From 5fac63f89396a5493a94e7f8c0f40e26c1b86471 Mon Sep 17 00:00:00 2001 From: Anjana Tiha Date: Fri, 29 Mar 2019 18:01:29 -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 de528de..ae84ecd 100644 --- a/README.md +++ b/README.md @@ -9,7 +9,7 @@ Application : Image Recognition, Image Classification, Medical Imaging, B ### Description 1. Detected Malaria from segmented cells from the thin blood smear slide images collected from Malaria screening research activity by National Institutes of Health (NIH) with Deep Learning (Convolutional Neural Network) specifically by training pretrained model Nashnet completely from scratch. 2. Before feeding data into model, preprocessed and augmented image dataset containing 27,558 images (337MB) by adding random flips, rotations and shears. -3. After loading pretrainied model NasNetMobile, added global max pooling, global average pooling, flattened layer to output of trained model and concatenated them and also added dropout and batch normalization layers for regularization before adding final output layer - a dense layer with softmax activation and compiling with optimizer-Adam with learning rate-0.0001, metric-accuracy and loss-categorical crossentropy. +3. After loading pretrainied model NasNetMobile, added global max pooling, global average pooling, flattened layer to output of trained model and concatenated them. Also added dropout and batch normalization layers for regularization before adding final output layer - a dense layer with softmax activation and compiling with optimizer-Adam with learning rate-0.0001, metric-accuracy and loss-categorical crossentropy. 4. Trained for 10 iterations and attained training accuracy 96.47% and loss(categorical crossentrpy) 0.1026 and validation accuracy of 95.46% and loss 0.1385. #### Code