AI and Deep Learning Made Easy for Medical Imaging

Hello everyone.

Welcome to this session on Deep Learning in Healthcare — where artificial intelligence meets medicine in ways that are reshaping how we diagnose, treat, and care for patients.

Over the last decade, deep learning — a branch of AI inspired by how the human brain works — has moved from theory into real-world applications. Let’s begin our journey into the world of deep learning and its growing impact on the future of healthcare.

In this seesion, we'll explore what deep learning is, how it works, where it's being applied in healthcare today.

I'm excited to welcome you to this session on Core Concepts of Deep Learning – part 1 with the belief that you have a clear understanding of the key buzz words

Whether you’re new to AI or already working in the space, understanding the foundational principles of deep learning is key to unlocking its true potential.

In this talk, we’ll break down what deep learning really is, and understand the essential building blocks that power technologies like self-driving cars, virtual assistants, and even medical diagnostics.

We’ll touch on artificial neurons, activation functions, layers, backpropagation, and key architectures, in a way that's intuitive and practical.

By the end of this session, you'll have a clear roadmap of deep learning fundamentals, and how they all fit together to create intelligent systems.

So let’s get started into this session.”

In our previous lecture, we examined the fundamental components of neural network which also extends to DL, including the role of neurons, the significance of weights and biases as learnable parameters, the contribution of activation functions in modeling non-linear relationships, and the function of loss metrics in guiding the learning process. These elements together form the structural and functional basis of a neural network model .

In this session, we will advance our exploration by studying additional core elements that underpin the design, training, and optimization of deep learning systems. A clear understanding of these concepts is essential, as they provide the theoretical and practical framework required to transition from simply understanding neural networks to effectively constructing and applying them in real-world contexts.

This is an  interesting lecture on modelling a simple NN for mapping XOR function. The XOR (exclusive OR) function serves as a fundamental benchmark in neural network research, illustrating the necessity of non-linear modeling. This study demonstrates the design and training of a simple neural network to approximate the XOR mapping. By incorporating a hidden layer with non-linear activation functions, the model effectively captures the non-linear separability inherent in the XOR problem, highlighting the representational power of multi-layer networks.

“Have you ever wondered how a computer can look at an image and say, ‘This is a brain tumor,’ or ‘That’s a dog’?

This power comes from Convolutional Neural Networks, or CNNs — one of the most exciting innovations in Deep Learning.

Deep Learning is a branch of Artificial Intelligence that mimics how the human brain learns from experience, using multiple layers of artificial neurons.
Within this family of deep models, CNNs are the specialists — designed to understand and interpret visual data like images and videos.

Think of Deep Learning as the big umbrella, and CNNs as one of its most powerful architectures, focused on vision.

CNNs automatically learn to detect patterns — from simple edges and textures in the early layers, to complex shapes and meaningful objects in deeper layers.

They’ve transformed how we analyze medical scans, identify diseases, and even enable self-driving cars to ‘see’ the world.

In short, CNNs are the eyes of Deep Learning, turning pixels into understanding.”

In this lecture we’ll learn the core concepts of CNN.

“Now that we’ve understood the theory behind Convolutional Neural Networks, it’s time to move from concept to code.

In this session, we’ll build a CNN model to classify Chest X-Ray images, one of the most widely used diagnostic tools in healthcare.

Our goal is to see how deep learning can automatically learn patterns from X-ray images — such as identifying signs of pneumonia — without manual feature extraction.

Through coding, we’ll explore how each layer of the CNN contributes to feature learning and classification.

By the end, we’ll have a working deep learning model that demonstrates the power of AI in medical image diagnosis.”