AI With Zero Coding | Disease Detection with Google Teachable Machine (Full Project)

Summary

This video introduces a project focused on leveraging AI for healthcare, specifically for detecting and classifying chest diseases from x-ray images. Viewers are guided through the process of using Google Teachable Machine to train an AI model capable of identifying conditions like healthy lungs, COVID-19, bacterial pneumonia, and viral pneumonia, with zero coding required. The project emphasizes the reduction of time and costs in disease detection while demonstrating the ease of building, testing, and deploying AI models with pre-existing data. It also hints at deeper AI concepts to explore in future lectures.

Takeaways

• 😀 AI has the potential to reduce healthcare costs in the U.S. by $150 billion by 2026 and outperform expert doctors in many healthcare areas.
• 😀 AI can significantly improve the speed and accuracy of diagnosing diseases from X-ray images, MRI scans, and CT scans.
• 😀 The project focuses on automating the detection and classification of chest diseases from X-ray images using AI.
• 😀 The goal is to classify chest diseases into four categories: healthy, COVID-19, bacterial pneumonia, and viral pneumonia.
• 😀 You will work as a consultant hired by a hospital in Toronto to develop an AI model for fast disease classification using 133 X-ray images.
• 😀 Google Teachable Machine is used to build and train the AI model, with no coding required for the entire process.
• 😀 The AI model will be trained on 133 images per class, totaling 532 images to teach the model how to classify the diseases accurately.
• 😀 The model’s performance is tested and evaluated using a testing dataset, ensuring that the AI model has never seen the test data during training.
• 😀 Key AI training concepts covered include learning rate, epochs, batch size, accuracy, and loss, which affect the model's performance.
• 😀 After training, the AI model can be deployed and used to detect diseases from new X-ray images in under a minute, potentially reducing reliance on expert doctors.
• 😀 The project is designed to make AI accessible for healthcare improvements, allowing anyone to train and deploy AI models without coding knowledge.

Q & A

What is the main goal of the healthcare AI project discussed in the script?

The main goal of the project is to automate the process of detecting and classifying chest diseases from X-ray images using an AI model, in order to reduce the cost and time of detection.

How does AI help in improving healthcare according to the script?

AI can help improve healthcare by reducing costs, automating diagnostic processes, and improving the speed and accuracy of disease detection, particularly by outperforming expert human doctors in some areas, such as detecting diseases from X-ray images.

What are the four classes of chest diseases the AI model is designed to detect?

The four classes of chest diseases the AI model is designed to detect are Healthy, COVID-19, Bacterial Pneumonia, and Viral Pneumonia.

What role does Google Teachable Machine play in this project?

Google Teachable Machine is used to build, train, and deploy an AI model for detecting and classifying chest diseases based on X-ray images, all without requiring any coding skills.

How is the data for training the AI model organized in this project?

The data for training the AI model is organized into four categories: Healthy, COVID-19, Viral Pneumonia, and Bacterial Pneumonia, with 133 X-ray images for each class.

What are the key learning outcomes for this project?

The key learning outcomes are to build, train, and deploy AI models to detect and classify chest diseases using X-ray images, understand AI concepts like artificial neural networks, and evaluate the performance of the model using key performance indicators.

What is the significance of using 133 images for each class in the training data?

Using 133 images for each class ensures that the AI model is exposed to a sufficient number of examples from each disease category, allowing it to learn the characteristics of each class more effectively.

What is the process of testing the AI model after training?

After training, the AI model is tested using a separate set of images that the model has never seen before. These images are used to assess the model's accuracy and performance in classifying the chest diseases.

What does the term 'epoch' refer to in the context of AI model training?

An 'epoch' refers to one complete pass through the entire training dataset. It is a process during which the AI model updates its weights based on the data it processes to improve its predictions.

What is the importance of using a testing dataset that the model has never seen before?

Using a testing dataset that the model has never seen ensures that the model's performance is evaluated based on its ability to generalize to new, unseen data, rather than simply memorizing the training data.