Semantic Segmentation for Pneumothorax Detection & Segmentation

So, Here in this Blog, i will show you that how can we solve the healthcare problem by enabling the power of Deep Learning.

1. Introduction :

So here in this section we will discuss about computer vision,

Image for post

https://machinelearningmastery.com/what-is-computer-vision/

Computer vision is the simply the process of perceiving the images and videos available in the digital formats.

In Machine Learning (ML) and AI — Computer vision is used to train the model recognize certain patterns and store the data into their artificial memory to utilize the same for predicting the results in real-life use

The application of computer vision in artificial intelligence is becoming unlimited and now expanded into emerging fields like automotive, healthcare, retail, robotics, agriculture, autonomous flaying like drones and manufacturing etc…

Image for post

Machine Learning Jobs

So here in this blog by enabling the power of deep learning , we will show you that how we can solve one of the problem of computer vision called ‘ Image Segmentation ’

2.Image Segmentation :

Image for post

https://thegradient.pub/semantic-segmentation/

What is Image Segmentation?

Image Segmentation is a task of computer vision in which we partitioning images into different segments.

yes, sounds like Object detection , but no it different task than object detection …. Because Object Detection methods helps us draw bounding boxes around certain entities/Objects in Given Image ,

But on other side Image segmentation lets us achieve more detailed understanding of imagery than image classification or object detection.

in Simple words , in Image Segmentation we basically assign/classify each pixel to a particular class.

#computer-vision #neural-networks #artificial-intelligence #deep-learning #ai

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Semantic Segmentation for Pneumothorax Detection & Segmentation
Dominic  Feeney

Dominic Feeney

1621242214

Semantic Segmentation with TensorFlow Keras - Analytics India Magazine

(https://analyticsindiamag.com/google-arts-culture-uses-ai-to-preserve-endangered-languages/)

Semantic Segmentation laid down the fundamental path to advanced Computer Vision tasks such as object detectionshape recognitionautonomous drivingrobotics, and virtual reality. Semantic segmentation can be defined as the process of pixel-level image classification into two or more Object classes. It differs from image classification entirely, as the latter performs image-level classification. For instance, consider an image that consists mainly of a zebra, surrounded by grass fields, a tree and a flying bird. Image classification tells us that the image belongs to the ‘zebra’ class. It can not tell where the zebra is or what its size or pose is. But, semantic segmentation of that image may tell that there is a zebra, grass field, a bird and a tree in the given image (classifies parts of an image into separate classes). And it tells us which pixels in the image belong to which class.

In this article, we discuss semantic segmentation using TensorFlow Keras. Readers are expected to have a fundamental knowledge of deep learning, image classification and transfer learning. Nevertheless, the following articles might fulfil these prerequisites with a quick and clear understanding:

  1. Getting Started With Deep Learning Using TensorFlow Keras
  2. Getting Started With Computer Vision Using TensorFlow Keras
  3. Exploring Transfer Learning Using TensorFlow Keras

Let’s dive deeper into hands-on learning.

#developers corner #densenet #image classification #keras #object detection #object segmentation #pix2pix #segmentation #semantic segmentation #tensorflow #tensorflow 2.0 #unet

Chando Dhar

Chando Dhar

1619799996

Deep Learning Project : Real Time Object Detection in Python & Opencv

Real Time Object Detection in Python And OpenCV

Github Link: https://github.com/Chando0185/Object_Detection

Blog Link: https://knowledgedoctor37.blogspot.com/#

I’m on Instagram as @knowledge_doctor.

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#python project #object detection #python opencv #opencv object detection #object detection in python #python opencv for object detection

Wanda  Huel

Wanda Huel

1601280960

Statistical techniques for anomaly detection

Anomaly and fraud detection is a multi-billion-dollar industry. According to a Nilson Report, the amount of global credit card fraud alone was USD 7.6 billion in 2010. In the UK fraudulent credit card transaction losses were estimated at more than USD 1 billion in 2018. To counter these kinds of financial losses a huge amount of resources are employed to identify frauds and anomalies in every single industry.

In data science, “Outlier”, “Anomaly” and “Fraud” are often synonymously used, but there are subtle differences. An “outliers’ generally refers to a data point that somehow stands out from the rest of the crowd. However, when this outlier is completely unexpected and unexplained, it becomes an anomaly. That is to say, all anomalies are outliers but not necessarily all outliers are anomalies. In this article, however, I am using these terms interchangeably.

There are numerous reasons why understanding and detecting outliers are important. As a data scientist when we make data preparation we take great care in understanding if there is any data point unexplained, which may have entered erroneously. Sometimes we filter completely legitimate outlier data points and remove them to ensure greater model performance.

There is also a huge industrial application of anomaly detection. Credit card fraud detection is the most cited one but in numerous other cases anomaly detection is an essential part of doing business such as detecting network intrusion, identifying instrument failure, detecting tumor cells etc.

A range of tools and techniques are used to detect outliers and anomalies, from simple statistical techniques to complex machine learning algorithms, depending on the complexity of data and sophistication needed. The purpose of this article is to summarise some simple yet powerful statistical techniques that can be readily used for initial screening of outliers. While complex algorithms can be inevitable to use, sometimes simple techniques are more than enough to serve the purpose.

Below is a primer on five statistical techniques.

#anomaly-detection #machine-learning #outlier-detection #data-science #fraud-detection

Semantic Segmentation for Pneumothorax Detection & Segmentation

So, Here in this Blog, i will show you that how can we solve the healthcare problem by enabling the power of Deep Learning.

1. Introduction :

So here in this section we will discuss about computer vision,

Image for post

https://machinelearningmastery.com/what-is-computer-vision/

Computer vision is the simply the process of perceiving the images and videos available in the digital formats.

In Machine Learning (ML) and AI — Computer vision is used to train the model recognize certain patterns and store the data into their artificial memory to utilize the same for predicting the results in real-life use

The application of computer vision in artificial intelligence is becoming unlimited and now expanded into emerging fields like automotive, healthcare, retail, robotics, agriculture, autonomous flaying like drones and manufacturing etc…

Image for post

Machine Learning Jobs

So here in this blog by enabling the power of deep learning , we will show you that how we can solve one of the problem of computer vision called ‘ Image Segmentation ’

2.Image Segmentation :

Image for post

https://thegradient.pub/semantic-segmentation/

What is Image Segmentation?

Image Segmentation is a task of computer vision in which we partitioning images into different segments.

yes, sounds like Object detection , but no it different task than object detection …. Because Object Detection methods helps us draw bounding boxes around certain entities/Objects in Given Image ,

But on other side Image segmentation lets us achieve more detailed understanding of imagery than image classification or object detection.

in Simple words , in Image Segmentation we basically assign/classify each pixel to a particular class.

#computer-vision #neural-networks #artificial-intelligence #deep-learning #ai

Arno  Bradtke

Arno Bradtke

1601334000

Anomaly detection with Local Outlier Factor (LOF)

Today’s article is my 5th in a series of “bite-size” article I am writing on different techniques used for anomaly detection. If you are interested, the following are the previous four articles:

Today I am going beyond statistical techniques and stepping into machine learning algorithms for anomaly detection.

#outlier-detection #fraud-detection #data-science #machine-learning #anomaly-detection