The recent surge of Deep Learning has led to breakthrough advancements in almost every field of its application. A particular deep learning architecture, arguably the most popular one is the Convolution Neural Networks. The interest in convnets has seen an exponential increase due to their effectiveness and scalability. CNNs have become the go-to solution for image data problems and has provided results that are at par with if not better than human standards. The simplicity of the CNN architecture is another big factor of its success. The image processing and classification capabilities of CNN have found great usage in medical field, making it possible to detect and classify diseases as severe as Cancer effectively for the sake of better care. In this project, I’ve initiated an elaborate study of Convolution Neural Networks, built multiple architectures from scratch and furthered our understanding with the preparation of an elementary dog-cat CNN classifier model followed by a more extensive CNN model for detection of lung cancer in a patient. The project is built on Google’s interactive and versatile cloud platform for AI development Google Colaboratory, using the open-source neural network library ‘Keras’ for model development and libraries such as matplotlib and tensorboard (tensorflow) for result plotting and analysis. Data for training and testing our model was extracted from the ‘ LUNA 2016 medical image database ’. The model was tuned using Grid-Search and achieved over 97% test accuracy in its final iterations. To culminate,I have enlisted some future-work prospects like De-convolution/Translated-Convolution,implement one or more named CNN networks like Inception or Alexnet, test the model on larger images etc