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Ryan Ahmed
In this project, we will train deep neural network model based on Convolutional Neural Networks (CNNs) and Residual Blocks to detect the type of Diabetic Retinopathy from images. Diabetic Retinopathy is the leading cause of blindness in the working-age population of the developed world and estimated to affect over 347 million people worldwide. Diabetic Retinopathy is disease that results from complication of type 1 & 2 diabetes and can develop if blood sugar levels are left uncontrolled for a prolonged period of time. With the power of Artificial Intelligence and Deep Learning, doctors will be able to detect blindness before it occurs.
Register for this course and see more details by visiting:
OpenCourser.com/course/knpd6o/diabetic
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What's inside
Syllabus
Retinopathy Detection Using Deep Learning
In this project, we will train a deep neural network model based on Convolutional Neural Networks (CNNs) and Residual Blocks to detect the type of Diabetic Retinopathy from images. Diabetic Retinopathy is a disease that results from complication of type 1 and 2 diabetes. The disease can develop if blood sugar levels are left uncontrolled for a prolonged period of time. It is caused by the damage of blood vessels in the retina which is located in the back of patient’s eyes. Diabetic Retinopathy is the leading cause of blindness in the working-age population of the developed world and is estimated to affect over 347 million people worldwide.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Suitable for students, professionals, and anyone interested in the healthcare industry
Provides strong foundation in diabetic retinopathy detection using deep learning
Led by Ryan Ahmed as an instructor, who is recognized for their work in healthcare and technology
Emphasizes the practical application of deep neural network models for medical diagnosis
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Reviews summary
Practical ai project
Learners say this is a practical and useful project suitable for learners with some experience in Python. The instructor's explanations are clear, and the step-by-step approach is highly praised. However, some argue that it could go into more depth on neural networks and theory.
Clear instructor teaching.
"Instructor could've talked more in depth about the neural networks and how it works."
"Some parts of the theory and intuition video were confusing."
"The course is very nicely explained."
"BIG THUMB UP for the instructor , who explain the whole concept including almost every line of code with the required details for one to digest the topics easily."
Prerequisites in Python and CNN helpful.
"I would recommend this project for those who have some prior experience in Python and CNN to try out this exciting real world project."
Course is practical and hands-on.
"very practical also informative."
"very useful and practical application - the instructor explanations were very clear and covered everything. "
"Well Instructed Project."
"The course is very nicely explained."
Issues accessing necessary project files.
"not able to access files now as i need them to complete the project"
"It is no problem with this project course. But one thing is dissapoined about this course is never provide the resources, such like the notebook that I have completed. Since I have pay, I think I have the absolutely right to obtained what I have been done for this project."
Lacks depth in neural networks and theory.
"Instructor could've talked more in depth about the neural networks and how it works."
"Some parts of the theory and intuition video were confusing."
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Diabetic Retinopathy Detection with Artificial Intelligence with these
activities:
Join a Study Group for Diabetic Retinopathy Detection
Show steps
Engaging with peers will provide opportunities for knowledge exchange, collaboration, and support.
Browse courses on
Deep Learning
Show steps
-
Find or create a study group with other students taking this course.
-
Meet regularly to discuss course materials, share insights, and work on projects together.
Review Fundamentals of Deep Learning
Show steps
Reviewing these topics will build a stronger foundational understanding and prepare you for the more advanced concepts covered in this course.
Browse courses on
Deep Learning
Show steps
-
Revisit the key concepts of deep neural networks, such as convolutional neural networks (CNNs) and residual blocks.
-
Go over the basics of image processing and computer vision.
-
Review the different types of deep learning models and their applications.
Read 'Deep Learning for Computer Vision' by Jason Brownlee
Show steps
This book provides a comprehensive overview of deep learning techniques for computer vision and will enhance your understanding of the concepts covered in this course.
View
Concurrent NumPy in Python: Faster NumPy With...
on Amazon
Show steps
-
Read the chapters on CNNs, residual networks, and image segmentation.
-
Work through the exercises and examples provided in the book.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Implement CNN and Residual Block Architectures
Show steps
Implementing these architectures from scratch will help you solidify your understanding of their functionality and how they contribute to the effectiveness of deep learning models.
Browse courses on
Convolutional Neural Networks
Show steps
-
Choose a programming language and framework for deep learning, such as Python with TensorFlow or Keras.
-
Create a simple CNN architecture and train it on a small dataset.
-
Add residual blocks to the CNN architecture and observe the improvement in accuracy.
Attend a workshop on Deep Learning for Healthcare
Show steps
Attending a workshop will provide you with additional insights, practical examples, and networking opportunities.
Browse courses on
Deep Learning
Show steps
-
Research and identify relevant workshops.
-
Register and attend the workshop.
-
Actively participate in discussions and ask questions.
Build a Diabetic Retinopathy Detection Model
Show steps
Building a complete model will provide hands-on experience in applying the concepts and techniques learned in this course to a real-world problem.
Browse courses on
Deep Learning Applications
Show steps
-
Collect a dataset of diabetic retinopathy images.
-
Preprocess the images and extract relevant features.
-
Train and evaluate a deep learning model for diabetic retinopathy detection.
-
Deploy the model and test its performance on new data.
Write a Blog Post on Advancements in Diabetic Retinopathy Detection
Show steps
Creating a blog post will help you synthesize your knowledge, communicate complex concepts, and share your insights with others.
Browse courses on
Deep Learning Applications
Show steps
-
Research and gather information on recent advancements in diabetic retinopathy detection.
-
Organize your thoughts and outline the key points of your post.
-
Write a clear and engaging blog post that explains the advancements and their implications.
Contribute to an Open-Source Diabetic Retinopathy Detection Project
Show steps
Contributing to open-source will provide you with real-world experience and allow you to make a meaningful impact on the field.
Browse courses on
Open Source
Show steps
-
Identify an open-source project related to diabetic retinopathy detection.
-
Review the project's documentation and codebase.
-
Identify an area where you can contribute, such as bug fixing, feature development, or documentation improvement.
Join a Study Group for Diabetic Retinopathy Detection
Show steps
Engaging with peers will provide opportunities for knowledge exchange, collaboration, and support.
Browse courses on
Deep Learning
Show steps
Show steps
Show steps
- Find or create a study group with other students taking this course.
- Meet regularly to discuss course materials, share insights, and work on projects together.
Review Fundamentals of Deep Learning
Show steps
Reviewing these topics will build a stronger foundational understanding and prepare you for the more advanced concepts covered in this course.
Browse courses on
Deep Learning
Show steps
Show steps
Show steps
- Revisit the key concepts of deep neural networks, such as convolutional neural networks (CNNs) and residual blocks.
- Go over the basics of image processing and computer vision.
- Review the different types of deep learning models and their applications.
Read 'Deep Learning for Computer Vision' by Jason Brownlee
Show steps
This book provides a comprehensive overview of deep learning techniques for computer vision and will enhance your understanding of the concepts covered in this course.
View
Concurrent NumPy in Python: Faster NumPy With...
on Amazon
Show steps
Show steps
Show steps
- Read the chapters on CNNs, residual networks, and image segmentation.
- Work through the exercises and examples provided in the book.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Implement CNN and Residual Block Architectures
Show steps
Implementing these architectures from scratch will help you solidify your understanding of their functionality and how they contribute to the effectiveness of deep learning models.
Browse courses on
Convolutional Neural Networks
Show steps
Show steps
Show steps
- Choose a programming language and framework for deep learning, such as Python with TensorFlow or Keras.
- Create a simple CNN architecture and train it on a small dataset.
- Add residual blocks to the CNN architecture and observe the improvement in accuracy.
Attend a workshop on Deep Learning for Healthcare
Show steps
Attending a workshop will provide you with additional insights, practical examples, and networking opportunities.
Browse courses on
Deep Learning
Show steps
Show steps
Show steps
- Research and identify relevant workshops.
- Register and attend the workshop.
- Actively participate in discussions and ask questions.
Build a Diabetic Retinopathy Detection Model
Show steps
Building a complete model will provide hands-on experience in applying the concepts and techniques learned in this course to a real-world problem.
Browse courses on
Deep Learning Applications
Show steps
Show steps
Show steps
- Collect a dataset of diabetic retinopathy images.
- Preprocess the images and extract relevant features.
- Train and evaluate a deep learning model for diabetic retinopathy detection.
- Deploy the model and test its performance on new data.
Write a Blog Post on Advancements in Diabetic Retinopathy Detection
Show steps
Creating a blog post will help you synthesize your knowledge, communicate complex concepts, and share your insights with others.
Browse courses on
Deep Learning Applications
Show steps
Show steps
Show steps
- Research and gather information on recent advancements in diabetic retinopathy detection.
- Organize your thoughts and outline the key points of your post.
- Write a clear and engaging blog post that explains the advancements and their implications.
Contribute to an Open-Source Diabetic Retinopathy Detection Project
Show steps
Contributing to open-source will provide you with real-world experience and allow you to make a meaningful impact on the field.
Browse courses on
Open Source
Show steps
Show steps
Show steps
- Identify an open-source project related to diabetic retinopathy detection.
- Review the project's documentation and codebase.
- Identify an area where you can contribute, such as bug fixing, feature development, or documentation improvement.
Career center
Learners who complete Diabetic Retinopathy Detection with Artificial Intelligence will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers use their knowledge of machine learning, computer science, and statistics to build and implement machine learning models to solve complex problems and improve existing systems. This course may be useful for Machine Learning Engineers who want to apply their skills to the healthcare industry. The course will help them understand the basics of diabetic retinopathy and how to use deep learning to detect it.
Epidemiologist
Epidemiologist
Epidemiologists study the causes and distribution of diseases and injuries in populations. This course may be useful for Epidemiologists who want to learn more about diabetic retinopathy and how to use deep learning to detect it. The course will help them understand the basics of diabetic retinopathy and how to use deep learning to detect it.
Biostatistician
Biostatistician
Biostatisticians use statistics to analyze data in the health sciences. This course may be useful for Biostatisticians who want to learn more about diabetic retinopathy and how to use deep learning to detect it. The course will help them understand the basics of diabetic retinopathy and how to use deep learning to detect it.
Ophthalmologist
Ophthalmologist
Ophthalmologists are medical doctors who specialize in the diagnosis and treatment of eye diseases. This course may be useful for Ophthalmologists who want to learn more about diabetic retinopathy and how to use deep learning to detect it. The course will help them understand the basics of diabetic retinopathy and how to use deep learning to detect it.
Optometrist
Optometrist
Optometrists are healthcare professionals who provide primary eye care. This course may be useful for Optometrists who want to learn more about diabetic retinopathy and how to use deep learning to detect it. The course will help them understand the basics of diabetic retinopathy and how to use deep learning to detect it.
Medical Imaging Specialist
Medical Imaging Specialist
Medical Imaging Specialists use imaging technologies to diagnose and treat diseases. This course may be useful for Medical Imaging Specialists who want to learn more about diabetic retinopathy and how to use deep learning to detect it. The course will help them understand the basics of diabetic retinopathy and how to use deep learning to detect it.
Data Scientist
Data Scientist
Data Scientists use their knowledge of statistics, computer science, and data analysis to extract insights from data. This course may be useful for Data Scientists who want to apply their skills to the healthcare industry. The course will help them understand the basics of diabetic retinopathy and how to use deep learning to detect it.
Public Health Researcher
Public Health Researcher
Public Health Researchers study the causes and prevention of diseases and injuries. This course may be useful for Public Health Researchers who want to learn more about diabetic retinopathy and how to use deep learning to detect it. The course will help them understand the basics of diabetic retinopathy and how to use deep learning to detect it.
Healthcare Technology Specialist
Healthcare Technology Specialist
Healthcare Technology Specialists help healthcare organizations implement and use new technologies. This course may be useful for Healthcare Technology Specialists who want to learn more about diabetic retinopathy and how to use deep learning to detect it. The course will help them understand the basics of diabetic retinopathy and how to use deep learning to detect it.
Software Engineer
Software Engineer
Software Engineers design, develop, test, and maintain software systems. This course may be useful for Software Engineers who want to work on healthcare-related software. The course will help them understand the basics of diabetic retinopathy and how to use deep learning to detect it.
Healthcare Administrator
Healthcare Administrator
Healthcare Administrators plan, direct, and coordinate the provision of healthcare services. This course may be useful for Healthcare Administrators who want to learn more about diabetic retinopathy and how to use deep learning to detect it. The course will help them understand the basics of diabetic retinopathy and how to use deep learning to detect it.
Medical Doctor
Medical Doctor
Medical Doctors diagnose and treat diseases and injuries. This course may be useful for Medical Doctors who want to learn more about diabetic retinopathy and how to use deep learning to detect it. The course will help them understand the basics of diabetic retinopathy and how to use deep learning to detect it.
Health Policy Analyst
Health Policy Analyst
Health Policy Analysts develop and analyze policies to improve the health of populations. This course may be useful for Health Policy Analysts who want to learn more about diabetic retinopathy and how to use deep learning to detect it. The course will help them understand the basics of diabetic retinopathy and how to use deep learning to detect it.
Healthcare Consultant
Healthcare Consultant
Healthcare Consultants help healthcare organizations improve their operations and efficiency. This course may be useful for Healthcare Consultants who want to learn more about diabetic retinopathy and how to use deep learning to detect it. The course will help them understand the basics of diabetic retinopathy and how to use deep learning to detect it.
Clinical Research Coordinator
Clinical Research Coordinator
Clinical Research Coordinators manage clinical research studies. This course may be useful for Clinical Research Coordinators who want to learn more about diabetic retinopathy and how to use deep learning to detect it. The course will help them understand the basics of diabetic retinopathy and how to use deep learning to detect it.
For more career information including salaries, visit:
OpenCourser.com/course/knpd6o/diabetic
Reading list
We've selected eight 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
Diabetic Retinopathy Detection with Artificial Intelligence.
Save
Provides a comprehensive overview of deep learning for image analysis, covering topics such as convolutional neural networks, recurrent neural networks, and generative adversarial networks. It valuable resource for anyone interested in learning more about this field.
Save
Provides a comprehensive overview of computer vision algorithms and applications. It covers topics such as image formation, feature extraction, object detection, and image segmentation. It valuable resource for anyone interested in learning more about this field.
This textbook provides a comprehensive overview of ophthalmology, including a chapter on diabetic retinopathy. It valuable resource for ophthalmologists and other healthcare professionals who treat patients with eye diseases.
Save
Provides an overview of the state-of-the-art in artificial intelligence in medicine. It discusses the use of AI in various medical applications, including diagnosis, treatment, and drug discovery.
Provides a comprehensive overview of ophthalmology, including the diagnosis and management of diabetic retinopathy. It is written in a clear and concise style and is well-illustrated with high-quality images.
Provides a comprehensive overview of pattern recognition and machine learning algorithms. It covers topics such as supervised learning, unsupervised learning, and reinforcement learning. It valuable resource for anyone interested in learning more about this field.
Provides a practical guide to deep learning using Python. It covers topics such as convolutional neural networks, recurrent neural networks, and generative adversarial networks. It valuable resource for anyone interested in learning more about this field.
Provides an overview of machine learning in healthcare. It covers topics such as supervised learning, unsupervised learning, and reinforcement learning. It valuable resource for anyone interested in learning more about this field.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/knpd6o/diabetic
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Effort
2 hours
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Ryan Ahmed
Language
English
Good to know
Suitable for students, professionals, and anyone interested in the healthcare industry
Provides strong foundation in diabetic retinopathy detection using deep learning
Led by Ryan Ahmed as an instructor, who is recognized for their work in healthcare and technology
Emphasizes the practical application of deep neural network models for medical diagnosis
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