A systematic review on feature extraction methods and deep learning models for detection of cancerous lung nodules at an early stage -the recent trends and challenges.

Abstract

Lung cancer is one of the most common life-threatening worldwide cancers affecting both the male and the female populations. The appearance of nodules in the scan image is an early indication of the development of cancer cells in the lung. The Low Dose Computed Tomography screening technique is used for the early detection of cancer nodules. Therefore, with more Computed Tomography (CT) lung profiles, an automated lung nodule analysis system can be utilized through image processing techniques and neural network algorithms. A CT image of the lung consists of many elements such as blood vessels, ribs, nodules, sternum, bronchi and nodules. These nodules can be both benign and malignant, where the latter leads to lung cancer. Detecting them at an earlier stage can increase life expectancy by up to 5 to 10 years. To analyse only the nodules from the profile, the respected features are extracted using image processing techniques. Based on the review, textural features were the promising ones in medical image analysis and for solving computer vision problems. The importance of uncovering the hidden features allows Deep Learning algorithms (DL) to function better, especially in medical imaging, where accuracy has improved. The earlier detection of cancerous lung nodules is possible through the combination of multi-featured extraction and classification techniques using image data. This technique can be a breakthrough in the deep learning area by providing the appropriate features. One of the greatest challenges is the incorrect identification of malignant nodules results in a higher false positive rate during the prediction. The suitable features make the system more precise in prognosis. In this paper, the overview of lung cancer along with the publicly available datasets is discussed for the research purposes. They are mainly focused on the recent research that combines feature extraction and deep learning algorithms used to reduce the false positive rate in the automated detection of lung nodules. The primary objective of the paper is to provide the importance of textural features when combined with different deep-learning models. It gives insights into their advantages, disadvantages and limitations regarding possible research gaps. These papers compare the recent studies of deep learning models with and without feature extraction and conclude that DL models that include feature extraction are better than the others.