This document proposes a method for classifying breast cancer cells using unsupervised linear transformation (PCA) along with cosine similarity. It involves the following steps: (1) applying PCA to select robust features from a breast cancer dataset, (2) projecting the data into a lower dimensional space using the selected features, and (3) classifying the cells as normal, benign, or malignant using cosine similarity in the reduced dimensional space. Experiments show the accuracy increases from 78.9% without PCA to 99.12% when using the proposed PCA-Cosine similarity method, demonstrating its effectiveness for breast cancer classification.
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).
Breast Cancer Detection from Mammography Images Using Machine Learning Algorithms (U-Net Segmentation and Dense Net Classifier implementation are in progress)
Breast Cancer Diagnosis using a Hybrid Genetic Algorithm for Feature Selection based on Mutual Information (Abeer Alzubaidi, Georgina Cosma, David Brown and Graham Pockley)
Interactive Technologies and Games (ITAG) Conference 2016
Health, Disability and EducationDates: Wednesday 26 October 2016 - Thursday 27 October 2016 Location: The Council House, NG1 2DT
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).
Breast Cancer Detection from Mammography Images Using Machine Learning Algorithms (U-Net Segmentation and Dense Net Classifier implementation are in progress)
Breast Cancer Diagnosis using a Hybrid Genetic Algorithm for Feature Selection based on Mutual Information (Abeer Alzubaidi, Georgina Cosma, David Brown and Graham Pockley)
Interactive Technologies and Games (ITAG) Conference 2016
Health, Disability and EducationDates: Wednesday 26 October 2016 - Thursday 27 October 2016 Location: The Council House, NG1 2DT
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 diagnosis and recurrence prediction using machine learning tech...eSAT Journals
Abstract Breast Cancer has become the common cause of death among women. Due to long hours invested in manual diagnosis and lesser diagnostic system available emphasize the development of automated diagnosis for early diagnosis of the disease. Our aim is to classify whether the breast cancer is benign or malignant and predict the recurrence and non-recurrence of malignant cases after a certain period. To achieve this we have used machine learning techniques such as Support Vector Machine, Logistic Regression, KNN and Naive Bayes. These techniques are coded in MATLAB using UCI machine learning depository. We have compared the accuracies of different techniques and observed the results. We found SVM most suited for predictive analysis and KNN performed best for our overall methodology. Keywords: Breast Cancer, SVM, KNN, Naive Bayes, Logistic Regression, Classification.
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.
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
Existing model uses structured data to predict the patients of either high risk or low risk.
But for a complex disease, structured data is not a good way to describe the disease.
We propose a new convolutional neural network (CNN)-based multimodal disease risk prediction algorithm using structured and unstructured data from hospital.
In this paper, we mainly focus on the risk prediction of cerebral infarction.
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
Overview of breast cancer screening and diagnosis Linnet Thomas
Breast cancer development and progression
Diagnosis methods
Treatment methods
Existing screening and diagnostic tools
Emerging techniques for screening and diagnosis
Innovative products or technologies for breast cancer screening and diagnosis
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
Brain tumor classification using artificial neural network on mri imageseSAT Journals
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.
FRACTAL PARAMETERS OF TUMOUR MICROSCOPIC IMAGES AS PROGNOSTIC INDICATORS OF C...csandit
Research in the field of breast cancer outcome prognosis has been focused on molecular biomarkers, while neglecting the discovery of novel tumour histology structural clues. We thus
aimed to improve breast cancer prognosis by fractal analysis of tumour histomorphology. This study included 92 breast cancer patients without systemic treatment. Fractal parametersfractal dimension and lacunarity of the breast tumour microscopic histology possess prognostic value comparable to the major clinicopathological prognostic parameters. Fractal analysis was performed for the first time on routinely produced archived pan-tissue stained primary breast tumour sections, indicating its potential for clinical use as a simple and cost-effective prognostic indicator of distant metastasis risk to complement the molecular approaches for
cancer risk prognosis.
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 diagnosis and recurrence prediction using machine learning tech...eSAT Journals
Abstract Breast Cancer has become the common cause of death among women. Due to long hours invested in manual diagnosis and lesser diagnostic system available emphasize the development of automated diagnosis for early diagnosis of the disease. Our aim is to classify whether the breast cancer is benign or malignant and predict the recurrence and non-recurrence of malignant cases after a certain period. To achieve this we have used machine learning techniques such as Support Vector Machine, Logistic Regression, KNN and Naive Bayes. These techniques are coded in MATLAB using UCI machine learning depository. We have compared the accuracies of different techniques and observed the results. We found SVM most suited for predictive analysis and KNN performed best for our overall methodology. Keywords: Breast Cancer, SVM, KNN, Naive Bayes, Logistic Regression, Classification.
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.
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
Existing model uses structured data to predict the patients of either high risk or low risk.
But for a complex disease, structured data is not a good way to describe the disease.
We propose a new convolutional neural network (CNN)-based multimodal disease risk prediction algorithm using structured and unstructured data from hospital.
In this paper, we mainly focus on the risk prediction of cerebral infarction.
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
Overview of breast cancer screening and diagnosis Linnet Thomas
Breast cancer development and progression
Diagnosis methods
Treatment methods
Existing screening and diagnostic tools
Emerging techniques for screening and diagnosis
Innovative products or technologies for breast cancer screening and diagnosis
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
Brain tumor classification using artificial neural network on mri imageseSAT Journals
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.
FRACTAL PARAMETERS OF TUMOUR MICROSCOPIC IMAGES AS PROGNOSTIC INDICATORS OF C...csandit
Research in the field of breast cancer outcome prognosis has been focused on molecular biomarkers, while neglecting the discovery of novel tumour histology structural clues. We thus
aimed to improve breast cancer prognosis by fractal analysis of tumour histomorphology. This study included 92 breast cancer patients without systemic treatment. Fractal parametersfractal dimension and lacunarity of the breast tumour microscopic histology possess prognostic value comparable to the major clinicopathological prognostic parameters. Fractal analysis was performed for the first time on routinely produced archived pan-tissue stained primary breast tumour sections, indicating its potential for clinical use as a simple and cost-effective prognostic indicator of distant metastasis risk to complement the molecular approaches for
cancer risk prognosis.
A Novel DBSCAN Approach to Identify Microcalcifications in Cancer Images with...Editor IJCATR
Cancer is the most deadly disease among the human life. Breast Cancer is one of the most common cancers in this
industrialized world and it is the most common cause of cancer related death among worldwide. Many segmentation
technologies and clustering technologies like K-Means, K-Mediod, CLARANS etc have been proposed to identify the
microcalcifications but this paper presents our new approach of identification of cancer cells in the images containing with
noise and the performance analysis
Comparison of Image Segmentation Algorithms for Brain Tumor DetectionIJMTST Journal
This paper deals with the implementation of Simple Algorithms for detection of size and shape of tumor in brain using MRI images. Generally, CT scan or MRI that is directed into intracranial cavity produces a complete image of brain. This image is visually examined by the physician for detection & diagnosis of brain tumor. However this method of detection resists the accurate determination of stage & size of tumor. To avoid that, this project uses computer aided method for segmentation (detection) of brain tumor by applying Fuzzy C-Means, K-Means, Gaussian Kernel and Pillar K-means algorithms. This segmentation process includes a new mechanism for clustering the elements of high-resolution images in order to improve precision and reduce computation time. The system applies FCM, Gaussian kernel and K-means clustering to the image later optimized by Pillar Algorithm. It designates the initial centroids’ positions by calculating the Euclidian distance metric between each data point and all previous centroids. Then it selects data points which have the maximum distance as new initial centroids. This algorithm distributes all initial centroids according to the maximum accumulated distance metric. In addition, it also reduces the time for analysis. At the end of the process the tumor is extracted from the MRI image and its exact position and the shape is also determined. This paper evaluates the proposed approach for Brain tumor detection by comparing with K-means, Fuzzy C means, Gaussian Kernel and manually segmented algorithms. The experimental results clarify the effectiveness of proposed approach to improve the segmentation quality in aspects of precision and computational time.
Performance and Evaluation of Data Mining Techniques in Cancer DiagnosisIOSR Journals
Abstract: We analyze the breast Cancer data available from the WBC, WDBC from UCI machine learning with
the aim of developing accurate prediction models for breast cancer using data mining techniques. Data mining
has, for good reason, recently attracted a lot of attention, it is a new Technology, tackling new problem, with
great potential for valuable commercial and scientific discoveries. The experiments are conducted in WEKA.
Several data mining classification techniques were used on the proposed data. There are many classification
techniques in data mining such as Decision Tree, Rules NNge, Tree random forest, Random Tree, lazy IBK. The
aim of this paper is to investigate the performance of different classification techniques. The data breast cancer
data with a total 286 rows and 10 columns will be used to test and justify the different between the classification
methods and algorithm.
Keywords - Machine learning, data mining Weka, classification, breast cancer
Bridging the STEM gender gap through cultural inclusion and educational opportunity, this opportunity was granted to a selected set of women from UB to showcase their research.
Possibilistic Fuzzy C Means Algorithm For Mass classificaion In Digital Mammo...IJERA Editor
Mammography is an effective imaging modality of breast cancer abnormalities detection. Survival rate of breast cancer treatment can be increased via early detection of mammography. However detecting the mass in the early stage is a tough task for radiologist. Detection of suspicious abnormalities is a continual task. Out of thousand cases only 3 to 4 are analyzed as cancerous by a radiologist and thus abnormality may be left out. 10-30% of cancers are failed to detect by radiologist. Computer Aided Diagnosis helps the radiologists to detect abnormalities earlier than traditional procedures. Because of some negligence in capturing device, the image may be affected by noise this leads to fault diagnosis. Preprocessing can remove this unwanted noise. In this paper features such as entropy, circularity, edge detection, and correlation are extracted from the image to distinguish normal and abnormal regions of a mammogram. Classification and detection of mammogram can be done by Possibilistic Fuzzy C Means algorithm and Support Vector Machine using extracted features.
A Soft-Decision Approach for Microcalcification Mass Identification from Digi...DR B.Surendiran .
Breast cancer is one of the major causes of fatality among women aged 40 and above. Digital mammography is used by radiologist for analysis and interpretation of cancer. Visual reading and interpretation of mammograms is a very demanding and expensive job. Even well-trained experts may have an inter-observe variation rate of 65-75 percent. Computer Aided Diagnosis (CAD) systems have been developed to complement radiologists in interpreting mammograms for mass detection and identification of calcification. Thus, it is very important to develop CADs that can identify malignant lesions effectively. A combination of CAD scheme and expert knowledge would effectively improve the rate of detection and accuracy of masses. We use a soft-decision approach for identifying the microcalcification mass present in digital mammograms. A suitable clustering algorithm is applied for partitioning the digital mammogram into various meaningful regions. During post processing phase, the background region is identified and not considered for further processing. The Coefficient of Variation (CV) of various regions of the partitioned mammogram is calculated and the microcalcification lesion present in mammogram is identified. The experimental result is found to be encouraging.
Human brain is the most complex structure where identifying the tumor like diseases are extremely challenging because differentiating the components of a brain is complex. In this paper, pillar k-means algorithm is used for segmentation of brain tumor from magnetic resonance image (MRI).Generally, the brain tumor is detected by radiologist through analysis of MR images which takes longer time. The pillar k-means algorithm’s experimental results clarify the effectiveness of our approach to improve the segmentation quality, accuracy, and computational time. Classify, the tumor from the brain MR images using Bayesian classification.
Classification of mammograms based on features extraction techniques using su...CSITiaesprime
Now mammography can be defined as the most reliable method for early breast cancer detection. The main goal of this study is to design a classifier model to help radiologists to provide a second view to diagnose mammograms. In the proposed system medium filter and binary image with a global threshold have been applied for removing the noise and small artifacts in the preprocessing stage. Secondly, in the segmentation phase, a hybrid bounding box and region growing (HBBRG) algorithm are utilizing to remove pectoral muscles, and then a geometric method has been applied to cut the largest possible square that can be obtained from a mammogram which represents the region of interest (ROI). In the features extraction phase three method was used to prepare texture features to be a suitable introduction to the classification process are first Order (statistical features), local binary patterns (LBP), and gray-level co-occurrence matrix (GLCM), Finally, support vector machine (SVM) has been applied in two-level to classify mammogram images in the first level to normal or abnormal, and then the classification of abnormal once in the second level to the benign or malignant image. The system was tested on the MAIS the mammogram image analysis society (MIAS) database, in addition to the image from the Teaching Oncology Hospital, Medical City in Baghdad, where the results showed achieving an accuracy of 95.454% for the first level and 97.260% for the second level, also, the results of applying the proposed system to the MIAS database alone were achieving an accuracy of 93.105% for the first level and 94.59 for the second level.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Modified fuzzy rough set technique with stacked autoencoder model for magneti...IJECEIAES
Breast cancer is the common cancer in women, where early detection reduces the mortality rate. The magnetic resonance imaging (MRI) images are efficient in analyzing breast cancer, but it is hard to identify the abnormalities. The manual breast cancer detection in MRI images is inefficient; therefore, a deep learning-based system is implemented in this manuscript. Initially, the visual quality improvement is done using region growing and adaptive histogram equalization (AHE), and then, the breast lesion is segmented by Otsu thresholding with morphological transform. Next, the features are extracted from the segmented lesion, and a modified fuzzy rough set technique is proposed to reduce the dimensions of the extracted features that decreases the system complexity and computational time. The active features are fed to the stacked autoencoder for classifying the benign and malignant classes. The results demonstrated that the proposed model attained 99% and 99.22% of classification accuracy on the benchmark datasets, which are higher related to the comparative classifiers: decision tree, naïve Bayes, random forest and k-nearest neighbor (KNN). The obtained results state that the proposed model superiorly screens and detects the breast lesions that assists clinicians in effective therapeutic intervention and timely treatment.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
The Art of the Pitch: WordPress Relationships and Sales
Breast cancer classification
1. Breast Cancer Classification based on Unsupervised Linear
Transformation along with Cos Similarity
Machine Learning
Dr. Ashwan A. Abdulmunem
8/2/2021
2.
3. Introduction
- Breast cancer is one of the leading causes of mortality in women. Early detection and treatment are
imperative for improving survival rates.
- According to a recent report published by the American Cancer Society, breast cancer is the most prevalent
form of cancer in women, in the USA. In 2017 alone, studies indicate that approximately 252,000 new cases of
invasive breast cancer and 63,000 cases of in situ breast cancer are expected to be diagnosed, with 40,000
breast cancer-related deaths expected to occur [1]. Consequently, there is a real need for early diagnosis and
treatment, in order to reduce morbidity rates and improve patients’ quality of life.
1)DeSantis, C.E., Ma, J., Goding Sauer, A., Newman, L.A., Jemal, A.: Breast cancer statistics, 2017, racial
disparity in mortality by state. CA: a cancer journal for clinicians 67(6) (2017) 439–448
https://www.memorialplasticsurgery.com/breast-cancer-statistics-2017/
5. Breast Cancer: General Classification
Approaches
● Grade. Grading focuses on the appearance of the breast cancer cells compared to the appearance of normal
breast tissue. Normal cells in an organ like the breast become differentiated, meaning that they take on specific
shapes and forms that reflect their function as part of that organ. Pathologists describe cells as well differentiated
(low-grade), moderately differentiated (intermediate-grade), and poorly differentiated (high-grade) as the cells
progressively lose the features seen in normal breast cells.
● Stage. The TNM classification for staging breast cancer is based on the size of the cancer where it originally
started in the body and the locations to which it has travelled.
TNM stands for:
tumour
node
metastasis
● DNA-based classification. Understanding the specific details of a particular breast cancer may include looking
at the cancer cell DNA by several different laboratory approaches. When specific DNA mutations or gene
expression profiles are identified in the cancer cells this may guide the selection of treatments, either by targeting
these changes, or by predicting from these alterations which non-targeted therapies are most effective.
7. Proposed Method: Abstract
- Detection and classification of breast cancer at the cellular level is one of the most
challenging problems. Since the morphology and other cellular features of cancer
cells are different from normal healthy cells, it is possible to classify cancer cells
and normal cells using such features.
- The classical methods of segmentation and classification for malignant cells are not
only repetitive but also very time-consuming[2].
- Using PCA to select robust and informative features
[2]Khan, S.U., Islam, N., Jan, Z. et al. A machine learning-based approach for the segmentation and classification of malignant
cells in breast cytology images using gray level co-occurrence matrix (GLCM) and support vector machine (SVM). Neural
Comput & Applic (2020). https://doi.org/10.1007/s00521-021-05697-1
11. Breast Cancer Dataset
o Number of instances 569
o ID number of patients
o Diagnosis (M = Malignant, B = Benign)
o 30 Features
Ten Real values
a) radius (mean of distances from center to points on the perimeter)
b) texture (standard deviation of gray-scale values)
c) perimeter
d) area
e) smoothness (local variation in radius lengths)
f) compactness (perimeter^2 / area - 1.0)
g) concavity (severity of concave portions of the contour)
h) concave points (number of concave portions of the contour)
i) symmetry
j) fractal dimension
The mean, standard error, and "worst" or largest (mean of the three largest values) of these features were computed for
each image, resulting in 30 features. For instance, field 3 is Mean Radius, field 13 is Radius SE, field 23 is Worst Radius.
14. Unsupervised Linear Transformation or Dimensionality
Reduction (PCA)
We propose to use a combination of PCA with Cos similarity algorithms to find best features of
Cancer dataset named PCA-Cos algorithm. Principal Component Analysis (PCA), is well
known for reduction dimensional and statistical measurements in big data manipulating.
15. PCA (cont.)
Sometimes we need to "compress" our data to speed up algorithms or to visualize data. One way is to use
dimensionality reduction which is the process of reducing the number of random variables under
consideration by obtaining a set of principal variables.
Two approaches:
Feature selection: find a subset of the input variables.
Feature projection (also Feature extraction): transforms the data in the high-dimensional space to a space
of fewer dimensions. PCA is one of the methods following this approach.
16. PCA (cont.)
mathematically" (precisely)? We need to know about:
• Mean: finds the most balanced point in the data.
• Variance: measures the spread of data from the mean.
• Covariance: indicates the direction in that data are spreading.
17. PCA Algorithm
1.Subtract the mean to move to the original axes.
2.From the original data (a lot of features x1,x2,…,xN), we construct a covariance matrix U.
3.Find the eigenvalues λ1,λ2,… and correspondent eigenvectors v1,v2,… of that matrix (we call
them eigenstuffs). Choose K<N couples λ and v (the highest eigenvalues) and we get a reduced
matrix K<N.
4.Projection original data points to the K-dimensional plane created based on these new
eigenstuffs. This step creates new data points on a new dimensional space (K).
5.Now, instead of solving the original problem (N features), we only need to solve a new problem
with K features (K<N).
20. Cosine Similarity :
•A measure of similarity between two non-zero vectors of an inner
product space
•The cosine of the trigonometric angle between two vectors
•The inner product of two vectors normalized to length 1
•Not a measure of vector magnitude, just the angle between vectors
24. ◼Based on the experiments we can conclude that, The Cos
similarity learning can work effectively along with PCA
algorithm. By using this combination, the results obviously
improved. The accuracy rate without PCA is 78.9% about 24
false negatives values from whole testing instances. While
when using PCA the accuracy increased to 99.12% give
more acceptable findings to justify this combination. As a
result, a machine learning with effective feature selection
give a reliable outcome in more vital problem in the health
community.
CONCLUSION