## 1 Introduction  
Every year, the American Cancer Society estimate the new cancer cases in the United States regarding the new number of cancer cases and deaths on population-based cancer. In the year 2021, about 1,898,160 new cancer cases were encountered, and 608,570 deaths were projected in the United States [1], and 85% will be non-small cell lung cancer (NSCLC) [2; 3], among others. NSCLC can be detected with radiofrequency (RF) excision and stereotactic body radiotherapy (SBRT) methods. Lung cancer has two main types, i.e., NSCLC and small cell lung carcinoma (SCLC). Both of these types of cancer spread in different ways and require different treatment accordingly. NSCLC spreads very slowly and is different than SCLC, which is related to smoking, and propagates very rapidly, forms tumors, and spreads in whole body. The deaths based on SCLC are proportional to the number of cigarettes smoked [4].  
This study is specifically designed to propose automated tools which improve lung cancer prediction. The tumor type and stage can further improve the early detection of NSCLC. Usually, due to the late detection of cancer cases, there is a 16% survival rate of NSCLC for five years. Additionally, chemotherapy is a standard therapy for SCLC, and the response for SCLC patients is 60%. Thus, the recurrence rate is a few months, resulting in an overall survival of 6% for SCLC. In the past few decades, the survival rates for these two types of cancer have been changed.  
The diagnosis of lung cancer is mainly based on findings from traditional chest radiography [5], bronchoscopy, computed tomography (CT) [6], magnetic resonance (MR) [7], positron emission tomography (PET), and biopsy. Due to late diagnosis, lung cancer prognosis remains poor, and advanced metastasis is usually present at the time of presentation. The 5-year persistence ratio for lung cancer confined to the lungs is approximately 54%, but only 4% for inoperable, advanced lung cancer. Treatment modalities depend on the extent and type of the cancer, and include surgery, radiotherapy, and chemotherapy. Computed tomography (CT) is a very sensitive tool for diagnosing, assessing the extent of tumor growth, and monitoring disease progression.  
The main objective of this study is to utilize different image enhancement methods to improve lung cancer prediction by improving the image quality, and to further investigate which enhancement method is more robust. To the best of our knowledge, these methods have not been utilized on this dataset. We utilized image enhancement methods, such as image adjustment, gamma correction, thresholding, contrast stretching for improving input image quality and noise removal, etc., and compared results without image enhancement methods. The image enhancement methods improved the prediction performance on the Lung Cancer Alliance dataset. After utilizing image enhancement methods, the texture features based on GLCM were computed. Previously, researchers utilized different image enhancement methods, such as histogram equalization (HE), to improve the image appearance. For low-contrast foreground and background images, HE increases contrast and decreases intensity [8]. The image adjustment method was used by [9] to improve the resolution in a 3-dimensional glasses viewing system. Moreover, contrast adjustment was utilized by [10] to enhance MRI images of visual attenuation. Similarly, different authors [11] utilized enhanced methods to remove gamma correction, haze, external noise, etc. Section 1 is the introduction, Section 2 explains the material and methods utilized in our study, Section 3 describes the results and discussions, and Section 4 details the conclusion along with limitations and future recommendations.