--- a
+++ b/MI-DESS_IWTSE/ModelDCB.py
@@ -0,0 +1,59 @@
+# ==============================================================================
+# Copyright (C) 2023 Haresh Rengaraj Rajamohan, Tianyu Wang, Kevin Leung, 
+# Gregory Chang, Kyunghyun Cho, Richard Kijowski & Cem M. Deniz 
+#
+# This file is part of OAI-MRI-TKR
+#
+# This program is free software: you can redistribute it and/or modify
+# it under the terms of the GNU Affero General Public License as published
+# by the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU Affero General Public License for more details.
+
+# You should have received a copy of the GNU Affero General Public License
+# along with this program.  If not, see <https://www.gnu.org/licenses/>.
+# ==============================================================================
+#!/usr/bin/env python3
+
+from keras.models import Sequential,Model
+from keras.optimizers import SGD, Adam
+from keras.layers import Input#, Dropout, Dense, Conv3D, MaxPooling3D, GlobalMaxPooling3D ,GlobalAveragePooling3D, Activation, BatchNormalization,Flatten
+#from keras.models import Sequential
+#from keras.layers import Dropout, Dense, Conv3D, MaxPooling3D,GlobalMaxPooling3D, GlobalAveragePooling3D, Activation, BatchNormalization,Flatten
+from resnet3d import Resnet3DBuilder
+#from keras.regularizers import l2
+
+
+def generate_model(learning_rate = 1 * 10 **(-4)):
+    
+    print('**************************')
+    
+    
+    model = Resnet3DBuilder.build_resnet_18((48, 96, 18, 32), 1)
+    model.compile(loss='binary_crossentropy',
+                      metrics = ['accuracy'],
+                      optimizer = Adam(lr=learning_rate,beta_1=0.99, beta_2=0.999))#SGD(lr=1e-2, momentum = 0.9))
+    return model
+
+    #input1 = Input(shape=(384, 384, 36, 1))
+    #input2 = Input(shape=(352, 352, 144, 1))
+
+    #out = Resnet3DBuilder.build_resnet_18((48, 96, 18, 32),input1,input2,1)
+       
+    #merged_model = Model([input1, input2], out)
+    
+    merged_model.compile(loss='binary_crossentropy',
+                      metrics = ['accuracy'],
+                      optimizer = Adam(lr=learning_rate,beta_1=0.99, beta_2=0.999))#SGD(lr=1e-2, momentum = 0.9))
+    #loss='categorical_crossentropy'
+
+    return merged_model
+
+
+
+
+