From 937f226b02d9aa5c0e4556350e2896a7249162cb Mon Sep 17 00:00:00 2001 From: Jack Xu Date: Sat, 12 Jun 2021 20:40:55 -0400 Subject: [PATCH] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 2b7c9c3..c077264 100644 --- a/README.md +++ b/README.md @@ -36,7 +36,7 @@ - To further improve the performance, we start to engineer the dataset to better utilize the model we use: - The initial thought is that the provided image has RGB channels exactly same to provide a black and white image, hence three channels have duplicated information, which is redundant for **Resnet34**. - In classical computer vision, we would use morphological operators (dilation and erosion) to extract features from the image. In addition, we figure out whether patient has COVID-19 based on the abnormal features within the chest scan. As a result, the idea is to provide **Resnet34** a sense of where the the chest region is and where the features are, with dilation and erosion respectively. Hence, we can utilize the three channels with R:(gray image), G:(erosion image), B:(dilation image), and the **Resnet34** can now fully utilize all three channels to produce a better prediction: - ![dataset](img/dataset.png) + ![dataset](img/rgb.png) - Sample training dataset becomes: ![Training Sample](output/CUSTOM-MODEL/v6-custom-3/plot_training-sample.png) - As a result, the performance is quite well: