Deep Mask For X-ray Based Heart Disease Classification
This work addresses heart disease classification for medical diagnosis, but it appears incremental as it applies existing methods like U-net to a specific dataset.
The authors tackled the problem of accurately classifying heart disease from X-ray images by developing a deep learning model that uses a U-net to generate masks focusing on the heart region, resulting in improved reliability and accuracy, though no concrete numbers are provided.
We build a deep learning model to detect and classify heart disease using $X-ray$. We collect data from several hospitals and public datasets. After preprocess we get 3026 images including disease type VSD, ASD, TOF and normal control. The main problem we have to solve is to enable the network to accurately learn the characteristics of the heart, to ensure the reliability of the network while increasing accuracy. By learning the doctor's diagnostic experience, labeling the image and using tools to extract masks of heart region, we train a U-net to generate a mask to give more attention. It forces the model to focus on the characteristics of the heart region and obtain more reliable results.