--- a
+++ b/classification.py
@@ -0,0 +1,111 @@
+# -*- coding: utf-8 -*-
+"""Classification.ipynb
+**
+ * This file is part of Hybrid CNN-LSTM for COVID-19 Severity Score Prediction paper.
+ *
+ * Written by Ankan Ghosh Dastider and Farhan Sadik.
+ *
+ * Copyright (c) by the authors under Apache-2.0 License. Some rights reserved, see LICENSE.
+ */
+"""
+from keras.applications import ResNet152V2,DenseNet201,NASNetMobile,Xception
+#from keras.applications import DenseNet121
+from keras.layers.merge import concatenate
+import tensorflow as tf
+
+IMG_WIDTH = 128
+IMG_HEIGHT = 128
+IMG_CHANNELS = 3
+ 
+#Build the model
+#Branch 1
+inputs = Input((IMG_HEIGHT, IMG_WIDTH, IMG_CHANNELS))
+
+#s = Lambda(lambda x: x / 255)(inputs)
+s=inputs
+
+#Make 3 positional Arguments
+c1 = Conv2D(256, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(s)
+p1=  MaxPool2D(pool_size=(2,2))(c1)
+p1=  Dropout(0.2)(p1)
+
+mid1 = p1
+
+c1_1= Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(p1) #64,128
+
+c2=  Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(s)
+p2=   MaxPool2D(pool_size=(2,2))(c2)
+#p2= Dropout(0.5)(p2)
+
+mid2 = p2
+mid2 = Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(mid2)
+mid2 = Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(mid2)
+mid2 =  Dropout(0.2)(mid2)
+P1_R = MaxPool2D(pool_size=(2,2))(mid2)
+
+              
+R1=concatenate([c1_1,p2])
+R1.shape
+
+#R1=Dropout(0.5)(R1) #Extra
+            
+C1_R=Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(R1)
+mid1 = Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(mid1)
+mid1 = Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(mid1)
+mid1 =  Dropout(0.2)(mid1)
+            
+mid1_1 = concatenate([C1_R,mid1])
+
+C11_R=Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(mid1_1)
+C11_R=Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(C11_R)
+C11_R = MaxPool2D(pool_size=(2,2))(C11_R)
+C11_R =  Dropout(0.2)(C11_R)
+
+mid2_1 = concatenate([C11_R,P1_R])
+
+mid2_1 = Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(mid2_1)
+x = Conv2D(16, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(mid2_1)
+
+densenet = DenseNet201(weights='imagenet', include_top=False)
+
+# input = Input(shape=(SIZE, SIZE, N_ch))
+#x = Conv2D(3, (3, 3), padding='same',activation='relu')(s)
+#x = Conv2D(3, (3, 3), padding='same',activation='relu')(x)
+#x = Conv2D(3, (3, 3), padding='same',activation='relu')(x)
+x = (Flatten())(x)
+
+#branch 2
+
+c_b_1=Conv2D(3, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(s)
+
+branch_2 = densenet(c_b_1)
+    
+branch_2 = GlobalAveragePooling2D()(branch_2)
+branch_2= BatchNormalization()(branch_2)
+branch_2 = Dropout(0.5)(branch_2)
+branch_2= Dense(256, activation='relu')(branch_2)
+
+#concatenate model
+
+final=concatenate([x,branch_2])
+final = BatchNormalization()(final)
+final = Dropout(0.2)(final)
+final = Dense(1024, activation='relu')(final)
+final= Dropout(0.2)(final)
+final= Dense(512, activation='relu')(final)
+final= Dropout(0.2)(final) #Extra
+final= Dense(128, activation='relu')(final)
+final= Dropout(0.5)(final) #Extra
+final= Dense(64, activation='relu')(final)
+final= Dropout(0.5)(final) #Extra
+
+#multi output
+output = Dense(3,activation = 'softmax', name='root')(final)
+      
+# model
+model = Model(inputs,output)
+
+optimizer = Adam(lr=0.002, beta_1=0.9, beta_2=0.999, epsilon=0.1, decay=0.0)#lr=0.002
+model.compile(loss='categorical_crossentropy', optimizer=optimizer, metrics=['accuracy'])#kullback_leibler_divergence#categorical_crossentropy
+model.summary()
+