Artificial Intelligence: Developing a Custom Algorithm

"It seems probable that once the machine thinking method had started, it would not take long to outstrip our feeble powers." - Alan Turing

Neural Networks

Instead of reading about what machine learning is, here is a visual representation of a neural network.

Credits to https://adamharley.com

Building an Image Classification Algorithm with TensorFlow

During my final year at the university, I wanted to dive into the world of machine learning. My idea was to build an image classification algorithm that could distinguish between chest X-rays of patients with COVID-19 and those without it. This was not specific to X-rays but rather the algorithm itself. To accomplish this, I dived into convolutional neural networks (CNNs).

Convolutional neural networks, or CNNs for short, are the essence of image classification in the field of artificial intelligence. They're inspired by the human vision system and designed to process visual data, making them the perfect choice for image classification tasks. A CNN consists of multiple layers, including convolutional layers that learn to recognize features in the input data. These features might be simple, like edges and corners, or complex, like textures and shapes.

Choosing the Right Architecture: ResNet50V2

Selecting the right architecture for your CNN is crucial, and I opted for the ResNet50V2 architecture. It's a powerful and efficient model that has demonstrated exceptional performance in various image recognition tasks. Its residual learning approach enables it to handle complex datasets effectively, which is precisely what I needed for my COVID-19 classification project.

The Training Process

After the model was trained, it was time to put it to the test. I provided it with a new set of chest X-ray images it had never seen before. Watching the model's predictions was like witnessing the magic of machine learning in action. It accurately classified the images, identifying cases of COVID-19 with remarkable precision. This was the moment I realized the potential of AI.

Try it Yourself

Curious about the results? You can test the algorithm yourself. Check out the web interface below:
Machine Learning Web Application

In Conclusion

This journey into the world of machine learning, CNNs, and image classification has been a rewarding experience. Not only did it enhance my understanding of artificial intelligence, but it also showcased the potential for technology to make a positive impact on healthcare.