diff --git a/model.py b/model.py
index 7df5b6889..606a74ecc 100644
--- a/model.py
+++ b/model.py
@@ -9,8 +9,45 @@
 from keras.callbacks import ModelCheckpoint, LearningRateScheduler
 from keras import backend as keras
 
+def jaccard_distance(y_true, y_pred, smooth=100):
+    intersection = K.sum(K.abs(y_true * y_pred), axis=-1)
+    sum_ = K.sum(K.abs(y_true) + K.abs(y_pred), axis=-1)
+    jac = (intersection + smooth) / (sum_ - intersection + smooth)
+    return (1 - jac) * smooth
 
-def unet(pretrained_weights = None,input_size = (256,256,1)):
+def dice_coef(y_true, y_pred):
+    smooth = 1
+    y_true_f = K.flatten(y_true)
+    y_pred_f = K.flatten(y_pred)
+    intersection = K.sum(y_true_f * y_pred_f)
+    return (2. * intersection +smooth) / (K.sum(y_true_f) + K.sum(y_pred_f) +smooth)
+
+def dice_coef_loss(y_true, y_pred):
+    print("dice loss")
+    return 1-dice_coef(y_true, y_pred)
+def specificity(y_true,y_pred):
+    specificity=0
+    y_true_f = K.flatten(y_true)
+    y_pred_f = K.flatten(y_pred)
+    TP=K.sum(y_true_f*y_pred_f)
+    TN=K.sum((1-y_true_f)*(1-y_pred_f))
+    FP=K.sum((1-y_true_f)*y_pred_f)
+    FN=K.sum(y_true_f*(1-y_pred_f))
+    specificity=(TN)/((TN+FP))
+    return specificity
+def sensitivity(y_true,y_pred):
+    sensitivity=0
+    y_true_f = K.flatten(y_true)
+    y_pred_f = K.flatten(y_pred)
+    TP=K.sum(y_true_f*y_pred_f)
+    TN=K.sum((1-y_true_f)*(1-y_pred_f))
+    FP=K.sum((1-y_true_f)*y_pred_f)
+    FN=K.sum(y_true_f*(1-y_pred_f))
+    sensitivity=(TP)/((TP+FN))
+       
+    return sensitivity
+
+def unet(pretrained_weights = None,input_size = (256,256,3)):
     inputs = Input(input_size)
     conv1 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(inputs)
     conv1 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv1)
@@ -54,7 +91,7 @@ def unet(pretrained_weights = None,input_size = (256,256,1)):
 
     model = Model(input = inputs, output = conv10)
 
-    model.compile(optimizer = Adam(lr = 1e-4), loss = 'binary_crossentropy', metrics = ['accuracy'])
+    model.compile(optimizer = Adam(lr = 1e-4), loss = dice_coef_loss, metrics = ['accuracy'])
     
     #model.summary()
 
@@ -62,5 +99,3 @@ def unet(pretrained_weights = None,input_size = (256,256,1)):
     	model.load_weights(pretrained_weights)
 
     return model
-
-