Deep Learning in Lung and Colon Cancer classifications

The past few decades have seen revolutionary advancements in the field of Healthcare and Medicine. During this period, the underlying reasons for many fatal diseases have been unveiled, novel diagnosis techniques were invented, and medical solutions were developed. Instead of these breakthroughs, we are still vulnerable and haunted by certain diseases like cancer. Globally, cancer is the second most common cause of mortality which accounts for the death of one in every six affected individuals. Among many other types, the colon and lung are the most prevailing and life-threatening ones. Only these two variants are responsible for 25% of all cancer cases around the world. However, early lung and colon cancer detection can significantly enhance the possibility of survival. Currently, Deep Learning (DL) has facilitated the automated identification of cancer cells which is allowing us to assess more patients in a minimum time and without the help of a medical professional. The study has put forward a DL framework to classify and differentiate five types of colon and lung cancer cells. Digital Image processing (DIP) and Deep Learning (DL) algorithms are used to process and evaluate histopathological images of cancer cells. The proposed framework has obtained a maximum testing accuracy of 98.3% in identifying and classifying cancer cells. By employing the proposed model in practice, medical experts will be able to integrate an accurate and automated system for diagnosing different forms of colon and lung cancers.