Niramai Health Analytix, a Bengaluru-based startup is creating an AI-powered software system for breast cancer screening. Niramai is using following technologies to achieve the objective of breast cancer screening:
- Thermal image processing using thermal sensing device (thermal camera)
- Machine learning algorithm
- Hardware devices integrated with real-time cloud-based diagnostics; These hardware devices are capable of capturing thermal images
What/How of Thermal Image Processing?
Thermal image processing, also termed as thermal imaging, is a method of improving visibility of objects in a dark environment by detecting the objects’ infrared radiation and creating an image based on that information. source: techtarget.
The key to capturing thermal images of an object is a heat sensor (also called as thermal camera) which is capable of detecting tiny differences in the temperature. Given the fact that all objects emit infrared energy (heat), also termed as heat signature, thermal camera (heat sensor) collects the infrared radiation from objects in the scene and creates an electronic / digital image based on information about the temperature differences.
How could thermal imaging and machine learning go hand-in-hand?
Given above, it is understood that thermal imaging would result in an electronic / digital image of an object. These images can be fed into machine learning algorithm and train a model based on supervised learning technique. Deep learning algorithms such as Convolutional neural network can be used to classify these images to different categories. If you are planning to create AI solution by feeding images, you could try cloud AI solutions such as some of the following:
- AWS machine learning services (Apache MXNet on AWS, Amazon Rekognition)
- Google Vision
Thermal image processing + machine learning -> Niramai solution for Breast Cancer Classification
Niramai uses a high resolution thermal sensing device to capture thermal images. These images are, then, sent over internet to cloud storage system. The images are then fed into machine learning model for training, testing and optimizing the model.
Technology Landscape of Niramai Health Analytix
The following is how the technology landscape some developing AI solution such as Niramai may look like:
- Thermal imaging devices (hardware)
- Big data technologies for data processing
- Machine learning algorithms
- Cloud-computing for storage, large-scale processing
- Web application for accessing the results
Check out my latest book titled as First Principles Thinking: Building winning products using first principles thinking.
- Agentic Reasoning Design Patterns in AI: Examples - October 18, 2024
- LLMs for Adaptive Learning & Personalized Education - October 8, 2024
- Sparse Mixture of Experts (MoE) Models: Examples - October 6, 2024
I found it very helpful. However the differences are not too understandable for me