Northeastern Global News reports on a positive use for artificial intelligence, with a system that outperforms other techniques by helping doctors spot potentially deadly skin cancers with 99 percent accuracy:
To improve early detection, Northeastern University researchers turned to artificial intelligence. Divya Chaudhary, an assistant teaching professor of computer science at Northeastern’s Seattle campus, and Peng Zhang, a graduate student in the Khoury College of Computer Sciences, developed a new and highly efficient hybrid system called the SegFusion Framework to help doctors spot melanoma more quickly and accurately.
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Chaudhary and Zhang combined the capabilities of two powerful deep learning models that use many layers of connected algorithms to recognize patterns and make decisions from large amounts of data. One of them highlights suspicious spots in skin images, while the other analyzes those areas to decide whether they are cancerous.
When tested against other popular AI approaches, SegFusion consistently came out on top. On the International Skin Imaging Collaboration 2020 dataset, for example, it correctly identified melanoma with 99.01% accuracy….
To connect the two models, the researchers built a “data bridge.” First, the segmentation model produces a black-and-white mask of a suspicious area. The bridge overlays the mask onto the original images so the second model can better analyze the highlighted region and classify it as cancerous or not.
The two models currently work one after the other, with some manual steps in between. The team’s goal is to merge them into a fully automated system that streamlines the process from image capture to diagnosis.
Looking ahead, Chaudhary and her students want to expand the system by adding patients’ health records, such as blood pressure and oxygen levels, to improve accuracy even further. They also hope to create an app for dermatologists, allowing the AI to run quietly in the background and assist with real-time decisions during checkups.