Health Care

The project aims to develop an engine that can automatically detect & remove rubber stamp from scanned/captured document images.. There are many challenging that we have to cope with in this project. For example, no any standards for rubbers so far (e.g. the variety of rubber shapes, colors) or especially dealing with both scanned and captured images are also a big challenging. After trying several methods and considering between two important metrics (accuracy and performance), we finally deployed YOLOv3 for object detection step and making use of K-means scikit-learn and OpenCV for output generation.

The project aims to build an algorithm to detect a visual signal for pneumonia in medical images. Specifically, the algorithm needs to automatically locate lung opacities on chest radiographs. The dataset size is set around 23,124 images while the validation size is 2,560 images. To solve the problem, we built our own U-Net with the enhancement of resblock to improve the accuracy of the algorithm. The result based testing dataset (1000 images) is very positive (f2 score ~0.2)