Deep learning technique for early detection of lung cancer

Cancer is a deadly illness brought on by a confluence of many metabolic anomalies and genetic disorders. One of the main causes of death and disability among people nowadays is lung cancer. When choosing the appropriate course of action, the histopathological identification of such malignancies is typically the most crucial factor. Early diagnosis of a disease on either front significantly reduces death rates. Researchers can investigate a large number of patients in a much shorter amount of time and at a cheaper cost by using machine learning and deep learning techniques to speed up such cancer diagnosis. To effectively detect lung cancer, we developed a hybrid ensemble feature extraction model in this study. It combines high-performance filtering for cancer imaging collections with deep feature extraction and ensemble learning. Histopathological (LC25000) lung and colon datasets are used to assess the model. The results of the study show that our hybrid model has a 99.63% accuracy rate in detecting lung cancer. The results of the investigation demonstrate that our suggested approach performs noticeably better than current approaches. These models may therefore be used in clinics to assist physicians in diagnosing cancer. KEYWORDS: Deep learning, Feature extraction, Hand-Crafted Descriptors, lung cancer, and Transfer learning.