Rusty  Bernier

Rusty Bernier

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COVID-19 Detector Flask Chest X-rays and CT Scans using Deep Learning

Implementing AI based models and Flask app to detect COVID-19 in Chest X-rays and CT Scans using four Deep Learning Algorithms: VGG16…

#deep-learning #machine-learning #coronavirus #flask #covid19 #programming

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COVID-19 Detector Flask Chest X-rays and CT Scans using Deep Learning
Rusty  Bernier

Rusty Bernier

1592148152

COVID-19 Detector Flask Chest X-rays and CT Scans using Deep Learning

Implementing AI based models and Flask app to detect COVID-19 in Chest X-rays and CT Scans using four Deep Learning Algorithms: VGG16…

#deep-learning #machine-learning #coronavirus #flask #covid19 #programming

Margaret D

Margaret D

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Top Deep Learning Development Services | Hire Deep Learning Developer

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Mikel  Okuneva

Mikel Okuneva

1603735200

Top 10 Deep Learning Sessions To Look Forward To At DVDC 2020

The Deep Learning DevCon 2020, DLDC 2020, has exciting talks and sessions around the latest developments in the field of deep learning, that will not only be interesting for professionals of this field but also for the enthusiasts who are willing to make a career in the field of deep learning. The two-day conference scheduled for 29th and 30th October will host paper presentations, tech talks, workshops that will uncover some interesting developments as well as the latest research and advancement of this area. Further to this, with deep learning gaining massive traction, this conference will highlight some fascinating use cases across the world.

Here are ten interesting talks and sessions of DLDC 2020 that one should definitely attend:

Also Read: Why Deep Learning DevCon Comes At The Right Time


Adversarial Robustness in Deep Learning

By Dipanjan Sarkar

**About: **Adversarial Robustness in Deep Learning is a session presented by Dipanjan Sarkar, a Data Science Lead at Applied Materials, as well as a Google Developer Expert in Machine Learning. In this session, he will focus on the adversarial robustness in the field of deep learning, where he talks about its importance, different types of adversarial attacks, and will showcase some ways to train the neural networks with adversarial realisation. Considering abstract deep learning has brought us tremendous achievements in the fields of computer vision and natural language processing, this talk will be really interesting for people working in this area. With this session, the attendees will have a comprehensive understanding of adversarial perturbations in the field of deep learning and ways to deal with them with common recipes.

Read an interview with Dipanjan Sarkar.

Imbalance Handling with Combination of Deep Variational Autoencoder and NEATER

By Divye Singh

**About: **Imbalance Handling with Combination of Deep Variational Autoencoder and NEATER is a paper presentation by Divye Singh, who has a masters in technology degree in Mathematical Modeling and Simulation and has the interest to research in the field of artificial intelligence, learning-based systems, machine learning, etc. In this paper presentation, he will talk about the common problem of class imbalance in medical diagnosis and anomaly detection, and how the problem can be solved with a deep learning framework. The talk focuses on the paper, where he has proposed a synergistic over-sampling method generating informative synthetic minority class data by filtering the noise from the over-sampled examples. Further, he will also showcase the experimental results on several real-life imbalanced datasets to prove the effectiveness of the proposed method for binary classification problems.

Default Rate Prediction Models for Self-Employment in Korea using Ridge, Random Forest & Deep Neural Network

By Dongsuk Hong

About: This is a paper presentation given by Dongsuk Hong, who is a PhD in Computer Science, and works in the big data centre of Korea Credit Information Services. This talk will introduce the attendees with machine learning and deep learning models for predicting self-employment default rates using credit information. He will talk about the study, where the DNN model is implemented for two purposes — a sub-model for the selection of credit information variables; and works for cascading to the final model that predicts default rates. Hong’s main research area is data analysis of credit information, where she is particularly interested in evaluating the performance of prediction models based on machine learning and deep learning. This talk will be interesting for the deep learning practitioners who are willing to make a career in this field.


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Deep Learning in Healthcare — X-Ray Imaging

This is part 2 of the application of Deep learning on X-Ray imaging. Here the focus will be on understanding X-ray images — with a special focus on Chest X-rays.

Interpreting Chest X-Rays:

Figure 1. Chest X-Ray — 1) Lungs, 2) Right Hemidiaphragm, 3) Left Hemidiaphragm, 4) Right Atrium, 5) Left Atrium (By Diego Grez — Radiografía_pulmones_Francisca_Lorca.jpg, CC BY-SA 3.0, https://commons.wikimedia.org/w/index.php?curid=10302947. Editing by Author)

X-ray images are grayscale images, that is, the images have some pixels which are dark and some that are bright. In medical imaging terms, these are images that have values ranging from 0 to 255, where 0 corresponds to the completely dark pixels, and 255 corresponds to the completely white pixels.

Figure 2. The grayscale bar

Different values on the X-ray image correlate to different areas of density:

  1. Dark — Locations on the body which are filled with Air are going to appear black.
  2. Dark Grey — Subcutaneous tissues or fat
  3. Light Grey — Soft tissues like the heart and blood vessels
  4. Off White — Bones such as the ribs
  5. Bright White — Presence of metallic objects such as pacemakers or defibrillators

The way physicians interpret an image is by looking at the borders between the different densities. As in Figure 1, the ribs appear off-white because they are dense tissues, but since the lungs are filled with air, the lungs appear dark. Similarly below the lung is the hemidiaphragm, which is again a soft tissue and hence appears light grey. This helps us give a clear understanding of the location and extent of the lungs.

So, if two objects with different densities are present close to each other they can be demarcated in an X-ray image.

Now if something were to happen in the lungs, such as Pneumonia, then, the air dense lungs will change into water-dense lungs. This will cause the demarcation lines to fade since the pixel densities will start closing in on the grayscale bar.

For taking a chest X-ray, normally the patient is asked to stand, and the X-rays are shot from either front to back (Anterior-Posterior) or from back to front (Posterior-Anterior).

#artificial-intelligence #machine-learning #x-rays #deep-learning #deep learning

Chest X-Ray Abnormality Classification Using Monk AI

Key Tasks

In this blog post, we will be performing three main tasks:-

  • To create a binary classifier to classify the chest x-ray images as normal/abnormal.To compare three deep neural network architectures.To create a multi-label classifier to generate 14 disease labels and the respective confidence scores.

Three deep neural network architectures used by me are Vgg16, Resnet50, and MobileNet.

Table of Contents

1. Installing Monk

2. Downloading dataset

3. Importing Framework and libraries

4. Visualizing and Exploring the Samples Provided from Dataset

5. Visualizing and Exploring the Samples Provided from Dataset

6. Comparing

7. Infer


Installing Monk

We will start by setting up the Monk AI toolkit and its dependencies on the platform you are working with and I am using Google Colab as my environment.

!git clone https://github.com/Tessellate-Imaging/monk_v1.git
!cd monk_v1/installation/Misc && pip install -r requirements_colab.txt

Downloading the Dataset

After setting up the Monk toolkit the next step is to install Kaggle and download the NIH Chest X-Ray Dataset from Kaggle on our Colab notebook.

! pip install -q kaggle

To download any dataset from Kaggle we need to first download the kaggle.json file by going to MyAccount on Kaggle and download a new API. Then we will upload the JSON file on our Colab notebook.

from google.colab import files

files.upload()

Now we can download the zip file of the dataset from Kaggle and unzip it.

! mkdir ~/.kaggle
! cp kaggle.json ~/.kaggle/
! chmod 600 ~/.kaggle/kaggle.json
! kaggle datasets download -d 'nih-chest-xrays/sample'
! unzip -qq sample.zip

The dataset has a total of 15 classes (14 disease classes and 1 “no findings” class).

Image for post

A Chest X-Ray Image from NIH Chest X-Ray Dataset in Kaggle

Importing Frameworks and libraries

Monk provides us three major frameworks to work with i.e., Keras, Pytorch, and Mxnet. We are using Keras framework for this project and the Pandas library is used for visualizing and exploring the dataset. To set up a working directory of a project we initialize a prototype for the framework being used.

from keras_prototype import prototype
import pandas as pd

Visualizing and Exploring the Samples provided from Dataset

Two DataFrames were made, one had multi-labeled target values which comprised of 14 disease classes and 1 “no finding” class, for binary classification of images another DataFrame was formed by replacing the disease classes and the “no finding” class by **abnormal **and **normal **respectively

$ df=pd.read_csv('sample/sample_labels.csv')

$ for i in range(len(df)):
          df["Finding Labels"][i] = df["Finding Labels"][i].replace("|", ",");
$ df.to_csv("sample/kush1.csv", index=False)
$ for i in range(len(df)):
if df["Finding Labels"][i] == "No Finding":
df["Finding Labels"][i] = "Normal";
else:
               df["Finding Labels"][i] = "Abnormal";
$ df.to_csv("sample/kush2.csv",index=False)

#image-classification #monk #chest-x-ray #deep-learning #computer-vision #deep learning