Lung cancer classification using fuzzy c-means and fuzzy kernel C-Means based on CT scan image

Abstract

<span id="docs-internal-guid-94842888-7fff-2ae1-cd5c-026943b95b7f"><span>Cancer is one of the diseases with the highest mortality rate in the world. Cancer is a disease when abnormal cells grow out of control that can attack the body's organs side by side or spread to other organs. Lung cancer is a condition when malignant cells form in the lungs. To diagnose lung cancer can be done by taking x-ray images, CT scans, and lung tissue biopsy. In this modern era, technology is expected to help research in the field of health. Therefore, in this study feature extraction from CT images was used as data to classify lung cancer. We used CT scan image data from SPIE-AAPM Lung CT challenge 2015. Fuzzy C-Means and fuzzy kernel C-Means were used to classify the lung nodule from the patient into benign or malignant. Fuzzy C-Means is a soft clustering method that uses Euclidean distance to calculate the cluster center and membership matrix. Whereas fuzzy kernel C-Means uses kernel distance to calculate it. In addition, the support vector machine was used in another study to obtain 72% average AUC. Simulations were performed using different k-folds. The score showed fuzzy kernel C-Means had the highest accuracy of 74%, while fuzzy C-Means obtained 73% accuracy. </span></span>

The combination of color-texture features and machine learning for detecting Dayak beads

“…Covering 0 • , 45 • , 90 • , and 135 • , the number of texture features produced consists of 16 features. The GLCM was made to quantify the heterogeneity of surface patterns and roughness displayed on digital images [26]. The explanation of each type of GLCM and the equations used are as follows [27]:…”

Brainstorm on artificial intelligence applications and evaluation of their commercial impact

“…The classification of diseases is a classic task [30], [31]: for example, the LYNA algorithm, analysing medical images, can help to find tissue problems. Surgical training simulation is another rich medical field.…”

Automated 3D convolutional neural network architecture design using genetic algorithm for pulmonary nodule classification

“…Computed tomography (CT) screening has proven effective in early detection of lung nodules, offering a potential solution to mitigate this situation and decreases lung cancer mortality rates [7]. However, the manual nodule detection process is laborious and time-consuming for radiologists since it requires a long time owing to the fact that they review sheer volume of scans in a day, which may affect their capacity to accurately identifying and classifying tumors [2], [8]. Even expert radiologists sometimes faces difficulty detecting and diagnosing lung nodules in CT scans [9].…”

“…Approaches used throughout the improvement and segmentation rounds are sometimes manually and carefully tweaked in order to prepare the region of interest (RoI) for feature extraction [7]. Rustam et al [8] used feature extraction techniques including gray level co-occurrence matrices (GLCM), lung nodule size, and local binary pattern (LBP) to extract features from the SPIE-AAPM-NCI Lungx Challenge in 2015. Fuzzy kernel C-means and fuzzy C-means were employed for classifying 2D lung nodules images as benign or malignant.…”