Investigation of Deep Learning based Lung Cancer Detection using Histopathological Images

Medicine and healthcare have advanced significantly in the past four decades. We discovered the underlying causes of numerous illnesses at this time, produced efficient therapies, and invented novel diagnostic instruments. Cancer is still one of the biggest problems we confront in spite of these advancements. It accounts for almost one out of every six deaths globally, making it the second most common cause of mortality. With more than 25% of all cases, lung cancer is not only the most prevalent but also the deadliest of all malignancies. Early detection the key to raising survival rate. The likelihood of successful therapy is greatly increased when cancer is detected in its early stages. In order to make cancer diagnosis quicker, more accurate, and less expensive, this study investigates the application of artificial intelligence (AI). In order to classify five different types of lungs, tissues are two benign (non-cancerous) and three malignant (cancerous)—we offer a framework for analysing histopathological pictures using contemporary Deep Learning (DL) techniques and Digital Image Processing (DIP). By improving diagnostic precision, this method seeks to assist healthcare providers in more accurately detecting cancer.