flat-slab construction has been widely used in construction today because of many advantages that it offers. The basic philosophy in
the design of flat slab is to consider only gravity forces; this method ignores the effect of punching shear due to unbalanced moments
at the slab column junction which is critical. An attempt has been made to generate generalized design sheets which accounts both
punching shear due to gravity loads and unbalanced moments for cases (a) interior column; (b) edge column (bending perpendicular
to shorter edge); (c) edge column (bending parallel to shorter edge); (d) corner column. These design sheets are prepared as per
codal provisions of IS 456-2000. These design sheets will be helpful in calculating the shear reinforcement to be provided at the
critical section which is ignored in many design offices. Apart from its usefulness in evaluating punching shear and the necessary
shear reinforcement, the design sheets developed will enable the designer to fix the depth of flat slab during the initial phase of the
design.
Keywords: Flat slabs, punching shear, unbalanced moment.
Evaluation of performance of intake tower dam for recent earthquake in indiaeSAT Journals
Abstract
Intake towers are typically tall, hollow, reinforced concrete structures and form entrance to reservoir outlet works. A parametric
study on dynamic behavior of circular cylindrical towers can be carried out to study the effect of depth of submergence, wall thickness
and slenderness ratio, and also effect on tower considering dynamic analysis for time history function of different soil condition and
by Goyal and Chopra accounting interaction effects of added hydrodynamic mass of surrounding and inside water in intake tower of
dam
Key words: Hydrodynamic mass, Depth of submergence, Reservoir, Time history analysis,
Evaluation of operational efficiency of urban road network using travel time ...eSAT Journals
Abstract
Efficiency of the road network system is analyzed by travel time reliability measures. The study overlooks on an important measure of
travel time reliability and prioritizing Tiruchirappalli road network. Traffic volume and travel time were collected using license plate
matching method. Travel time measures were estimated from average travel time and 95th travel time. Effect of non-motorized vehicle
on efficiency of road system was evaluated. Relation between buffer time index and traffic volume was created. Travel time model has
been developed and travel time measure was validated. Then service quality of road sections in network were graded based on
travel time reliability measures.
Keywords: Buffer Time Index (BTI); Average Travel Time (ATT); Travel Time Reliability (TTR); Buffer Time (BT).
Estimation of surface runoff in nallur amanikere watershed using scs cn methodeSAT Journals
Abstract
The development of watershed aims at productive utilization of all the available natural resources in the entire area extending from
ridge line to stream outlet. The per capita availability of land for cultivation has been decreasing over the years. Therefore, water and
the related land resources must be developed, utilized and managed in an integrated and comprehensive manner. Remote sensing and
GIS techniques are being increasingly used for planning, management and development of natural resources. The study area, Nallur
Amanikere watershed geographically lies between 110 38’ and 110 52’ N latitude and 760 30’ and 760 50’ E longitude with an area of
415.68 Sq. km. The thematic layers such as land use/land cover and soil maps were derived from remotely sensed data and overlayed
through ArcGIS software to assign the curve number on polygon wise. The daily rainfall data of six rain gauge stations in and around
the watershed (2001-2011) was used to estimate the daily runoff from the watershed using Soil Conservation Service - Curve Number
(SCS-CN) method. The runoff estimated from the SCS-CN model was then used to know the variation of runoff potential with different
land use/land cover and with different soil conditions.
Keywords: Watershed, Nallur watershed, Surface runoff, Rainfall-Runoff, SCS-CN, Remote Sensing, GIS.
Estimation of morphometric parameters and runoff using rs & gis techniqueseSAT Journals
Abstract
Land and water are the two vital natural resources, the optimal management of these resources with minimum adverse environmental
impact are essential not only for sustainable development but also for human survival. Satellite remote sensing with geographic
information system has a pragmatic approach to map and generate spatial input layers of predicting response behavior and yield of
watershed. Hence, in the present study an attempt has been made to understand the hydrological process of the catchment at the
watershed level by drawing the inferences from moprhometric analysis and runoff. The study area chosen for the present study is
Yagachi catchment situated in Chickamaglur and Hassan district lies geographically at a longitude 75⁰52’08.77”E and
13⁰10’50.77”N latitude. It covers an area of 559.493 Sq.km. Morphometric analysis is carried out to estimate morphometric
parameters at Micro-watershed to understand the hydrological response of the catchment at the Micro-watershed level. Daily runoff
is estimated using USDA SCS curve number model for a period of 10 years from 2001 to 2010. The rainfall runoff relationship of the
study shows there is a positive correlation.
Keywords: morphometric analysis, runoff, remote sensing and GIS, SCS - method
-
Effect of variation of plastic hinge length on the results of non linear anal...eSAT Journals
Abstract The nonlinear Static procedure also well known as pushover analysis is method where in monotonically increasing loads are applied to the structure till the structure is unable to resist any further load. It is a popular tool for seismic performance evaluation of existing and new structures. In literature lot of research has been carried out on conventional pushover analysis and after knowing deficiency efforts have been made to improve it. But actual test results to verify the analytically obtained pushover results are rarely available. It has been found that some amount of variation is always expected to exist in seismic demand prediction of pushover analysis. Initial study is carried out by considering user defined hinge properties and default hinge length. Attempt is being made to assess the variation of pushover analysis results by considering user defined hinge properties and various hinge length formulations available in literature and results compared with experimentally obtained results based on test carried out on a G+2 storied RCC framed structure. For the present study two geometric models viz bare frame and rigid frame model is considered and it is found that the results of pushover analysis are very sensitive to geometric model and hinge length adopted. Keywords: Pushover analysis, Base shear, Displacement, hinge length, moment curvature analysis
Effect of use of recycled materials on indirect tensile strength of asphalt c...eSAT Journals
Abstract
Depletion of natural resources and aggregate quarries for the road construction is a serious problem to procure materials. Hence
recycling or reuse of material is beneficial. On emphasizing development in sustainable construction in the present era, recycling of
asphalt pavements is one of the effective and proven rehabilitation processes. For the laboratory investigations reclaimed asphalt
pavement (RAP) from NH-4 and crumb rubber modified binder (CRMB-55) was used. Foundry waste was used as a replacement to
conventional filler. Laboratory tests were conducted on asphalt concrete mixes with 30, 40, 50, and 60 percent replacement with RAP.
These test results were compared with conventional mixes and asphalt concrete mixes with complete binder extracted RAP
aggregates. Mix design was carried out by Marshall Method. The Marshall Tests indicated highest stability values for asphalt
concrete (AC) mixes with 60% RAP. The optimum binder content (OBC) decreased with increased in RAP in AC mixes. The Indirect
Tensile Strength (ITS) for AC mixes with RAP also was found to be higher when compared to conventional AC mixes at 300C.
Keywords: Reclaimed asphalt pavement, Foundry waste, Recycling, Marshall Stability, Indirect tensile strength.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
NO1 Uk best vashikaran specialist in delhi vashikaran baba near me online vas...Amil Baba Dawood bangali
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Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry.
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Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
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Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
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Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
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Brain tumor classification using artificial neural network on mri images
1. IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
_______________________________________________________________________________________
Volume: 04 Issue: 12 | Dec-2015, Available @ http://www.ijret.org 218
BRAIN TUMOR CLASSIFICATION USING ARTIFICIAL NEURAL
NETWORK ON MRI IMAGES
Shubhangi S. Veer (Handore)1
, Pradeep M. Patil2
1
Research Scholar, Ph.D student,JJTU , Rajasthan
handore.shubhangi@gmail.com
2
Jt. Director, Trinity College of Engg. and Research ,Pune ,Maharashtra.
patil.pm@rediffmail.com
Abstract
In this paper, an attempt has been made to summarize the multi-resolution transformation and the different classifiers useful to
analyze the brain tumor using MRI. X-ray, MRI, Ultrasound etc. are different techniques used to scan brain tumor images.
Radiologist prefers MRI to get detail information about tumor to help him diagnoses. In this paper we have used MRI of brain
tumor for analysis. We have used Digital image processing tool for detection of the tumor. The identification, detection and
classification of brain tumor have been done by extracting features from MRI with the help of wavelet transformation. The MRI of
brain tumor is classified into two categories normal and abnormal brain. In this work Digital image processing has been used as
a tool for getting clear and exact details about tumor in earlier stages. This helps the physicians and practitioners for diagnoses.
Key word – Brain tumor, Wavelet transform, segmentation.
--------------------------------------------------------------------***----------------------------------------------------------------------
1. INTRODUCTION
Brain tumor is the main cause of cancer deaths worldwide.
Brain tumor can affect people at any age. Brain tumor
increases mortality among children and adults. The brain is
one of the complex organs in the human body. There are
more than 100 billion nerves present in human brain that are
in an overlapped form. Due to such a complex structure of
the human brain, a diagnosis of the tumor area in the brain is
a challenging task. The tumor is due to uncontrollable
growth of cells in the brain. There are basically two types of
primary brain tumors that are Benign tumor and Malignant
tumor.
The tumor is small in size. The identification of the tumor is
based on their growth pattern. Benign tumor grows slowly
and it has well defined borders. It can be removed
completely by surgery and it does not spread in the spinal
cord, other parts of brain or other areas of the body. The
malignant type of tumor is fast-growing and affects the
healthy brain cells and may spread to other parts of the brain
or spinal cord. It is more harmful and may remain untreated.
So detection of such brain tumor location, identification and
classification in earlier stage is a serious issue in medical
science [1].
Imaging technology in Medicine helps doctors to observe
the interior portions of the human body for easy diagnosis.
Digital image processing is one of the tool by which it is
easier to analyze medical images in a short span of times, by
digital image processing it is possible to detect, identify and
analyze the brain tumor easily. Digital image processing
also has an advantage like reproducing original data without
any change and enhancing an image which helps the
Radiologist for diagnosis in earlier stage.
2. LITERATURE REVIEW
According to National Brain Tumor Society, people having
primary tumor are about 688,000 and according to Central
Nervous System (CNS) in the United States, 138,000 people
with malignant tumor and 550,000 with nonmalignant
tumors [2]. So classification of tumor is an important issue.
Numbers of authors have worked on this issue, which
described in this session. ShanShen et al proposed fuzzy c-
means clustering (IFCM). The proposed algorithm is based
on neighborhood attraction. It is considered that it exist
between neighboring pixels. This neighborhood attraction
depends on the pixel intensities , the spatial position of the
neighbor pixels and on neighborhood pixel structure . The
classification of tumor is done with the help of artificial
neural network (ANN) based on the similarity between
feature vectors [3]. Dina Aboul Dahab et al proposed a
technique for the enhancement an image in a spatial domain
based on direct manipulation of pixels in a neighborhood of
pixels. The enhancement in frequency domain based on the
concept of the convolution theorem and spatial filters. In
this paper author used probabilistic neural networks (PNN)
which is based on learning vector quantization (LVQ). LVQ
is a supervised competitive learning technique; it defines
object boundaries and the rules for pixels that are the nearest
neighborhood [4]. Chandrakant Biradar et al proposed an
algorithm to extract the tumor region from MRI images.
They applied DWT to decompose an image. The author
applied segmentation on DWT image to extract tumor
region. They extracted the features like size; shape and
texture to classify the type of tumor .they have used SVM
classifier for classification [5]. Atiq Islam et al proposed an
algorithm for segmentation of brain tumor MRI. Due to
complex appearance of brain in MRI, author designed
2. IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
_______________________________________________________________________________________
Volume: 04 Issue: 12 | Dec-2015, Available @ http://www.ijret.org 219
multiresolutionfractal technique to get multifractal features
form tumor segmented images. The segmentation of
proposed multiracial feature compared with Gabor features
in this paper. The classification has been done by using
multiracial features of tumor with the help of novel
DiverseAdaBoost SVM classifier to improve tumor
classification rate [6]. Meiyan Huang et al proposed the
classification framework with the help of local independent
projection into the classical classification model. The
performance evaluation of proposed LIPC classifier had
done by using two-spiral structure by authors. They also
compared two classification techniques like SRC and
Support Vector Machine (SVM) [7]. Dr. P.V. Ramaraju et al
proposed an algorithm for classification of brain tumor.
They have used MRI of brain to detect and classify a tumor.
This algorithm is based on a wavelet transformation. The
wavelet transformation is used to extract features from brain
tumor MRI. Author has used feed forward probabilistic
neural networks (PNN) to classify brain tumor based on
these extracted features. They classified MRI of brain into
benign, malignant or normal classes [8].
3. PROPOSED METHODOLOGY
The MRI of brain is represented here as a gray scale image.
These gray scale images are having intensity levels ranging
from 0-255, where 0 represents black color and 255
represents white color. Figure 1 shows a sample MRI of
brain tumor from the data base used for this work. The
database is of 72 brain MRI image.
Figure.1.Brain Tumor MRI image
The blood cells in brain are represented by white color and
remaining portion of brain is represented by different gray
color shade, whose intensity is less than 255 in MRI. Using
this basic idea we have designed our algorithm to find out
first order textural features from MRI of brain tumor and
selection of exact classifier technique from the study of
different classifiers based on the features.
In this proposed algorithm MRI image of brain as are used
as an input. These MRI images of brain are clearer than CT
scan images. Figure 2 shows various stages that are
followed for tumor declaration. The major blocks used are
I. Preprocessing.
II. Feature extraction
III. Classification
I. PREPROCESSING
Input for proposed algorithm is a brain tumor image
obtained from MRI. These images are not that much clear
for tumor declaration. So preprocessing is the first step to
make an input image more visible to human eye. At this
stage to remove the blurriness of MRI images we have used
median filter .Median filter remove the noise from MRI of
brain tumor. Median filter gives a smooth image. After
getting a smooth image, the next step is enhancement. The
image enhancement increases sharpness of this filtered
image. Here the enhancement of this filtered image has
been done using power law transform. Power log transform
gives sharper image then the input image.
II. FEAURETURE EXTRAXTION
Feature extraction is a stage where we have extracted the
features from the sharpened image, which required as an
input for classifier. In this algorithm we have used discrete
cosine transformation (DWT). It is one of the types of
wavelet transformation.
In this algorithm, two level decomposition of „Symlet‟ DWT
is used. This transform is applied to enhanced image. The
DWT divides enhanced image into four regions using low
pass filter and high pass filter bank and gives four different
coefficients like absolute coefficient, diagonal coefficient,
horizontal coefficient and vertical coefficient. The major
information of an image is present in absolute coefficients as
compare to remaining three regions. So we have selected
absolute coefficients for feature extraction. We extracted
different features like Contrast, Homogeneity, Correlation,
Energy etc. These features are used as an input to classifier
for tumor declaration.
Figure.2. Block diagram of tumor declaration
Tumor
Declaration
Image
Acquisition
Preprocessing Feature Extraction Classification
3. IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
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Volume: 04 Issue: 12 | Dec-2015, Available @ http://www.ijret.org 220
III. CLASSIFICATION
Classification is the essential step, where the brain tumor
MRI classified into cancerous and noncancerous classes.
The selection of a particular classifier is based on number of
inputs and number of outputs to the classifier, features
extracted from patterns as well as the type of input images.
There are number of classifiers can be used to classify an
images into different classes. Some classifiers are as
follows.
a. MULTILAYER PERCEPTRONS (MLP)
Multilayer perceptron‟s are layered feed forward networks.
These networks are mostly used for classification of an
image into different classes. Multilayer perceptron‟s are
useful in number of applications, where statistical
classification is required.
b. PRINCIPAL COMPONENT ANALYSIS
(PCA)
PCA is useful to approximate the original information with
the help of least number of component .PCA is useful for
approximating data with lower dimensional vectors. The
PCA approach is based on eigenvectors, which is based on
covariance matrix of an original image. By using PCA the
test image was first identified by projecting the original
image onto the eigen space to extract the corresponding set
of weights and then the comparison has done with the help
of a set of weights of an image in the training set [9].
c. SUPPORT VECTOR MACHINE (SVM)
After feature extraction from an image, classification is the
main task. The performance of classifier depends upon the
number of features, samples. There are number of classifiers
available for classification. SVM is one of the supervised
classifier which gives good results in medical diagnosis. It
gives better result in a high dimension feature space. SVM
has also been applied on different real world problems such
as face recognition, text categorization; cancer diagnosis
.This SVM classifier gives better result for classification of
binary MRI images [10].
d. PROBABLISTIC NEURAL NETWORK (PNN)
Probabilistic Neural Network is the classifier that is useful
for medical application. In some algorithms feature
extraction has done with the help of GLCM and
classification of images has done by using PNN. This
classifier gives fast and accurate classification [11].
In proposed methodology MLP classifier has used its
advantage is that it is easy to use and it can easily
approximate any gray scale input or output map. The
MATLAB and Nerosolution tools have been used for the
classification of brain tumor MRI.
4. RESULT AND DISCUSSION
In proposed algorithm classification of brain tumor has done
using MLP. MLP verified these MRI input and classified it
into Benign or Malignant tumor classes. Here MLP
designed with number of hidden and output layers with
different transfer functions and learning rules along with
various percentages of training and testing data. Some
sample results shown here are of three and five hidden
layers and with different hidden layer transfer functions like
TanhAxon, SigmoidAxon and LinearsigmoidAxon. The
output layer transfer function is Sigmoid Axon. The learning
rule at hidden layers and output layer is Levenbergmarqua.
There are 29 different MLP combinations with their results
shown here. This includes graph of mean square error
(MSE) after three runs. The table which gives information
regarding the minimum MSE, regression factor and
percentage accuracy. By referring these results it‟s easy to
verify a MLP network which gives accurate result by
considering the conditions like true positive, false positive,
true negative and false negative.
1. The results of MLP that designed with 50% database for
training and 50%database for testing a network with three
hidden layers has been shown in Figure 3 and Table 1. The
TanhAxon function at hidden layer, Sigmoid Axon
function at output layer and Levenberg learning rule has
been used to design this MLP classifier.
Figure.3. Average MSE with S.D.B. for three runs
Table.1. Performance of MLP
Best
Networks Training
Cross
Validation
Run # 1 2
Epoch # 999 999
Minimum MSE 0.006136364 1.77468E-09
Final MSE 0.006136364 1.79021E-09
Output / Desired Col1(1) Col1(0)
Col1(1) 13 6
Col1(0) 5 14
-0.1
0
0.1
0.2
0.3
1
100
199
298
397
496
595
694
793
892
991
AverageMSE
Epoch
Average MSE with Standard
Deviation Boundaries for 3 Runs
Training
+ 1 Standard
Deviation
- 1 Standard
Deviation
Cross
Validation
4. IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
_______________________________________________________________________________________
Volume: 04 Issue: 12 | Dec-2015, Available @ http://www.ijret.org 221
Performance Col1(1) Col1(0)
MSE 0.290002106 0.289712851
NMSE 1.163230668 1.162070435
MAE 0.290280969 0.289828325
Min Abs Error 2.99852E-08 8.58814E-08
Max Abs Error 1.009131705 1.004234283
r 0.420533115 0.421132249
Percent Correct 72.22222222 70
2. The results of MLP that designed with 50% database for
training and 50%database for testing a network with three
hidden layers has been shown in Figure 4 and Table 2. The
Sigmoid Axon function at hidden layer, Sigmoid Axon
function at output layer and Levenberg learning rule has
been used to design this MLP classifier.
Figure.4. Average MSE with S.D.B. for three runs
Table.2. Performance of MLP
Best
Networks Training
Cross
Validation
Run # 1 1
Epoch # 1000 1000
Minimum MSE 0.006136364 6.87844E-07
Final MSE 0.006136364 6.87844E-07
Output / Desired Col1(1) Col1(0)
Col1(1) 12 5
Col1(0) 6 15
Performance Col1(1) Col1(0)
MSE 0.284861912 0.284861912
NMSE 1.14261278 1.14261278
MAE 0.291762638 0.291762638
Min Abs Error 1.35502E-07 1.35502E-07
Max Abs Error 1.000821603 1.000821603
r 0.419999862 0.419999862
Percent
Correct 66.66666667 75
3. The results of MLP that designed with 60% database for
training and 40%database for testing a network with three
hidden layers has been shown in Figure 5 and Table 3. The
TanhAxon function at hidden layer, Sigmoid Axon
function at output layer and Levenberg learning rule has
been used to design MLP classifier.
Figure.5. Average MSE with S.D.B. for three runs
Table.3. Performance of MLP
Best
Networks Training
Cross
Validation
Run # 1 1
Epoch # 727 6
Minimum MSE 0.009878049 0.000508521
Final MSE 0.009878049 0.101250136
Output / Desired Col1(0) Col1(1)
Col1(0) 10 1
Col1(1) 5 14
Performance Col1(0) Col1(1)
MSE 0.171753813 0.176993994
NMSE 0.68701525 0.707975976
MAE 0.208805816 0.204471573
Min Abs Error 0.01867669 0.003248765
Max Abs Error 0.98049437 0.996037063
r 0.657378803 0.655903134
Percent
Correct 66.66666667 93.33333333
4. The results of MLP that designed with 70% database for
training and 30%database for testing a network with three
hidden layers has been shown in Figure 6 and Table 4.The
TanhAxon function at hidden layer, Sigmoid Axon function at
output layer and Levenberg learning rule has been used to
design this MLP classifier.
-0.1
0
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0.2
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0.4
1
100
199
298
397
496
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793
892
991
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Epoch
Average MSE with Standard
Deviation Boundaries for 3 Runs
Training
+ 1 Standard
Deviation
- 1 Standard
Deviation
Cross
Validation
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Epoch
Average MSE with Standard
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Training
+ 1 Standard
Deviation
- 1 Standard
Deviation
Cross
Validation
5. IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
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Volume: 04 Issue: 12 | Dec-2015, Available @ http://www.ijret.org 222
Figure.6. Average MSE with S.D.B.for three runs
Table.4. Performance of MLP
Best
Networks Training
Cross
Validation
Run # 1 1
Epoch # 1000 50
Minimum MSE 0.020663265 0.000487995
Final MSE 0.020663265 0.101249976
Output / Desired Col1(0) Col1(1)
Col1(0) 8 1
Col1(1) 3 10
Performance Col1(0) Col1(1)
MSE 0.159034196 0.16115271
NMSE 0.636136782 0.644610842
MAE 0.178435182 0.179954692
Min Abs Error 0.000593434 3.47955E-05
Max Abs Error 0.997620132 1.000161918
r 0.678696463 0.673600142
Percent
Correct 72.72727273 90.90909091
5. The results of MLP that designed with 80% database for
training and 20% database for testing a network with three
hidden layers has been shown in Figure 7 and Table 5.The
Linear SigmoidAxon function at hidden layer, Sigmoid
Axon function at output layer and Levenberg learning rule
has been used to design this MLP classifier.
Figure.7. Average MSE with S.D.B.for three runs
Table.5. Performance of MLP
Best
Networks Training
Cross
Validation
Run # 1 3
Epoch # 141 4
Minimum MSE 0.038974871 5.52201E-05
Final MSE 0.038974871 0.074255015
Output / Desired Col1(1) Col1(0)
Col1(1) 0 0
Col1(0) 9 6
Performance Col1(1) Col1(0)
MSE 0.585828947 0.586106719
NMSE 2.440953948 2.442111331
MAE 0.597553803 0.597578527
Min Abs Error 0.011661897 0.011327293
Max Abs Error 0.988214604 0.98850352
r 0.681737659 0.681731578
Percent Correct 0 100
6. The results of MLP that designed with 50% database for
training and 50%database for testing a network with five
hidden layers. The TanhAxon function at hidden layer has
been shown in Figure 8 and Table 6, Sigmoid Axon function
at output layer and Levenberg learning rule has been used to
design this MLP classifier.
Figure.8. Average MSE with S.D.B. for three runs
Table.6. Performance of MLP
Best
N Networks Training
Cross
Validation
Run # 1 2
Epoch # 1000 10
Minimum MSE 0.018409091 0.004710426
Final MSE 0.018409091 0.101250078
Output / Desired Col1(1) Col1(0)
Col1(1) 10 0
Col1(0) 5 23
0
0.05
0.1
0.15
0.2
0.25
1
100
199
298
397
496
595
694
793
892
991
AverageMSE
Epoch
Average MSE with Standard
Deviation Boundaries for 3 Runs
Training
+ 1 Standard
Deviation
- 1 Standard
Deviation
Cross
Validation
-0.2
-0.1
0
0.1
0.2
0.3
0.4
0.5
1
100
199
298
397
496
595
694
793
892
991
AverageMSE
Epoch
Average MSE with Standard
Deviation Boundaries for 3 Runs
Training
+ 1 Standard
Deviation
- 1 Standard
Deviation
Cross
Validation
-0.05
0
0.05
0.1
0.15
0.2
0.25
1
100
199
298
397
496
595
694
793
892
991
AverageMSE
Epoch
Average MSE with Standard
Deviation Boundaries for 3 Runs
Training
+ 1 Standard
Deviation
- 1 Standard
Deviation
Cross
Validation
6. IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
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Volume: 04 Issue: 12 | Dec-2015, Available @ http://www.ijret.org 223
Performance Col1(1) Col1(0)
MSE 0.128431286 0.127960088
NMSE 0.537550078 0.535577877
MAE 0.209681988 0.206906826
Min Abs Error 0.02899802 0.026975136
Max Abs Error 0.934267124 0.936158062
r 0.721359045 0.722186609
Percent
Correct 66.66666667 100
7. The results of MLP that designed with 60% database for
training and 40%database for testing a network with five
hidden layers has been shown in Figure 9 and Table 7. The
SigmoidAxon function at hidden layer, Sigmoid Axon
function at output layer and Levenberg learning rule has
been used to design this MLP classifier.
Figure.9. Average MSE with S.D.B. for three runs
Table.7. Performance of MLP
Best
Networks Training
Cross
Validation
Run # 1 1
Epoch # 999 1000
Minimum MSE 0.004939024 3.08311E-13
Final MSE 0.004939024 3.08311E-13
Output / Desired Col1(0) Col1(1)
Col1(0) 15 6
Col1(1) 4 5
Performance Col1(0) Col1(1)
MSE 0.333292125 0.333295741
NMSE 1.435229248 1.43524482
MAE 0.333314401 0.333316308
Min Abs Error 8.81615E-08 4.62701E-08
Max Abs Error 1.00002567 1.000022725
r 0.256671268 0.256665338
Percent
Correct 78.94736842 45.45454545
8.The results of MLP designed with 70% database for
training and 30% database for testing a network with five
hidden layers has been shown in Figure10 and Table 8. The
TanhAxon function at hidden layer, Sigmoid Axon function
at output layer and Levenberg learning rule has been used to
design this MLP classifier.
Output / Desired Col1(1) Col1(0)
Col1(1) 5 3
Col1(0) 3 11
Performance Col1(1) Col1(0)
MSE 0.272784227 0.272809807
NMSE 1.178817554 1.178928093
MAE 0.272883818 0.272919695
Min Abs Error 2.91852E-06 1.71976E-06
Max Abs Error 1.000219309 1.000321875
r 0.410450333 0.410364346
Percent Correct 62.5 78.57142857
Figure.10. Average MSE with S.D.B. for three runs
Table.8. Performance of MLP
Best
Networks Training
Cross
Validation
Run # 1 2
Epoch # 1000 347
Minimum MSE 0.012397959 1.01311E-09
Final MSE 0.012397959 1.08339E-06
9. The results of MLP that designed with 70% database for
training and 30%database for testing a network with five
hidden layers has been shown in Figure 11 and Table 9. The
Sigmoid Axon function at hidden layer, Sigmoid Axon
function at output layer and Levenberg learning rule has
been used to design this MLP classifier.
Figure.11. Average MSE with S.D.B. for three runs
-0.2
0
0.2
0.4
0.6
1
100
199
298
397
496
595
694
793
892
991
AverageMSE
Epoch
Average MSE with Standard
Deviation Boundaries for 3 Runs
Training
+ 1 Standard
Deviation
- 1 Standard
Deviation
Cross
Validation
-0.1
0
0.1
0.2
0.3
0.4
1
100
199
298
397
496
595
694
793
892
991
AverageMSE
Epoch
Average MSE with Standard
Deviation Boundaries for 3 Runs
Training
+ 1 Standard
Deviation
- 1 Standard
Deviation
Cross
Validation
-0.1
0
0.1
0.2
0.3
0.4
1
100
199
298
397
496
595
694
793
892
991
AverageMSE
Epoch
Average MSE with Standard
Deviation Boundaries for 3 Runs
Training
+ 1 Standard
Deviation
- 1 Standard
Deviation
Cross
Validation
7. IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
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Volume: 04 Issue: 12 | Dec-2015, Available @ http://www.ijret.org 224
Table.9. Performance of MLP
Best
Networks Training
Cross
Validation
Run # 2 2
Epoch # 1000 1000
Minimum MSE 0.012397959 4.63139E-06
Final MSE 0.012397959 4.63139E-06
Output / Desired Col1(1) Col1(0)
Col1(1) 9 3
Col1(0) 1 9
Performance Col1(1) Col1(0)
MSE 0.187784297 0.187778004
NMSE 0.757396663 0.757371284
MAE 0.202055781 0.202035265
Min Abs Error 1.08352E-06 7.20899E-07
Max Abs Error 1.000000176 0.999999931
r 0.631840611 0.631864851
Percent
Correct 90 75
10. The results of MLP that designed with 70% database for
training and 30%database for testing a network with five
hidden layers has been shown in Figure 12 and Table 10.
The Linear Sigmoid Axon function at hidden layer, Sigmoid
Axon function at output layer and Levenberg learning rule
has been used to design this MLP classifier.
Figure.12. Average MSE with S.D.B. for three runs
Table.10. Performance of MLP
Best
Networks Training
Cross
Validation
Run # 3 3
Epoch # 47 37
Minimum MSE 0.02637897 0.00013607
Final MSE 0.02637897 0.006843285
Output / Desired Col1(1) Col1(0)
Col1(1) 9 4
Col1(0) 1 8
Performance Col1(1) Col1(0)
MSE 0.120585703 0.12047363
NMSE 0.486362335 0.485910307
MAE 0.204653233 0.2023745
Min Abs Error 0.008063106 0.002503527
Max Abs Error 1.021844722 1.018708783
r 0.728278251 0.728898772
Percent Correct 90 66.66666667
11. The results of MLP that designed with 80% database for
training and 20%database for testing a network with five
hidden layers has been shown in Figure13 and Table 11. The
Sigmoid Axon function at hidden layer, Linear Sigmoid
Axon function at output layer and Levenberg learning rule
has been used to design this MLP classifier.
Figure.13. Average MSE with S.D.B. for three runs
Table.11. Performance of MLP
Best
Networks Training
Cross
Validation
Run # 3 1
Epoch # 53 30
Minimum MSE 0.028464595 0.005342038
Final MSE 0.028464595 0.005342053
Output / Desired Col1(1) Col1(0)
Col1(1) 6 4
Col1(0) 2 3
Performance Col1(1) Col1(0)
MSE 0.318735299 0.31873529
NMSE 1.280632897 1.280632863
MAE 0.4240641 0.424064103
Min Abs Error 0.088236967 0.088237001
Max Abs Error 0.911763033 0.911762999
r 0.188982237 0.188982237
Percent Correct 75 42.85714286
-0.05
0
0.05
0.1
0.15
0.2
0.25
0.3
1
100
199
298
397
496
595
694
793
892
991
AverageMSE
Epoch
Average MSE with Standard
Deviation Boundaries for 3 Runs
Training
+ 1 Standard
Deviation
- 1 Standard
Deviation
Cross
Validation
0
0.1
0.2
0.3
0.4
0.5
1
100
199
298
397
496
595
694
793
892
991
AverageMSE
Epoch
Average MSE with Standard
Deviation Boundaries for 3 Runs
Training
+ 1 Standard
Deviation
- 1 Standard
Deviation
Cross
Validation
8. IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
_______________________________________________________________________________________
Volume: 04 Issue: 12 | Dec-2015, Available @ http://www.ijret.org 225
12. The results of MLP that designed with 90% database for
training and 10%database for testing a network with five
hidden layers has been shown in Figure 14 and Table 12.
The Sigmoid Axon function at hidden layer, TanhAxon
function at output layer and Levenberg learning rule has
been used to design this MLP classifier.
Figure.14. Average MSE with S.D.B. for three runs
Table.12. Performance of MLP
Best
Networks Training
Cross
Validation
Run # 2 2
Epoch # 1000 14
Minimum MSE 0.016071429 0.001209166
Final MSE 0.01607143 0.20251269
Output / Desired Col1(1) Col1(0)
Col1(1) 5 1
Col1(0) 0 2
Performance Col1(1) Col1(0)
MSE 0.141924775 0.142004327
NMSE 0.605545707 0.605885129
MAE 0.180090077 0.180807332
Min Abs Error 0.051770794 0.054609445
Max Abs Error 1.055553126 1.0555548
r 0.745347813 0.745351192
Percent Correct 100 66.66666667
13. The results of MLP that designed with 90% database for
training and 10%database for testing a network with five
hidden layers has been shown in Figure 15 and Table13. The
Sigmoid Axon function at hidden layer, Sigmoid Axon
function at output layer and Levenberg learning rule has
been used to design this MLP classifier.
Figure.15. Average MSE with S.D.B. for three runs
Table.13. Performance of MLP
Best Networks Training Cross Validation
Run # 1 1
Epoch # 823 2
Minimum MSE 0.016071429 0.00125
Final MSE 0.016071429 0.101250941
Output / Desired Col1(1) Col1(0)
Col1(1) 4 0
Col1(0) 0 4
Performance Col1(1) Col1(0)
MSE 0.003086419 0.00308642
NMSE 0.012345676 0.012345679
MAE 0.05555555 0.055555555
Min Abs Error 0.055555544 0.055555555
Max Abs Error 0.055555556 0.055555555
r 1 1
Percent Correct 100 100
7. CONCLUSION
The proposed algorithm for classification of brain tumor
MRI image classified MRI images database effectively into
benign tumor and malignant tumor with the help of MLP
classifier. The MLP classifier used here with three and five
number of hidden layers by using different hidden layer
functions and output layer functions. By observing the
above results, it‟s seen that MLP classifier is the best
classifier for brain tumor MRI database. MLP classifier
classified the database accurately at 90% database for
training and 10%database for testing a network with five
hidden layers, Sigmoid Axon function at hidden layer,
Sigmoid Axon function at output layer with Levenberg
learning rule.
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991
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Epoch
Average MSE with Standard
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Training
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Deviation
Cross
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Cross
Validation
9. IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
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Volume: 04 Issue: 12 | Dec-2015, Available @ http://www.ijret.org 226
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