Learn 3D Image Classification with Python and Keras
1: Learn 3D Image Classification with Python and Keras
Learn to predict viral pneumonia in CT scans with the help of 3D CNNs in Python and Keras : Hands-on
Welcome to the "Learn 3D Image Classification with Python and Keras" course. In this comprehensive and hands-on course, you will learn how to build a powerful 3D convolutional neural network (CNN) for classifying CT scans.
With the use of the Google Colab platform, Python, and Keras in TensorFlow, you will be able to effectively analyse medical images and predict the presence of viral pneumonia in computer tomography (CT) scans.
Medical imaging plays a vital role in disease diagnosis, and this course will provide you with the necessary skills and techniques to excel in this field. You will be able to tackle real-world challenges and gain a strong foundation in 3D image classification and deep learning.
This is an excellent opportunity for healthcare professionals, data scientists, and anyone looking to advance their AI skills.
By the end of this course, you will have a complete understanding of how to classify 3D images using Python and Keras. You will have a portfolio project that you can showcase to potential employers and be able to confidently apply your skills in a professional setting. With its clear and concise approach, this course is designed to maximize your learning potential in the shortest time possible.
Enroll now and take the first step towards a fulfilling career in 3D image classification and AI. Happy Learning!
Learn 3D Image Classification with Python and Keras | Udemy
What you'll learn
- Understanding of 3D image classification and its applications in medical imaging, specifically in classifying viral pneumonia in CT scans.
- Knowledge of how to use Python, Keras, and TensorFlow to build a 3D convolutional neural network (CNN) for image classification.
- Hands-on experience in pre-processing and preparing 3D images for input into a machine learning model.
- Understanding of the architecture and parameters used in a 3D convolutional neural network.
Requirements
- Basic knowledge of Python Programming
Who this course is for:
- Anyone who is interested in learning about 3D image classification and building a 3D convolutional neural network using Python, Keras, and TensorFlow on the Google Colab platform.
- AI enthusiasts who are eager to learn how to develop a deep learning model from scratch and want to apply their knowledge to the medical imaging domain.
- Data scientists and machine learning engineers who are interested in expanding their skill set in the field of medical imaging analysis and want to work on real-world projects.
- Healthcare professionals, such as radiologists and medical technicians, who are interested in utilizing advanced AI techniques to improve the accuracy of disease diagnosis from medical imaging data.
Engineer dedicated to utilizing the power of Machine learning and Deep learning to solve real-world problems, improve design and performance assessment. Over ten years of experience in engineering and R and D environment. Engineering professional with a focus on Multi-physics CFD-ML from IIT Madras. Experienced in implementing action-oriented solutions to complex business problem.
Learn 3D Image Classification with Python and Keras | Udemy
- On this page.
- Setup.
- Download and explore the dataset.
- Load data using a Keras utility. Create a dataset.
- Visualize the data.
- Configure the dataset for performance.
- Standardize the data.
- A basic Keras model. Create the model. Compile the model. Model summary. Train the model.
VGG16 is a pre-trained CNN model which is used for image classification. It is trained on a large and varied dataset and fine-tuned to fit image classification datasets with ease. Now, import a VGG16 model.
- Convolutional Neural Networks (CNNs) CNN's, also known as ConvNets, consist of multiple layers and are mainly used for image processing and object detection.
A 3D CNN is simply the 3D equivalent: it takes as input a 3D volume or a sequence of 2D frames (e.g. slices in a CT scan), 3D CNNs are a powerful model for learning representations for volumetric data.
- Step 1:- Import the required libraries. Here we will be making use of the Keras library for creating our model and training it. ...
- Step 2:- Loading the data. ...
- Step 3:- Visualize the data. ...
- Step 4:- Data Preprocessing and Data Augmentation. ...
- Step 6:- Evaluating the result.
Learn 3D Image Classification with Python and Keras | Udemy
Different from traditional machine learning, convolution neural network can be better used for image and time series data processing, especially for image classification and language recognition. The basic structure of convolution neural network is shown in Figure 4. Structure diagram of convolution neural network.
The Convolutional Neural Network (CNN or ConvNet) is a subtype of Neural Networks that is mainly used for applications in image and speech recognition. Its built-in convolutional layer reduces the high dimensionality of images without losing its information. That is why CNNs are especially suited for this use case.
Accuracy KNN method is 87,75%. While the detection accuracy used by CNN is 96,67%. The results obtained from these 2 methods can still be improved with advanced research namely with pre production on the set and the image used. The data set used has the same exposure level, image capture angle and image size.
INSTRUCTOR
Karthik K
