A Deep Learning-Based Classification Framework for Annotated Histopathology Lung Cancer Images.

Cancer is the second leading cause of death globally, with one in six people dying from it. It occurs when abnormal cells divide uncontrollably and spread to other organs in the body. Lung cancer is one of the most common and deadliest types of cancer. Several methods, such as X-rays, CT scans, PET-CT scans, bronchoscopies, and biopsies, can be used to diagnose lung cancer. Studies have shown that the type of histology in lung cancer is linked to the diagnosis and treatment course, making early and accurate detection of lung cancer histology crucial for improving survival rates. Artificial intelligence (AI) can aid in the automation of cancer detection, allowing for the evaluation of more cases in less time and at a lower cost. The main objective of this research is to evaluate the effectiveness of a newly proposed CNN model in distinguishing between benign and malignant lung cancer images obtained from digital pathology. To conduct the experiment, the LC25000 dataset, containing 5000 images for each category, was utilized, resulting in a total of 10,000 images. The findings of the proposed CNN model were then compared to those of existing deep learning models, demonstrating its ability to accurately identify cancerous tissues with a maximum accuracy of 99.9% to 100%, while also reducing processing time. These outcomes can play a crucial role in the development of a precise and automated system for identifying various types of lung cancer.