diff --git a/README.md b/README.md
index 3fa2bf6..5a8780f 100644
--- a/README.md
+++ b/README.md
@@ -1,4 +1,4 @@
-# PyCNN: Cellular Neural Networks Image Processing Python Library
+# PyCNN: Image Processing with Cellular Neural Networks in Python
 
 **Cellular Neural Networks (CNN)** [[wikipedia]](https://en.wikipedia.org/wiki/Cellular_neural_network) [[paper]](http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7600) are a parallel computing paradigm that was first proposed in 1988. Cellular neural networks are similar to neural networks, with the difference that communication is allowed only between neighboring units. Image Processing is one of its [applications](https://en.wikipedia.org/wiki/Cellular_neural_network#Applications). CNN processors were designed to perform image processing; specifically, the original application of CNN processors was to perform real-time ultra-high frame-rate (>10,000 frame/s) processing unachievable by digital processors.
 
@@ -6,7 +6,7 @@ This python library is the implementation of CNN for the application of **Image
 
 **Note**: The library has been **cited** in the research published on [Using Python and Julia for Efficient Implementation of Natural Computing and Complexity Related Algorithms](http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7168488), look for the reference #19 in the references section. I'm glad that this library could be of help to the community.
 
-**Note**: Cellular neural network (CNN) must not be confused with completely different Convolutional neural network (ConvNet).
+**Note**: Cellular neural network (CNN) must not be confused with completely different convolutional neural network (ConvNet).
 
 ![alt text](http://www.isiweb.ee.ethz.ch/haenggi/CNN_web/CNN_figures/blockdiagram.gif "CNN Architecture")
 
@@ -31,9 +31,9 @@ Note: Scipy and Numpy can be installed on a Windows machines using binaries prov
 ## Usage
 *Image Processing* using CNN is simple using this library, just clone the repository and use the following code.
 ```python
-from cnnimg import cnnimg
+from pycnn import pycnn
 
-cnn = cnnimg()
+cnn = pycnn()
 
 cnn.edgedetection('input.bmp', 'output1.png')
 cnn.grayscaleedgedetection('input.bmp', 'output2.png')
@@ -98,12 +98,12 @@ Here, the input is the popular face in image processing field, Lenna.
 
 ## API
 ```python
-from cnnimg import cnnimg
+from pycnn import pycnn
 ```
 Import the module in your main file.
 
 ```python
-cnn = cnnimg()
+cnn = pycnn()
 ```
 Initialize the cnn class
 
diff --git a/example.py b/example.py
index a4d3993..53b8e90 100644
--- a/example.py
+++ b/example.py
@@ -1,9 +1,9 @@
 #!/usr/bin/python
 # -*- coding: utf-8 -*-
-from cnnimg import cnnimg
+from pycnn import pycnn
 
 # Initialize the cnn class
-cnn = cnnimg()
+cnn = pycnn()
 
 # Perform respective image processing techniques on the given image
 
diff --git a/example_lenna.py b/example_lenna.py
index 19d0292..35481d0 100644
--- a/example_lenna.py
+++ b/example_lenna.py
@@ -1,9 +1,9 @@
 #!/usr/bin/python
 # -*- coding: utf-8 -*-
-from cnnimg import cnnimg
+from pycnn import pycnn
 
 # Initialize the cnn class
-cnn = cnnimg()
+cnn = pycnn()
 
 # Perform respective image processing techniques on the given image
 
diff --git a/cnnimg.py b/pycnn.py
similarity index 99%
rename from cnnimg.py
rename to pycnn.py
index 6e7f9cb..67e930c 100644
--- a/cnnimg.py
+++ b/pycnn.py
@@ -35,7 +35,7 @@
 
 warnings.filterwarnings("ignore") # Ignore trivial warnings
 
-class cnnimg:
+class pycnn:
     def __init__(self):
         self.filetypes = ["jpeg", "jpg", "png", "tiff", "gif", "bmp"] # Supported filetypes
         return