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I'm a computer vision student, YouTuber and online content creator

The original article on this link.

When doing image segmentation using CNNs, we often hear about the Dice coefficient, and sometimes we see the term dice loss. A lot of us get confused between these two metrics.

Physically they are the same, but when we look at their values we find that they are not the same!

The answer is very easy, but before talking about the difference between them, let’s talk about what is the dice coefficient because the dice loss is a special case of the dice coefficient.

Dice coefficient

When we do semantic segmentation for example, we want to…


This article will show you how to create an application that helps you crop any area from an image using Python and Tkinter.

The original article on this link.

If you don’t know how to create a desktop application using Tkinter then I recommend you to read this article, it can be useful for you before doing this application.

Introduction

Python is one of the most popular programming languages. Nowadays, you can find it in almost all the fields — data science, web development, machine learning, etc. And one of the utilities of Python is creating desktop applications. …


In this article, I will give you a quick way of how to convert a directory of Dicom files into one volume file (nifti).

The original article on my blog website here.

Image by MART PRODUCTION from Pexels

Introduction

In medical imaging, we use often two types of images, Dicom or Nifti which are the famous ones. But there is a difference between these two types of images or we can say files because they have more information than a normal image. If you want to know these differences then you can see this article.

Now if we want to create a nifti file, means that we will take a set of slices then put them together with the extension “.nii ”.

The process

To do this work you need only two lines…


You are having a lot of PDFs that you should read, but you don’t have time? so this solution is in this article. You can convert any PDFs into audio and you listen to it while doing something else.

The original article on my blog website here.

Image downloaded from Pexels

Introduction

As a student, scientist, or anyone who likes to read. We read PDFs almost every day. It can be a story, book, a paper…

But sometimes, we get bored from reading all the PDF especially the lazy people like me. For that, we prefer to listen to that PDF instead of reading it.

By doing this, we will make life easier, so that you can convert a PDF into an audio and you can listen to it while doing sport or something.

In this small article, I will show you how you…


The original article on my blog website here.

Abstract

Visualizing spectral images is difficult, often the spectral images are visualized as color images, but in this case, the information is limited. Because in the metameric case sometimes the colors are not distinguishable. For that, we can use a component that exists in the spectral images called the metameric black. This component contains some interesting information that we believe will help us to visualize the missing information of the spectral image. We use this information to propose a visualization that emphasizes metameric differences in colors. But the metameric black will have some…


In this article, I will give you some examples of using OpenCV and C++ for machine learning (Knn, SVM, BOW)

The original article on my blog website here.

If you have any trouble while installing OpenCV with C++, I made this video to show you how you can install it easily.

Knn with openCV

Definition

In artificial intelligence, more precisely in machine learning, the k nearest neighbors method is a supervised learning method.

In this framework, we have a training database made of N “input-output” pairs. To estimate the output associated to a new input x, the k nearest neighbors method consists in taking into account (in an identical way) the k training samples whose input is the closest to the new…


In this article, I will talk a little bit about how do the face and circle detection actually work.

The original article on my blog website here.

Created by the writer using CANVAS

If you have troubles with the OpenCV installation using C++, so I made a video for that (unique method).

How to install OpenCV with C++

Edge detection

Introduction

So in the first part, we’re going to make an edge detection using OpenCV, so in openCv there’s already an integrated function to calculate the edge with the “Canny” method, so to make this realization it is necessary to make the treatment of a frame by frame because we cannot make the treatment directly on the video then we are going to take only one frame…


You are having this problem while doing semantic image segmentation using PyTorch? Here is the solution that worked for me.

The original article on my blog website here.

Downloaded from Pexels

Introduction

For now, I’m doing a project about semantic tumor segmentation in the whole body in CT scans, but you know that doing this kind of problem is not easy so in this article, I will be talking about a famous error that you have probably faced when using PyTorch.

So looking at the error you can understand that the program is not specifying the exact part of the error. For me, I kept printing and printing shapes and values of the images and masks (labels) to see where the exact error was.


In this article, I will talk about how to create a simple deep learning model for linear regression.

The original article on my blog website here.

Introduction

As we know, deep learning has become a very important branch in the world and in all fields because they have found that it gives good results and with an accuracy close to 100%, the only problem with these networks is that to implement them we need to gather data (we are talking about thousands and more) because there are cases where we can’t always find the data to do the learning.

Generally, if we want to implement a deep learning model, we have to go through three steps: training, validation, and…


In this article, I will talk about a small project that I did this year, which is a Kaggle competition about how to classify 225 kinds of birds using a deep learning model.

The original article on my blog website here.

Abstract

The aim of this project is to classify 225 kinds of birds using a deep learning model for image classification. So for this work, we applied machine learning techniques to do the image recognition and the classification. We first addressed a classification problem by developing a pipeline. The solution we proposed for classification tasks was to use better features that were extracted by passing an image through a pre-trained convolutional neural network. We used Inception network to do this. Using these features we obtained very high accuracies

We also made use of…

Amine Mokhtari

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