# Optimal Thresholding for Multi-Window Computed Tomography (CT) to Predict Lung Cancer
###### Abstract
Machine learning techniques are widely used in medical diagnosis and the monitoring of patients [12; 13; 14; 15; 16; 17]. This method aids in the recognition of patterns and the computation of visual attributes that are relevant to the diagnosis process. ML-based nodule detectors generally extract important features from CT images and use some classification techniques to separate the nodules. For example, the model presented in [18] segments the nodule area and extracts 15 texture features from both the segmented nodule and its surrounding region; it then trains a model using support vector machines (SVM) to detect nodules. In the method introduced in [19], gray-scale CT images were preprocessed using median filters to eliminate noise. To separate the image into several parts and retrieve useful information during segmentation, watershed segmentation was performed. The method extracts image characteristics such as the area, perimeter, diameter, etc., from the segmented regions. To determine whether the identified nodule was cancerous or benign, the authors ultimately employed an SVM classifier. [PERSON] et al. [20] proposed a CAD system that separates the lungs using a histogram-based method and looks for potential nodules by computing different statistical and shape-based features and classifying them with SVM. Deep learning (DL) tries to mimic human behavior to gain knowledge [21]. Due to its high performance and versatility, it helps to process a large amount of medical information and shows appreciable performance in tasks such as segmentation, classification, and detection [22]. Several lung cancer detection methods in the literature exploit deep learning. For example, in the method presented in [23], the authors used U-Net [24] and ResNet [25] models to extract the features of CT images and then used different classifiers to classify cancerous CT scans. [PERSON] et al. [26] proposed a deep learning method by combining CNN and the fuzzy particle swarm optimization algorithm. The main advantage of this algorithm is that it lessens the computational intricacy of the CNN algorithm and gives better performance. The method introduced in [27] computes a set of features, such as geometric features, intensity features, texture descriptors, gradient features, and region descriptors, and classifies them using deep learning. [PERSON] et al. [28] deployed a cloud-based 3D deep convolutional neural network (3 DDCNN) to aid radiologists. The DL-based lung nodule detection presented in [29] employed histogram equalization to enhance the image quality and applied an improved profuse clustering technique to separate the region of interest. A CAD system [30] that uses CT images as input and identifies regions of interest first, followed by nearby regions, to evaluate CT scans for positive or negative lung cancer is presented. To diagnose cancer at an early stage, it employs 2D and 3D convolutional neural networks, namely Google-net-based models, as classifiers. To identify and categorize lung cancer in CT scans, [PERSON] et al. [31] proposed a CAD that identifies a small nodule (less than 10 mm) in a 3D CT scan image. For better results, each image was converted into Hounsfield units during the preprocessing step, before being segmented using the Watershed approach. Following segmentation, linear scaling is used to normalize the 3D images, which are provided to the U-Net-based malignancy classifier to locate tiny boxes of the best candidates for nodules. Finally, an image composed of these small 2D slices of the best nodule candidates is obtained, and the layers are classified using the 3D-CNN classifier. The 2017 Kaggle Data Science Bowl was considered by [PERSON] et al. [32]. They used a modified ResNet for both nodule detection and malignancy detection after preprocessing the CT scans. Most existing thresholding-based lung nodule detection techniques handle CT scan images consisting of lung windows only and do not consider the mediastinal windows in the detection of the disease.