--- a
+++ b/src/model_defs/vgg16.py
@@ -0,0 +1,48 @@
+############## Model Definition goes here ##############
+num_chan_in = 2
+height = 224
+width = 224
+num_classes = 6
+
+inputs = K.layers.Input([height, width, num_chan_in], name="DICOM")
+
+params = dict(kernel_size=(3, 3), activation="relu",
+                      padding="same",
+                      kernel_initializer="he_uniform")
+
+
+convA = K.layers.Conv2D(name="convAa", filters=64, **params)(inputs)
+convA = K.layers.Conv2D(name="convAb", filters=64, **params)(convA) #stride defaults to (1,1) for k.layers.conv
+poolA = K.layers.MaxPooling2D(name="poolA", pool_size=(2, 2))(convA) #stride defaults to pool size
+
+convB = K.layers.Conv2D(name="convBa", filters=128, **params)(poolA)
+convB = K.layers.Conv2D(name="convBb", filters=128, **params)(convB)
+poolB = K.layers.MaxPooling2D(name="poolB", pool_size=(2, 2))(convB)
+
+
+convC = K.layers.Conv2D(name="convCa", filters=256, **params)(poolB)
+convC = K.layers.Conv2D(name="convCb", filters=256, **params)(convC)
+poolC = K.layers.MaxPooling2D(name="poolC", pool_size=(2, 2))(convC)
+
+convD = K.layers.Conv2D(name="convDa", filters=512, **params)(poolC)
+convD = K.layers.Conv2D(name="convDb", filters=512, **params)(convD)
+poolD = K.layers.MaxPooling2D(name="poolD", pool_size=(2, 2))(convD)
+
+convE = K.layers.Conv2D(name="convEa", filters=512, **params)(poolD)
+convE = K.layers.Conv2D(name="convEb", filters=512, **params)(convE)
+poolE = K.layers.MaxPooling2D(name="poolE", pool_size=(2, 2))(convE)
+
+# img size after 5 MaxPooling (2,2) = 16*16*512 = 131072 post flattening layer
+flat = K.layers.Flatten()(poolC) # 131072
+
+dense1 = K.layers.Dense(4096, activation="relu")(flat) #VGG paper used dim which was a factor of 6.125, we followed similar strategy
+dense2 = K.layers.Dense(4096, activation="relu")(dense1)
+dense3 = K.layers.Dense(num_classes, activation="sigmoid")(dense2) 
+
+
+model = K.models.Model(inputs=[inputs], outputs=[dense3])
+opt = K.optimizers.Adam(learning_rate=0.01)
+model.compile(loss=K.losses.categorical_crossentropy,
+              optimizer=opt,
+              metrics=['accuracy'])
+