Image Recognition with Deep Neural Networks and its Use Cases

What is image recognition and computer vision?

Image recognition (or image classification) is the task of identifying images and categorizing them in one of several predefined distinct classes. So, image recognition software and apps can define what’s depicted in a picture and distinguish one object from another.

The field of study aimed at enabling machines with this ability is called computer vision. Being one of the computer vision (CV) tasks, image classification serves as the foundation for solving different CV problems, including:

Image classification with localization — placing an image in a given class and drawing a bounding box around an object to show where it’s located in an image.

Medical image analysis

Software powered by deep learning models help radiologists deal with a huge workload of interpreting various medical images: computed tomography (CT) and ultrasound scans, magnetic resonance imaging (MRI), or x-rays. IBM stresses that an emergency room radiologist must examine as many as 200 cases every day. Besides that, some medical studies contain up to 3,000 images. No wonder that medical images account for nearly 90 percent of all medical data.

AI-based radiology tools don’t replace clinicians but support their decision-making. They flag acute abnormalities, identify high-risk patients or those needing urgent treatment so that radiologists can prioritize their worklists.

IBM Research division in Haifa, Israel, is working on Cognitive Radiology Assistant for medical image analysis. The system analyzes medical images and then combines this insight with information from the patient’s medical records, and presents findings that radiologists can take into account when planning treatment.