Powerful open-source deep learning framework instrument MXNet supports multiple languages like C++, Python, R, Julia, Perl etc MXNet supported by Intel, Dato, Baidu, Microsoft, Wolfram Research, and research institutions such as Carnegie Mellon, MIT, the University of Washington, and the Hong Kong University of Science and Technology Symbolic Execution: Static symbolic graph executor, which provides efficient symbolic graph execution and optimization. Supports an efficient deployment of a trained model to low-end devices for inference, such as mobile devices, IoT devices (using AWS Greengrass), Serverless (Using AWS Lambda) or containers.

1. MXNet image segmentation to
Predict and Diagnose the Cardiac Diseases
KANNAN.R,
Research Scholar, Department of Computer Science,
Rathinam College of Arts & Science,
Coimbatore, Tamil Nadu, India.
dschennai@outlook.com
PAPER PRESENTATION - ICACI-2018
KARPAGAM COLLEGE OF ENGINEERING,COIMBATORE,
TAMIL NADU, INDIA

2. Machine Learning Algorithms with ROC Curve for Predicting
and Diagnosing the Heart Disease
Machine LearningIntroduction Heart Diseases
Materials & Methods Experimental Evaluation Results & Conclusion
1 2 3
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3. I.Introduction
• Machine Learning
• Deep Learning
• Convolutional Neural Network (CNN)
• Deep Learning Frameworks
• Heart Diseases
• Materials and Methods
• Sunnybrook Cardiac Dataset
• Apache MXNet
• Logistic Regression
• Experimental Evaluation
• Results and Conclusion

4. II.Machine Learning
Traditional Programming Vs Machine Learning

5. II.Machine Learning
• Machine Learning
• "Field of study that gives computers the ability to learn without being explicitly programmed“ by Arthur Samuel
• Deep learning
• subfield of machine learning and Inspired by the structure and function of the brain
• Convolutional Neural Network (CNN)
• Used in image recognition.
• Deep Learning Frameworks
• Keras, Tensorflow, MXNet, Caffe, Torch, Microsoft Cognitive Toolkit

6. III. Heart Diseases
• Heart Diseases is now becoming the leading cause of mortality in India with a significant risk of both males and females.

7. IV(A). Sunnybrook Cardiac Dataset
The Sunnybrook Cardiac Data (SCD) is the Cardiac MR Left Ventricle Segmentation Challenge data and contains
the 45 MR scan images from a varied of patients and pathologies: healthy, hypertrophy, heart failure with
infarction and heart failure without infarction.
This dataset classified by the four pathological groups based on the below.
 Heart failure with infarction (HF-I) group had ejection fraction (EF) < 40 percentage and evidence of late
gadolinium (Gd) enhancement.
 Heart failure without infarction (HF) group had EF < 40 percentage and no late Gd enhancement.
 LV hypertrophy (HYP) group had normal EF (> 55 percentage) and a ratio of left ventricular (LV) mass over
body surface area is > 83 g/m2.
 Healthy (N) group had EF > 55 percentage and no hypertrophy.

8. IV (B). Apache MXNet
• Powerful open-source deep learning framework instrument
• MXNet supports multiple languages like C++, Python, R, Julia, Perl etc
• MXNet supported by Intel, Dato, Baidu, Microsoft, Wolfram Research, and research institutions such as Carnegie
Mellon, MIT, the University of Washington, and the Hong Kong University of Science and Technology
• Symbolic Execution: Static symbolic graph executor, which provides efficient symbolic graph execution and
optimization.
• Supports an efficient deployment of a trained model to low-end devices for inference, such as mobile devices, IoT
devices (using AWS Greengrass), Serverless (Using AWS Lambda) or containers.

9. IV (C). LOGISTIC REGRESSION
• Statistical method for analyzing a dataset in which there are one or more independent variables that determine an
outcome.
• The outcome is measured with a dichotomous variable (in which there are only two possible outcomes).

10. V . EXPERIMENTAL EVALUATION
We have applied and evaluated the three process to achieve the results such as
• SCD image preprocess,
• MXNet to Image segmentation and measure
• Logistic regression to classify the MXNet outcomes

11. V(A) . SCD IMAGE PREPROCESS
The Image preprocessing contained the two steps
• Mine the images and create them even for easier to see and process
• Catch valuable metadata fields that could use for the data-cleaning and final results in the first
process

12. V(B) . MXNET TO IMAGE SEGMENTATION & MEASURE
we are running the MXNet Deep learning framework for segment and measure the images without much struggle.
The followings list of developments are achieved the results in the second process.
• Separation nets are unbalanced statistically. We depend on batch normalization.
• Minor batch sizes returned balanced statistically. We established for batch size = 3
• Elastic deformations were easily the real augmentations
• The shortcut connection provide a symbolic growth in accuracy.
• We select upstream after the shortcut combines.
• Adding extra layers rapidly controlled to diminishing returns
• Adding extra filters per layer presented no development
• It is tough, if not difficult the net to over fit.
• Training time around 7 hours

13. V(B) . MXNET TO IMAGE SEGMENTATION & MEASURE
After many trial and errors, we have completed the network with help of RELU activations and batch
normalization each convolution. We have used padding for the convolutional layers for without reducing the
output shape.
A. Normal. B. Heavy contraction. C. Chamber only fairly surrounded by LV tissue D. and E. Uncommon.

14. V(C) . LOGISTIC REGRESSION TO CLASSIFY THE MXNET OUTCOMES
We are predicting and diagnosis based on the segment results with help of the below
• metadata such as height (in cm), weight (in kg),
• End-diastole (in ml),
• end-systole (ml),
• sex, age,
• slice-count,
• slice distance,
• image orientation,
• scan time,
• image size
• using logistic regression.

15. VI . RESULTS AND CONCLUSION
• 30% of the data is hold out as a testing data set that is not seen during the training stage of the data.
• During the training of Deep learning, The MXNet is used to maximize the segmentation and classify the appropriate
patients.
• In this study of predictions shows that logistic regression performs best and it can predict with 0.91 % of accuracy.

16. THANK YOU