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Top1. Introduction
The most common cancer that claims lives in both men and women is lung cancer. According to American Cancer Society statistics, there are 220,000 new cases each year, 160,000 people die from the disease, and 15% of people with all stages of the disease survive for 5 years. However, the localized stage has a 5-year longevity rate of roughly 50%. In the localized stage, cancer does not spread outside the body, such as to lymph nodes (Aboamer et al., 2019; Ajeil et al., 2020a; Habibifar et al., 2019) The specific kind of tumors as well as additional factors like prognostics general health, etc., all have an impact on the 5-year survival rate. The main determinant of lung cancer survival rate is early recognition. Before lung cancer spreads to other parts of the body, symptoms do not manifest in the lung. Lung cancer is detected using various techniques, including microarray data analysis, sputum analysis, Computed Tomography (CT) scans, and chest radiography. Lung cancer identification with widespread chest CT screening is a promising technique.
A recent infectious disease called COVID-19 has been circulating all over the world (Azar & Hassanien, 2022; Abbas et al., 2022; Abdelmalek et al., 2018). The pandemic has had several health impacts, including economic loss, disruption of communication and information systems, social distancing, and quarantine procedures. Quarantine is an example of a confinement policy used to maintain a safe distance between people. The isolation step involves the treatment of people with suspected symptoms so that they can return to normal conditions; governments maintain medical facilities during this phase.
Along with the extensive usage of Machine Learning and Deep Learning approaches, the various feature selection methods have been utilized in statistics and pattern recognition for several years. (Waleed et al., 2022; Wen et al., 2022; Aboamer et al., 2014b; Acharyulu et al., 2021; Ahmadian et al., 2021; Ajeil et al., 2020b; Hamida et al., 2022a; Azar, 2020a,b). When there was an excessive amount of data that needed to be processed quickly, feature selection techniques were necessary (Azar et al., 2023d). These feature selection techniques were utilized to achieve the objectives of increasing classifier accuracy, decreasing dimensionality, removing superfluous and unrelated data, and more. It also aided in enhancing data comprehension and reducing the time required to run learning algorithms.
Deep learning techniques make very small elements in images visible that would not otherwise exist. “Convolutional Neural Networks (CNNs)” are the top excellent among academics for classification-related tasks in medical imaging issues because of their prowess in deep feature extraction and learning. CNNs are useful for detecting the features that discriminate different objects from each other. (Aboamer et al., 2014a; Ali et al., 2022b). However, CNNs are not suitable for applications with high learning capacity and large amounts of data as they are very sensitive to hyperparameters. Moreover, it is needed to consider the amount of data, since neural networks have a large complexity and require much time to process a dataset. These factors can make it challenging for practitioners to manually adjust these hyper-parameters so that they can be optimized effectively.
A heuristic is a method designed to solve a problem more quickly when more conventional methods are inefficient. (Ajeil, et al., 2020a; Al-Qassar et al., 2021a; Amara et al., 2019; Elkholy et al., 2020a; Azar & Banu, 2022). A black-box optimizer known as a meta-heuristic algorithm is given a collection of issue variables, including some restrictions in the form of limitations. The optimizer changes these variables by performing an updating procedure up until it finds an objective function's optimal value. The result is a close-to-optimal solution that has the objective function's maximum and minimum values. The objective is to find the best answers in a fair amount of time with the least amount of computational complexity.