Case Study 4 – Image Classification

A supervised learning problem to identify, interpret, and classify images

A Brief Overview of Our Client

Our client was a pioneering medical institution situated in Texas, United States. They are focused to serve the underserved populations in Texas. Each day they are leading the way for 21st-century medicine through clinical care, community impact, research, medical education, and innovation. Effectively classifying medical images play an important role in aiding clinical care and treatment. As a result, they were trying to interpret the value of image analysis and classification software into a range of diagnostic processes and disciplines.

Our Client's Barriers

Image analysis whether performed by humans or a machine can influence death or life decisions as doctors often depend on what they see as much as anything else in providing proper treatment. Consistent accuracy in medical treatments was a critical motto for them.

They find solutions to apparently impossible questions facing healthcare. Due to the high resolution of medical images and small dataset size, they suffer from limitations in the model layers, channels, and high conceptional cost.

In the traditional classification method, much effort and time are necessarily be spent on extracting and selecting classification features. After examining these problems, they decided to create a system that would automatically classify the medical images.

Our Client’s Objective

In addition to compatible accuracy, our client was aiming for an efficient model with high-level features that are extracted from a deep convolutional neural network and some selected traditional features to classify sundry medical images.

Solution We Offered

Classification between objects is an easy task for us, but it is undoubtedly a knotty task for machines. Therefore, image classification is a vital task within the computer vision field. Image classification can be accomplished by any machine learning algorithms such as SVM, logistic regression, and random forest. Our in-house experts have designed an AI-powdered image classification tool to be deployed in a wide range of diagnostic processes and disciplines including MRI, CT, ultrasound scanning, radiology, and diagnosis of skin diseases.

The tool predicts particular pathologies and compares new medical images with those on a patient’s historical record. Additionally, Machine learning for image classification is also a fight against cancer, especially for classifying breast lesions captured by ultrasound as either benign or malignant—a task traditionally falling on the shoulders, or rather the sight of doctors.

Our powerful and deep image classification tool is able to reduce some of the burdens. This is possible by visually identifying the presence or even absence of malignancy. This helps the medical imaging processes to reduce the need for invasive biopsy.

The Outcome

The client was fully satisfied with the image classification tool as the Medical image classification was one of the most critical problems in the image recognition area. The client was able to wisely categorize their medical images and help doctors in further research or disease diagnosis. We employed deep learning algorithms to achieve better performance.

If you are facing any image classification challenges in your medical operations, Ping us a mail with your detailed description of your requirements to