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/
Breast Cancer Detection with Convolutional Neural Networks (CNN)Mehmet Çağrı Aksoy
Photos and various addresses are taken from the internet. It may be subject to copyright.
For references:
https://github.com/mcagriaksoy/EEM-305-Signals-and-Systems
https://medium.com/intro-to-artificial-intelligence/deep-learning-series-1-intro-to-deep-learning-abb1780ee20
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
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
Predictive Analysis of Breast Cancer Detection using Classification AlgorithmSushanti Acharya
Dissertation project titled “Predictive analysis of Breast Cancer detection using Classification”. For the research conducted, Breast Cancer Wisconsin Diagnostics dataset was used for analysis. Using R language machine learning model was designed based on various algorithms and the derived results were then visualized to present the most accurate model of them all (SVM in this case).
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/
Breast Cancer Detection with Convolutional Neural Networks (CNN)Mehmet Çağrı Aksoy
Photos and various addresses are taken from the internet. It may be subject to copyright.
For references:
https://github.com/mcagriaksoy/EEM-305-Signals-and-Systems
https://medium.com/intro-to-artificial-intelligence/deep-learning-series-1-intro-to-deep-learning-abb1780ee20
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
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
Predictive Analysis of Breast Cancer Detection using Classification AlgorithmSushanti Acharya
Dissertation project titled “Predictive analysis of Breast Cancer detection using Classification”. For the research conducted, Breast Cancer Wisconsin Diagnostics dataset was used for analysis. Using R language machine learning model was designed based on various algorithms and the derived results were then visualized to present the most accurate model of them all (SVM in this case).
image classification is a common problem in Artificial Intelligence , we used CIFR10 data set and tried a lot of methods to reach a high test accuracy like neural networks and Transfer learning techniques .
you can view the source code and the papers we read on github : https://github.com/Asma-Hawari/Machine-Learning-Project-
Data Science - Part XVII - Deep Learning & Image ProcessingDerek Kane
This lecture provides an overview of Image Processing and Deep Learning for the applications of data science and machine learning. We will go through examples of image processing techniques using a couple of different R packages. Afterwards, we will shift our focus and dive into the topics of Deep Neural Networks and Deep Learning. We will discuss topics including Deep Boltzmann Machines, Deep Belief Networks, & Convolutional Neural Networks and finish the presentation with a practical exercise in hand writing recognition technique.
Breast Cancer Detection from Mammography Images Using Machine Learning Algorithms (U-Net Segmentation and Dense Net Classifier implementation are in progress)
Short presentation for a special lecture on Medicine Graduation Course in Hospital de Clínicas (https://www.hcpa.edu.br/), as part of a one-day special discipline on Machine Learning and Healthcare. The goal was introducing the importance of Deep Learning for Healthcare as well as showing some of the recent impact.
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
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.
Machine Learning for Disease PredictionMustafa Oğuz
A great application field of machine learning is predicting diseases. This presentation introduces what is preventable diseases and deaths. Then examines three diverse papers to explain what has been done in the field and how the technology works. Finishes with future possibilities and enablers of the disease prediction technology.
following topics are discussed inside the PPT:
Introduction
Objective
Motivation
Literature Survey
Some Key Features of Disease
Plan of Action
Methodology Adopted
Data Collection
Steps to be Performed
Functional Architecture
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.
Hot Topics in Machine Learning For Research and thesisWriteMyThesis
Machine Learning and its subsequent fields have undergone tremendous growth in the past few years. It has a number of potential applications and is being used in different fields. A lot of research work is going on in this field. For more information, check out the PPT details...
Classification of Breast Cancer Diseases using Data Mining Techniquesinventionjournals
Medical data mining has great deal for exploring new knowledge from large amount of data. Classification is one of the important data mining techniques for classification of data. In this research work, we have used various data mining based classification techniques for classification of cancer diseases patient or not. We applied the Breast Cancer-Wisconsin (Original) data set into different data mining techniques and compared the accuracy of models with two different data partitions. BayesNet achieved highest accuracy as 97.13% in case of 10-fold data partitions. We have also applied the info gain feature selection technique on BayesNet and Support Vector Machine (SVM) and achieved best accuracy 97.28% accuracy with BayesNet in case of 6 feature subset.
image classification is a common problem in Artificial Intelligence , we used CIFR10 data set and tried a lot of methods to reach a high test accuracy like neural networks and Transfer learning techniques .
you can view the source code and the papers we read on github : https://github.com/Asma-Hawari/Machine-Learning-Project-
Data Science - Part XVII - Deep Learning & Image ProcessingDerek Kane
This lecture provides an overview of Image Processing and Deep Learning for the applications of data science and machine learning. We will go through examples of image processing techniques using a couple of different R packages. Afterwards, we will shift our focus and dive into the topics of Deep Neural Networks and Deep Learning. We will discuss topics including Deep Boltzmann Machines, Deep Belief Networks, & Convolutional Neural Networks and finish the presentation with a practical exercise in hand writing recognition technique.
Breast Cancer Detection from Mammography Images Using Machine Learning Algorithms (U-Net Segmentation and Dense Net Classifier implementation are in progress)
Short presentation for a special lecture on Medicine Graduation Course in Hospital de Clínicas (https://www.hcpa.edu.br/), as part of a one-day special discipline on Machine Learning and Healthcare. The goal was introducing the importance of Deep Learning for Healthcare as well as showing some of the recent impact.
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
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.
Machine Learning for Disease PredictionMustafa Oğuz
A great application field of machine learning is predicting diseases. This presentation introduces what is preventable diseases and deaths. Then examines three diverse papers to explain what has been done in the field and how the technology works. Finishes with future possibilities and enablers of the disease prediction technology.
following topics are discussed inside the PPT:
Introduction
Objective
Motivation
Literature Survey
Some Key Features of Disease
Plan of Action
Methodology Adopted
Data Collection
Steps to be Performed
Functional Architecture
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.
Hot Topics in Machine Learning For Research and thesisWriteMyThesis
Machine Learning and its subsequent fields have undergone tremendous growth in the past few years. It has a number of potential applications and is being used in different fields. A lot of research work is going on in this field. For more information, check out the PPT details...
Classification of Breast Cancer Diseases using Data Mining Techniquesinventionjournals
Medical data mining has great deal for exploring new knowledge from large amount of data. Classification is one of the important data mining techniques for classification of data. In this research work, we have used various data mining based classification techniques for classification of cancer diseases patient or not. We applied the Breast Cancer-Wisconsin (Original) data set into different data mining techniques and compared the accuracy of models with two different data partitions. BayesNet achieved highest accuracy as 97.13% in case of 10-fold data partitions. We have also applied the info gain feature selection technique on BayesNet and Support Vector Machine (SVM) and achieved best accuracy 97.28% accuracy with BayesNet in case of 6 feature subset.
An intelligent mammogram diagnosis system can be very helpful for radiologist in detecting the abnormalities earlier than typical screening techniques. This paper investigates a new classification approach for detection of breast abnormalities in digital mammograms using League Championship Algorithm Optimized Ensembled Fully Complex valued Relaxation Network (LCA-FCRN). The proposed algorithm is based on extracting curvelet fractal texture features from the mammograms and classifying the suspicious regions by applying a pattern classifier. The whole system includes steps for pre-processing, feature extraction, feature selection and classification to classify whether the given input mammogram image is normal or abnormal. The method is applied to MIAS database of 322 film mammograms. The performance of the CAD system is analysed using Receiver Operating Characteristic (ROC) curve. This curve indicates the trade-offs between sensitivity and specificity that is available from a diagnostic system, and thus describes the inherent discrimination capacity of the proposed system. The result shows that the area under the ROC curve of the proposed algorithm is 0.985 with a sensitivity of 98.1% and specificity of 92.105%. Experimental results demonstrate that the proposed method can form an effective CAD system, and achieve good classification accuracy.
Breast cancer detection using ensemble of convolutional neural networksIJECEIAES
Early detection leading to timely treatment in the initial stages of cancer may decrease the breast cancer death rate. We propose deep learning techniques along with image processing for the detection of tumors. The availability of online datasets and advances in graphical processing units (GPU) have promoted the application of deep learning models for the detection of breast cancer. In this paper, deep learning models using convolutional neural network (CNN) have been built to automatically classify mammograms into benign and malignant. Issues like overfitting and dataset imbalance are overcome. Experimentation has been done on two publicly available datasets, namely mammographic image analysis society (MIAS) database and digital database for screening mammography (DDSM). Robustness of the models is accomplished by merging the datasets. In our experimentation, MatConvNet has achieved an accuracy of 94.2% on the merged dataset, performing the best amongst all the CNN models used individually. Hungarian optimization algorithm is employed for selection of individual CNN models to form an ensemble. Ensemble of CNN models led to an improved performance, resulting in an accuracy of 95.7%.
Controlling informative features for improved accuracy and faster predictions...Damian R. Mingle, MBA
Identification of suitable biomarkers for accurate prediction of phenotypic outcomes is a goal for personalized medicine. However, current machine learning approaches are either too complex or perform poorly.
For more information:
http://societyofdatascientists.com/controlling-informative-features-for-improved-accuracy-and-faster-predictions-in-omentum-cancer-models/?src=slideshare
Regularized Weighted Ensemble of Deep Classifiers ijcsa
Ensemble of classifiers increases the performance of the classification since the decision of many experts
are fused together to generate the resultant decision for prediction making. Deep learning is a classification algorithm where along with the basic learning technique, fine tuning learning is done for improved precision of learning. Deep classifier ensemble learning is having a good scope of research.Feature subset selection is another for creating individual classifiers to be fused for ensemble learning. All these ensemble techniques faces ill posed problem of overfitting. Regularized weighted ensemble of deep support vector machine performs the prediction analysis on the three UCI repository problems IRIS,Ionosphere and Seed data set, thereby increasing the generalization of the boundary plot between the
classes of the data set. The singular value decomposition reduced norm 2 regularization with the two level
deep classifier ensemble gives the best result in our experiments.
sis of health condition is very challenging task for every human being because life is directly related to health
condition. Data mining based classification is one of the important applications for classification of data. In this
research work, we have used various classification techniques for classification of thyroid data. CART gives highest
accuracy 99.47% as best model. Feature selection plays very important role to computationally efficient and increase
the performance of model. This research work focus on Info Gain and Gain Ratio feature selection technique to
reduce the irrelevant features from original data set and computationally increase the performance of model. We have
applied both the feature selection techniques on best model i. e. CART. Our proposed CART-Info Gain and CARTGain
Ratio gives 99.47% and 99.20% accuracy with 25 and 3 feature respectively.
Face Recognition for Human Identification using BRISK Feature and Normal Dist...ijtsrd
Face recognition is a kind of automatic human identification from face images has been performed widely research in image processing and machine learning. Face image, facial information of the person is presented and unique information for each person even two person possessed the same face. We propose a methodology for automatic human classification based on Binary Robust Invariant Scalable Keypoints BRISK feature of face images and the normal distribution model. In our proposed methodology, the normal distribution model is used to represent the statistical information of face image as a global feature. The human name is the output of the system according to the input face image. Our proposed feature is applied with Artificial Neural Networks to recognize face for human identification. The proposed feature is extracted from the face image of the Extended Yale Face Database B to perform human identification and highlight the properties of the proposed feature. Khin Mar Thi "Face Recognition for Human Identification using BRISK Feature and Normal Distribution Model" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26589.pdfPaper URL: https://www.ijtsrd.com/computer-science/multimedia/26589/face-recognition-for-human-identification-using-brisk-feature-and-normal-distribution-model/khin-mar-thi
Multivariate feature descriptor based cbir model to query large image databasesIJARIIT
The content based image retrieval (CBIR) applications have grown their popularity in the past decade with the
exponential growth in the image data volumes. The social networks have aggravated the size of image data on the internet. Social
network enables everyone to upload the images of one’s choice, which becomes the reason behind aggregation of millions of
images on the cyber space. It’s not possible to query these large image databases with the ordinary methods. Hence there was a
strong requirement of a smart and intelligent method to discover the similar images, which has been accomplished by using the
machine learning methods. In this paper, the multivariate feature descriptor method has been presented to extract the required
and relevant information from the large image databases. The proposed multivariate method involves the image color and texture
for the purpose of image matching to the query image (also known as a reference image). The most matching entities are returned
as the final results by the image extraction method. There are four methods, which involves three singular feature and one
multivariate feature based models, have been implemented. The multivariate model has been found much stable and returned
the maximum accuracy under this model.
KNOWLEDGE BASED ANALYSIS OF VARIOUS STATISTICAL TOOLS IN DETECTING BREAST CANCERcscpconf
In this paper, we study the performance criterion of machine learning tools in classifying breast cancer. We compare the data mining tools such as Naïve Bayes, Support vector machines, Radial basis neural networks, Decision trees J48 and simple CART. We used both binary and multi class data sets namely WBC, WDBC and Breast tissue from UCI machine learning depositary. The experiments are conducted in WEKA. The aim of this research is to find out the best classifier with respect to accuracy, precision, sensitivity and specificity in detecting breast cancer
A HYBRID MODEL FOR MINING MULTI DIMENSIONAL DATA SETSEditor IJCATR
This paper presents a hybrid data mining approach based on supervised learning and unsupervised learning to identify the closest data patterns in the data base. This technique enables to achieve the maximum accuracy rate with minimal complexity. The proposed algorithm is compared with traditional clustering and classification algorithm and it is also implemented with multidimensional datasets. The implementation results show better prediction accuracy and reliability.
Clustering and Classification of Cancer Data Using Soft Computing Technique IOSR Journals
Clustering and classification of cancer data has been used with success in field of medical side. In
this paper the two algorithm K-means and fuzzy C-means proposed for the comparison and find the accuracy of
the result. this paper address the problem of learning to classify the cancer data with two different method and
information derived from the training and testing .various soft computing based classification and show the
comparison of classification technique and classification of this health care data .this paper present the
accuracy of the result in cancer data.
K-Medoids Clustering Using Partitioning Around Medoids for Performing Face Re...ijscmcj
Face recognition is one of the most unobtrusive biometric techniques that can be used for access control as well as surveillance purposes. Various methods for implementing face recognition have been proposed with varying degrees of performance in different scenarios. The most common issue with effective facial biometric systems is high susceptibility of variations in the face owing to different factors like changes in pose, varying illumination, different expression, presence of outliers, noise etc. This paper explores a novel technique for face recognition by performing classification of the face images using unsupervised learning approach through K-Medoids clustering. Partitioning Around Medoids algorithm (PAM) has been used for performing K-Medoids clustering of the data. The results are suggestive of increased robustness to noise and outliers in comparison to other clustering methods. Therefore the technique can also be used to increase the overall robustness of a face recognition system and thereby increase its invariance and make it a reliably usable biometric modality.
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.
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.
Vaccine management system project report documentation..pdfKamal Acharya
The Division of Vaccine and Immunization is facing increasing difficulty monitoring vaccines and other commodities distribution once they have been distributed from the national stores. With the introduction of new vaccines, more challenges have been anticipated with this additions posing serious threat to the already over strained vaccine supply chain system in Kenya.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
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.
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.
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.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptx
Classification of Breast Masses Using Convolutional Neural Network as Feature Extractor and Classifier
1. Classification of Breast Masses Using
Convolutional Neural Network as
Feature Extractor and Classifier
Pinaki Ranjan Sarkara
, Deepak Mishraa
and Gorthi R.K.S.S Manyamb
a,b: Indian Institute of Space Science and Technology, Trivandrum
Indian Institute of Technology, Tirupati
Second International Conference on Computer Vision and
Image Processing & Workshop on Multimedia, IIT-Roorkee
10 September, 2017
2. Outline
1 Introduction
Motivation
Objective
Difficulties
2 Background Theories
Deep Learning
3 Approach
Databases & Pre-processing
Extraction of Region of Interests
Overall Architecture for Mass Classification
Feature Extraction & Classification
4 Results
Results on different databases
Contribution
5 Scope of Future Works
P. R. Sarkar A deep-CAD system for Breast Mass Classification 10 September, 2017 2/24
3. Introduction Motivation
Motivation
Motivation
Breast is the most common site of cancer among women in
India (27% of total). Studies show that nearly 48.45%
patients died in the year 2012ab.
Due to very small non-palpable micro-calcification
clusters/masses, approximately 1% to 20% of breast cancer is
missed by radiologists.
Due to the difficulties of Radiologists to detect
micro-calcification clusters a Computer Aided Diagnosis
(CAD) system is much needed.
a
Trends of breast cancer in India, http://www.breastcancerindia.net/statistics/trends.html.
b
Siegel R.L. Miller; K.D. Jemal; A, “Cancer statistics” 2017,
CA: A Cancer Journal for Clinicians 67(1) 730 (2017).
P. R. Sarkar A deep-CAD system for Breast Mass Classification 10 September, 2017 3/24
4. Introduction Objective
Objective
Objectives
Automatic mass classification from breast mammograms.
Low False Positive Rate and High True Positive Rate in
classification.
Figure 1: Objective in this work
P. R. Sarkar A deep-CAD system for Breast Mass Classification 10 September, 2017 4/24
5. Introduction Difficulties
Difficulties
Figure 2: The levels 1, 2 and 3 of ROIs represents normal, benign and malignant
classes respectively1. From these figures, it is understood that classification between
them is very difficult.
1
S. Beura, B. Majhi, and R. Dash,
“Mammogram classification using two dimensional discrete wavelet transform and gray-level co-occurrence matrix for dete
” Neurocomputing, vol. 154, pp. 114, 2015.
P. R. Sarkar A deep-CAD system for Breast Mass Classification 10 September, 2017 5/24
6. Background Theories Deep Learning
Deep Learning
“Deep Learning is an algorithm which has no theoretical limitations of
what it can learn; the more data you give and the more computational
time you provide, the better it is.”
- Geoffrey Hinton, Google
Deep learning maybe loosely defined as an attempt to train a
hierarchy of feature detectors with each layer learning a higher
representation of the preceding layer.
Deep learning discovers intricate structure in large data sets by
using the backpropagation algorithm to indicate how a machine
should change its internal parameters that are used to compute the
representation in each layer from the representation in the previous
layer2
.
2
LeCun, Y., Bengio, Y. and Hinton, G., 2015. Deep learning. Nature, 521(7553), pp.436-444..
P. R. Sarkar A deep-CAD system for Breast Mass Classification 10 September, 2017 6/24
7. Background Theories Deep Learning
Successful Architectures in DL
Many variants of deep learning architectures are being proposed
and some of them are proved to be successful such as:
Convolutional Neural Network (CNN)3
Deep Boltzmann Machine (DBM)4
Deep Belief Networks (DBN)5
Stacked Denoising Auto-encoders (SDAE)6
3
A. Krizhevsky, “Imagenet classification with deep convolutional neural networks”.
4
R. Salakhutdinov and G. E. Hinton, “Deep boltzmann machines, ” in AISTATS, vol. 1, p. 3, 2009.
5
G. E. Hinton, “Deep belief networks, ” Scholarpedia, vol. 4, no. 5, p. 5947, 2009.
6
P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio, and P.-A. Manzagol,
“Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion”.
P. R. Sarkar A deep-CAD system for Breast Mass Classification 10 September, 2017 7/24
8. Background Theories Deep Learning
Convolutional Neural Network
Figure 3: The CNN architecture is composed hierarchical units and each unit extracts
different level of features. Combining more units will produce deeper network along
with more semantic features.7
7
P.R.Sarkar, Deepak Mishra. and Gorthi R.K.S.S. Manyam,
“Classification of Breast Masses Using Convolutional Neural Network as Feature Extractor and Classifier”. CVIP-2017.
P. R. Sarkar A deep-CAD system for Breast Mass Classification 10 September, 2017 8/24
9. Approach Databases & Pre-processing
Databases & Pre-processing
We validated the proposed method using two publicly available
databases i.e. Mammographic Image Analysis Society (MIAS)8
and
CBIS-DDSM9
.
Within 2620 scanned cases based on the magnitude of abnormality,
the abnormal class is divided into two more classes, benign and
malignant. We have taken total 273 benign cases and 273
malignant cases from CBIS-DDSM.
From 322 cases of mini-MIAS database 64 benign and 51 malignant
cases are there in the abnormal class.
Each ROIs are resized to 84 × 84 to reduce the number of network
parameters also we used four rotations rot = {0o
, 90o
, 180o
, 270o
}
and increased the training data three times.
8
J. Suckling, J. Parker, D. Dance, S. Astley, I. Hutt, C. Boggis, I. Ricketts, E. Stamatakis, N. Cerneaz,
S. Kok, and others, “The mammographic image analysis society digital mammogram database,
” in Exerpta Medica. International Congress Series, vol. 1069, pp. 375378, 1994..
9
R. S. Lee and D. R. Francisco Gimenez, Assaf Hoogi, “Curated Breast Imaging Subset of DDSM,
” in The Cancer Imaging Archive, 2016..
P. R. Sarkar A deep-CAD system for Breast Mass Classification 10 September, 2017 9/24
10. Approach Extraction of Region of Interests
Extraction of Region of Interests
In this work, we evaluated our model on pre-segmented ROIs.
CBIS-DDSM database offers segmented ROIs for the
development of CAD systems.
A cropping operation has been applied on MIAS mammogram
images to extract the regions of interests (ROIs) which
contain the abnormalities, excluding the unwanted portions of
the image.
P. R. Sarkar A deep-CAD system for Breast Mass Classification 10 September, 2017 10/24
11. Approach Overall Architecture for Mass Classification
Overall Architecture for Mass Classification
Figure 4: Overall architecture of a complete breast mass classification framework. The
detection and localization network detects the mass in the mammogram then a
bounding-box regression is applied to get the mass ROI. The ROI is given to our
network to extract deep features for the classification task.
P. R. Sarkar A deep-CAD system for Breast Mass Classification 10 September, 2017 11/24
12. Approach Overall Architecture for Mass Classification
Detailed Parameters of Each Layers
Table 1: Detailed parameters of each layer
Name Filter size Depth of Filter Dropout
Conv1 11 32
ReLU1 1
Pooling1 3
Conv2 11 64
ReLU2 1
Dropout1 0.5
Pooling2 3
Conv3 7 96
ReLU3 1
Dropout2 0.15
Pooling3 2
Conv4 5 128
ReLU4 1
Dropout3 0.25
Pooling4 2
Fc5 1 5000
ReLU5 1
Dropout4 0.5
Fc6 1 1000
ReLU6 1
Fc7 1 2
Softmax 1
P. R. Sarkar A deep-CAD system for Breast Mass Classification 10 September, 2017 12/24
13. Approach Overall Architecture for Mass Classification
Training process
Here we try to minimize an objective function (in this work,
categorical cross entropy) through gradient descent algorithm.
Studies show that stochastic gradient descent (SGD) is very
good at optimizing the objective functions for DL. We have
used SGD for this work.
Parameters are updated using backpropagation algorithm.
After getting satisfactory traing accuracy and loss value, we
ensure that our network is well trained.
The trained network is used for testing new inputs and can be
fine-tuned on another databases like Inbreast, BCDR etc.
P. R. Sarkar A deep-CAD system for Breast Mass Classification 10 September, 2017 13/24
14. Approach Feature Extraction & Classification
Feature Extraction & Classification
CNN as classifier
Softmax is used for producing probability scores for each class.
Softmax works on the whole data cloud where SVM works only on
the data near to support vector.
SVM as classifier
We have used our trained CNN network as a deep feature extractor
as well as a classifier.
The deep feature is a 1000 × 1 dimensional feature vector and used
SVM with Radial Basis Function (RBF) kernel.
A grid search method is employed to find the best hyperparameters
w.r.t RBF kernel within a range.
P. R. Sarkar A deep-CAD system for Breast Mass Classification 10 September, 2017 14/24
15. Results Results on different databases
Result in MIAS Database
(a) Training and validation accuracy in
mini-MIAS database
(b) Training and validation loss in
mini-MIAS database
Figure 5: Training results in mini-MIAS database
P. R. Sarkar A deep-CAD system for Breast Mass Classification 10 September, 2017 15/24
16. Results Results on different databases
Result in DDSM Database
(a) Training and validation accuracy in
CBIS-DDSM database
(b) Training and validation loss in
CBIS-DDSM database
Figure 6: Training results in CBIS-DDSM database
P. R. Sarkar A deep-CAD system for Breast Mass Classification 10 September, 2017 16/24
17. Results Results on different databases
ROC curve analysis
0.0 0.2 0.4 0.6 0.8 1.0
False Positive Rate
0.0
0.2
0.4
0.6
0.8
1.0
TruePositiveRate
SVM as classifier, AUC = 0.9768
CNN as classifier, AUC = 0.9921
(a) ROC curve of classification in
mini-MIAS database
0.0 0.2 0.4 0.6 0.8 1.0
False Positive Rate
0.0
0.2
0.4
0.6
0.8
1.0
TruePositiveRate
SVM as classifier, AUC = 0.9922
CNN as classifier, AUC = 0.9993
(b) ROC curve of classification in
CBIS-DDSM database
Figure 7: Receiver Operating Characteristics curve analysis
P. R. Sarkar A deep-CAD system for Breast Mass Classification 10 September, 2017 17/24
19. Results Results on different databases
Merits of using CNN for CADs
Why this method?
CNNs are very good at learning intricate strictures from the
high-dimensional data.
It extracts semantic features which simulates the real
diagnosis process of the mammograms.
Our main idea was to show that, the CNN works as the
feature extractor as well as the classifier for this problem.
P. R. Sarkar A deep-CAD system for Breast Mass Classification 10 September, 2017 19/24
20. Results Results on different databases
Contribution
References Techniques Database Classification
performance
Xie et. al.10 Gray level features, MIAS 96.0%
textural features DDSM 95.7%
Jiao et. al.11 High & medium level, DDSM 96.7%
deep features
Arevalo et. al.12 CNN, SVM DDSM 96.7%
Beura et. al.13 2D-DWT, GLCM MIAS 98.0%
DDSM 98.8%
Ours Segmented ROIs, MIAS 99.081%
CNN DDSM 99.267%
Table 4: Comparison of classification performances
10
W. Xie, Y. Li, and Y. Ma,
“Breast mass classification in digital mammography based on extreme learning machine, ” Neurocomputing,
vol. 173, pp. 930941, 2016..
11
Z. Jiao, X. Gao, Y. Wang, and J. Li, “A deep feature based framework for breast masses classification,
” Neurocomputing, vol. 197, pp. 221231, July 2016..
12
J. Arevalo, F. A. Gonz alez, R. Ramos-Poll an, J. L. Oliveira, and M. A. G. Lopez,
Convolutional neural networks for mammography mass lesion classification,
in Engineering in Medicine and Biology Society (EMBC),
2015 37th Annual International Conference of the IEEE, pp. 797800, IEEE, 2015..
13
S. Beura, B. Majhi, and R. Dash,
“Mammogram classification using two dimensional discrete wavelet transform and gray-level co-occurrence matrix for dete
” Neurocomputing, vol. 154, pp. 114, 2015..
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21. Scope of Future Works
Some more contributions in this work
Towards end-to-end CAD system
Though in this work, we have used pre-segmented ROIs for
training ans testing of mammograms, later we used a fully
convolutional deep hierarchical saliency map prediction
network.
Here, we treat the masses as salient objects and predicts the
suspicious regions through this trained network.
This leads us to propose an end-to-end breast mass
classification framework.
P. R. Sarkar A deep-CAD system for Breast Mass Classification 10 September, 2017 21/24
22. Scope of Future Works
Scope of Future Works
End-to-end Breast Mass Classification Framework
The main objective of an end-to-end computer aided diagnosis
system is to improve the classification accuracy while minimizing
the false positives. One can improve our novel architecture for
better simulating the diagnosis procedure followed by radiologists
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