Transforming Lung Carcinoma Analysis: Deep Learning‘s Efficient Segmentation and Classification

Cancerous cells can develop in the lungs, a condition known as lung carcinoma. One of the most deadly and challenging malignancies to cure, lung cancer often spreads to other parts of the body at an early stage. The World Health Organisation reports that for several decades, lung cancer has topped all cancers globally in terms of incidence. Early detection and treatment of lung cancer are of the utmost importance. More effective treatments, less intrusive surgeries, and longer survival times may result from this. This paper presents a method for efficiently segmenting and classifying lung carcinomas using convolutional neural networks (CNNs) for the purpose of identifying malignant tumors. Here, additional effective methods such as an adaptive median filter for preprocessing, histogram equalization, edge-based segmentation, and Convolutional Neural Network (CNN) for successful classification are used to identify the lung carcinoma. To make segmentation more efficient, we use the Adaptive Median Filter in conjunction with the Histogram Equalization technique. It is also possible to transfer the important properties to the classification algorithm by deriving them from feature similarity. When compared to another prediction model that is already in use, the suggested model has the potential to attain superior accuracy. Reduced time and effort spent on detecting lung cancer are two benefits of the enhanced performance.