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Brain Tumor Classification Using Keras
Ashish Arya
Ashish Arya
In this 2-hour-long guided project, we will use an efficient net model and train it on a Brain MRI dataset. This dataset has more than 3000 Brain MRI scans which are categorized in four classes - Glioma Tumor, Meningioma Tumor, Pituitary Tumor and No Tumor....
Read more
In this 2-hour-long guided project, we will use an efficient net model and train it on a Brain MRI dataset. This dataset has more than 3000 Brain MRI scans which are categorized in four classes - Glioma Tumor, Meningioma Tumor, Pituitary Tumor and No Tumor....
Read more
In this 2-hour-long guided project, we will use an efficient net model and train it on a Brain MRI dataset. This dataset has more than 3000 Brain MRI scans which are categorized in four classes - Glioma Tumor, Meningioma Tumor, Pituitary Tumor and No Tumor. Our objective in this project is to create an image classification model that can predict Brain MRI scans that belong to one of the four classes with a reasonably high accuracy. Please note that this dataset, and the model that we train in the project, is for educational purposes only.
Project Prerequisite: Before you attempt this project, you should be familiar with programming in Python. You should also have a theoretical understanding of Convolutional Neural Networks, and optimization techniques. This is a hands on, practical project that focuses primarily on implementation, and not on the theory behind Convolutional Neural Networks.
We will be carrying out the entire project on the Google Colab environment so you will need a free Gmail account to complete this project.
This Guided Project was created by a Coursera community member.
Register for this course and see more details by visiting:
OpenCourser.com/course/t69xwb/brain
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Using a machine learning dataset to build a practical model is an effective way to refine data science skills
This project allows learners to apply Python, convolutional neural networks, and optimization techniques all at once
Creating an image classification model to predict brain MRI is highly specialized and industry-relevant
Designed for those with a background in machine learning theory, the focus of the course is implementation
Prior experience with Google Colab is helpful for this particular course
Note that the dataset and model created are for educational purposes only
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Reviews summary
Excellent beginner-friendly brain mri classification course
The course is beginner-friendly, with a clear and structured explanation of the material. The instructor does a great job of guiding learners through the project, and the content is excellent. Overall, learners found this course to be very helpful and informative, and an excellent way to learn about brain MRI classification using Keras.
Well-explained
"good"
"excellent"
Very helpful
"Very helpful to clear out some steps in training image data"
Great for beginners
"I am an absolute beginner and I just followed along with Mr. Arya and was successful!"
Great content
"Excellent content, now I know how "simple" it is to build this type of model with such accuracy."
Explanation could be improved
"The main problem I encountered was the explanation part. The instructor seems to be reading the code as he types it."
Activities
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it. Supplement your coursework and achieve mastery of the topics covered
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activities:
Enroll in Guided Python Tutorial
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Sign up for a guided Python tutorial on a website like Codecademy or W3Schools.
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Complete the tutorial at your own pace, taking notes as needed.
Practice Python Exercises
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Solving coding exercises will provide hands-on practice with Python concepts covered in the course.
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Visit websites like LeetCode or HackerRank to find Python coding exercises.
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Choose a few exercises and try to solve them on your own.
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Review solutions and identify areas for improvement.
Develop a Basic Python Project
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Working on a project will allow you to apply the course concepts, foster creativity, and build a portfolio.
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Identify a simple Python project idea such as a calculator, text editor, or game.
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Design the project structure, including modules and functions.
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Implement the code and test it thoroughly.
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Refine the project based on testing and feedback.
Show all three activities
Enroll in Guided Python Tutorial
Show steps
This tutorial can help you brush up on Python basics like variables, functions, and data structures which will enhance your understanding of the course concepts.
Browse courses on
Python
Show steps
Show steps
Show steps
- Sign up for a guided Python tutorial on a website like Codecademy or W3Schools.
- Complete the tutorial at your own pace, taking notes as needed.
Practice Python Exercises
Show steps
Solving coding exercises will provide hands-on practice with Python concepts covered in the course.
Browse courses on
Python
Show steps
Show steps
Show steps
- Visit websites like LeetCode or HackerRank to find Python coding exercises.
- Choose a few exercises and try to solve them on your own.
- Review solutions and identify areas for improvement.
Develop a Basic Python Project
Show steps
Working on a project will allow you to apply the course concepts, foster creativity, and build a portfolio.
Browse courses on
Python
Show steps
Show steps
Show steps
- Identify a simple Python project idea such as a calculator, text editor, or game.
- Design the project structure, including modules and functions.
- Implement the code and test it thoroughly.
- Refine the project based on testing and feedback.
Show all three activities
Career center
Learners who complete Brain Tumor Classification Using Keras will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists are responsible for collecting, analyzing, and interpreting data to help businesses make informed decisions. This course provides a solid foundation in the fundamentals of image classification using Convolutional Neural Networks, which is essential for Data Scientists who work in the field of medical imaging analysis. By taking this course, learners will gain the skills necessary to develop and implement image classification models that can be used to diagnose diseases and improve patient care.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are responsible for designing, developing, and deploying machine learning models. This course provides a practical introduction to the concepts and techniques of Convolutional Neural Networks, which are widely used in machine learning applications such as image classification. By taking this course, learners will gain the skills necessary to build and train image classification models that can be used in a variety of industries.
Deep Learning Engineer
Deep Learning Engineer
Deep Learning Engineers are responsible for developing and implementing deep learning models. This course provides a hands-on introduction to the concepts and techniques of Convolutional Neural Networks, which are a fundamental component of deep learning models. By taking this course, learners will gain the skills necessary to build and train deep learning models that can be used in a variety of applications.
Computer Vision Engineer
Computer Vision Engineer
Computer Vision Engineers are responsible for developing and implementing computer vision systems. This course provides a comprehensive introduction to the concepts and techniques of Convolutional Neural Networks, which are widely used in computer vision applications such as image classification and object detection. By taking this course, learners will gain the skills necessary to build and train computer vision systems that can be used in a variety of applications.
Medical Imaging Analyst
Medical Imaging Analyst
Medical Imaging Analysts are responsible for analyzing medical images to diagnose diseases and monitor patient progress. This course provides a practical introduction to the concepts and techniques of Convolutional Neural Networks, which are widely used in medical imaging analysis. By taking this course, learners will gain the skills necessary to develop and implement image classification models that can be used to diagnose diseases and improve patient care.
Neurologist
Neurologist
Neurologists are responsible for diagnosing and treating disorders of the nervous system. This course provides a comprehensive introduction to the concepts and techniques of Convolutional Neural Networks, which are widely used in medical imaging analysis. By taking this course, learners will gain the skills necessary to develop and implement image classification models that can be used to diagnose and monitor neurological disorders.
Neurosurgeon
Neurosurgeon
Neurosurgeons are responsible for performing surgery on the brain and spinal cord. This course provides a practical introduction to the concepts and techniques of Convolutional Neural Networks, which are widely used in medical imaging analysis. By taking this course, learners will gain the skills necessary to develop and implement image classification models that can be used to plan and perform neurosurgical procedures.
Radiologist
Radiologist
Radiologists are responsible for interpreting medical images to diagnose diseases and monitor patient progress. This course provides a comprehensive introduction to the concepts and techniques of Convolutional Neural Networks, which are widely used in medical imaging analysis. By taking this course, learners will gain the skills necessary to develop and implement image classification models that can be used to diagnose diseases and improve patient care.
Biomedical Engineer
Biomedical Engineer
Biomedical Engineers are responsible for designing and developing medical devices and systems. This course provides a practical introduction to the concepts and techniques of Convolutional Neural Networks, which are widely used in medical imaging analysis. By taking this course, learners will gain the skills necessary to develop and implement image classification models that can be used to improve the design and development of medical devices and systems.
Software Engineer
Software Engineer
Software Engineers are responsible for designing, developing, and maintaining software applications. This course provides a hands-on introduction to the concepts and techniques of Convolutional Neural Networks, which are widely used in a variety of software applications such as image classification and object detection. By taking this course, learners will gain the skills necessary to build and train image classification models that can be used in a variety of industries.
Data Analyst
Data Analyst
Data Analysts are responsible for collecting, analyzing, and interpreting data to help businesses make informed decisions. This course provides a solid foundation in the fundamentals of image classification using Convolutional Neural Networks, which is essential for Data Analysts who work in the field of medical imaging analysis. By taking this course, learners will gain the skills necessary to develop and implement image classification models that can be used to diagnose diseases and improve patient care.
Statistician
Statistician
Statisticians are responsible for collecting, analyzing, and interpreting data to help businesses make informed decisions. This course provides a solid foundation in the fundamentals of image classification using Convolutional Neural Networks, which is essential for Statisticians who work in the field of medical imaging analysis. By taking this course, learners will gain the skills necessary to develop and implement image classification models that can be used to diagnose diseases and improve patient care.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts are responsible for using mathematical and analytical techniques to solve complex business problems. This course provides a solid foundation in the fundamentals of image classification using Convolutional Neural Networks, which is essential for Operations Research Analysts who work in the field of medical imaging analysis. By taking this course, learners will gain the skills necessary to develop and implement image classification models that can be used to diagnose diseases and improve patient care.
Business Analyst
Business Analyst
Business Analysts are responsible for analyzing business processes and identifying opportunities for improvement. This course provides a solid foundation in the fundamentals of image classification using Convolutional Neural Networks, which is essential for Business Analysts who work in the field of medical imaging analysis. By taking this course, learners will gain the skills necessary to develop and implement image classification models that can be used to diagnose diseases and improve patient care.
Quality Assurance Analyst
Quality Assurance Analyst
Quality Assurance Analysts are responsible for ensuring that products and services meet quality standards. This course provides a solid foundation in the fundamentals of image classification using Convolutional Neural Networks, which is essential for Quality Assurance Analysts who work in the field of medical imaging analysis. By taking this course, learners will gain the skills necessary to develop and implement image classification models that can be used to diagnose diseases and improve patient care.
For more career information including salaries, visit:
OpenCourser.com/course/t69xwb/brain
Reading list
We've selected seven books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Brain Tumor Classification Using Keras.
Provides a comprehensive introduction to deep learning, covering the fundamental concepts and techniques used in the field. It valuable resource for anyone looking to gain a deeper understanding of deep learning and its applications.
Save
Comprehensive reference on convolutional neural networks, covering the theory, algorithms, and applications of these networks. It valuable resource for anyone interested in learning more about convolutional neural networks and their use in image processing and computer vision.
Provides a practical introduction to machine learning using Scikit-Learn, Keras, and TensorFlow. It valuable resource for anyone looking to gain hands-on experience with machine learning and its applications.
Provides a gentle introduction to machine learning, covering the fundamental concepts and techniques used in the field. It valuable resource for anyone looking to gain a basic understanding of machine learning and its applications.
Provides a non-technical introduction to artificial intelligence, covering the history, the fundamental concepts, and the applications of AI. It valuable resource for anyone looking to gain a basic understanding of AI and its implications for society.
Provides a comprehensive introduction to deep learning for medical image analysis, covering the fundamental concepts and techniques used in the field. It valuable resource for anyone looking to gain a deeper understanding of deep learning and its applications in medical image analysis.
Provides a comprehensive introduction to deep learning for computer aided diagnosis, covering the fundamental concepts and techniques used in the field. It valuable resource for anyone looking to gain a deeper understanding of deep learning and its applications in computer aided diagnosis.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/t69xwb/brain
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Time
2 hours
Via
Coursera
Institution
Coursera Community Project Network
Instructor
Ashish Arya
Good to know
Using a machine learning dataset to build a practical model is an effective way to refine data science skills
This project allows learners to apply Python, convolutional neural networks, and optimization techniques all at once
Creating an image classification model to predict brain MRI is highly specialized and industry-relevant
Designed for those with a background in machine learning theory, the focus of the course is implementation
Prior experience with Google Colab is helpful for this particular course
Note that the dataset and model created are for educational purposes only
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