diff --git a/README.md b/README.md
index ebff0ee..3b08176 100644
--- a/README.md
+++ b/README.md
@@ -8,9 +8,15 @@ Application : Image Recognition, Image Classification, Medical Imaging, B
### Description
-1. Detected Malaria from microscopic tissue images by completely retraining pretrained model (Google's "NASNet") from scratch.
-2. For training, concatenated global max pooling, global average pooling, flattened output, then added a dense layer with batch normalization and dropout and give to the output layer for final output prediction.
-3. Attained validation accuracy of 95.72% and loss 0.1385 on 27K+ (330MB+) image malaria dataset.
+1. Detected Malaria from segmented cells from the thin blood smear slide images with Deep Learning (Convolutional Neural Network).
+2. For training, used Malaria Dataset from Malaria screening research activity by National Institutes of Health (NIH).
+2. Before feeding data into model, preprocessed and augmented image dataset containing 27,558 images (337MB) by adding random flips, rotations and shears.
+3. For training, used pretrained model Nashnet and trained completely from scratch.
+4. After loading pretrainied model NasNetMobile, added global max pooling, global average pooling, flattened layer to output of trained model and concatenated them.
+5. Added dropout and batch normalization layers for regularization.
+6. Added final output layer with - a dense layer with softmax activation and compiled with optimizer Adam with learning rate 0.001, metric- accuracy and loss-categorical crossentropy.
+7. Trained for 10 iterations and attained training accuracy 96.47% and loss(categorical crossentrpy) 0.1026 and 7validation accuracy of 95.46% and loss 0.1385.
+
#### Code
@@ -25,7 +31,7 @@ Portfolio : Anjana Tiha's
Dataset Name : Malaria Cell Images Dataset
Dataset Link : Malaria Cell Images Dataset (Kaggle)
-Original Dataset : Malaria Datasets -National Institutes of Health (NIH)
+Original Dataset : Malaria Datasets - National Institutes of Health (NIH)
### Dataset Details