Automatic Detection of Various Types of Lung Cancer Based on Histopathological Images Using a Lightweight End-to-End CNN Approach

Lung cancer is one of the main causes of death and illness, and malignant lung tumours are the leading cause of both. According to reports, lung cancer incidence is on the rise. Lung cancer histopathology is an important element of patient care. Using artificial intelligence methods for the identification of lung cancer can become a highly valuable approach. In this article, we offer a modified lightweight end-to-end deep learning strategy based on convolutional neural networks (CNN) to accurately identify lung cancer. In this method, the input histopathology pictures are normalized before being fed into the CNN model, which is then used to detect lung cancer. The effectiveness of our approach is assessed using a publicly accessible database of histopathological pictures and compared to the most advanced cancer detection methods already in use. The examination of the results indicates that the suggested deep model for lung cancer diagnosis yields results of 0.995 percent, which is a better accuracy than other approaches. Due to this excellent outcome, our method is computationally effective.