def build_model(input_shape):
inputs = Input(input_shape)

c1 = Conv2D(8, (3, 3), activation='relu', padding='same') (inputs)
c1 = Conv2D(8, (3, 3), activation='relu', padding='same') (c1)
p1 = MaxPooling2D((2, 2)) (c1)

c2 = Conv2D(16, (3, 3), activation='relu', padding='same') (p1)
c2 = Conv2D(16, (3, 3), activation='relu', padding='same') (c2)
p2 = MaxPooling2D((2, 2)) (c2)

c3 = Conv2D(32, (3, 3), activation='relu', padding='same') (p2)
c3 = Conv2D(32, (3, 3), activation='relu', padding='same') (c3)
p3 = MaxPooling2D((2, 2)) (c3)

c4 = Conv2D(64, (3, 3), activation='relu', padding='same') (p3)
c4 = Conv2D(64, (3, 3), activation='relu', padding='same') (c4)

u71 = Conv2DTranspose(32, (2, 2), strides=(2, 2), padding='same') (c4)
u71 = concatenate([u71, c3])
c71 = Conv2D(32, (3, 3), activation='relu', padding='same') (u71)
c61 = Conv2D(32, (3, 3), activation='relu', padding='same') (c71)

u8 = Conv2DTranspose(16, (2, 2), strides=(2, 2), padding='same') (c71)
u8 = concatenate([u8, c2])
c8 = Conv2D(16, (3, 3), activation='relu', padding='same') (u8)
c8 = Conv2D(16, (3, 3), activation='relu', padding='same') (c8)

u9 = Conv2DTranspose(8, (2, 2), strides=(2, 2), padding='same') (c8)
u9 = concatenate([u9, c1], axis=3)
c9 = Conv2D(8, (3, 3), activation='relu', padding='same') (u9)
c9 = Conv2D(8, (3, 3), activation='relu', padding='same') (c9)

outputs = Conv2D(4, (1, 1), activation='sigmoid')(c9)


model = Model(inputs=[inputs], outputs=[outputs])
model.compile(optimizer='adam', metrics=[dice_coef], loss=bce_dice_loss)

return model