Automatic Segmentation of Lung Cancer Using Fuzzy K-Means Algorithm

Abstract

Treating malignant growth in the beginning times gives greater treatment choices, less intrusive medical procedure and expands the endurance rate. Finding lung disease just as liver malignancy at a beginning period is a difficult assignment since there are hardly any manifestations right now larger part of the cases are analyzed in later stages. In this paper, an augmented method of segmentation is proposed using the Fuzzy K-means algorithm to observe the initial stage of lung cancer from the human chest X-Ray images. Initially, the investigator used strategies like Histogram Equalization for image improvement, Watershed technique for segmentation and Edge detection for Extraction, etc. Existing Approaches decline to provide certainty in a problem-solving time operation. The datasets are collected from LIDC-IDRI and Kaggle. In this, 100 training dataset input images are taken for training the model: 80 Cancerous images and 20 non-cancerous images. To evaluate the data 20 images are taken. The detection means classifying tumour into three classes (i) non-cancerous tumour, (ii) Less affected tumour (iii) Highly affected tumour. Hence, a new technique to detect lung carcinoma nodules using median filters, Fuzzy K-means clustering for segmentation and SVM (Support Vector Machine) as the classifier is planned on this work. Hence, this way is profitable to the medical instruments producing industries and additionally guide the radiologist for the early analysis of cancer.

Selection and Optimization of Regional Economic Industrial Structure Based on Fuzzy k-Means Clustering Algorithm

“…Among them, technology intensive industries such as information technology service industry and scientific research industry have developed slowly, which needs attention. In the tertiary industry of city A, some traditional service industries are developing steadily, but the development of technological service industries and emerging service industries is slow [18,19]. Before 2009, city A was in the early stage of resource development, with huge resource development potential and strong resource dependence.…”

Computer User Behavior Anomaly Detection Based on K-Means Algorithm

“…Kachaoui and Belangour believe that some experts have used sequential pattern mining to establish user behavior patterns for anomaly detection and have achieved certain results; however, in order to facilitate data processing, the user behavior patterns established by them are not objective enough (not in line with the contingency and multirepetition characteristics of user behavior), resulting in inaccurate detection results [11]. e pattern mining-based anomaly detection algorithm proposed by Umamaheswari et al applies sequential patterns to mine the behavior patterns of a single user and improves the rationality and accuracy of anomaly detection by improving the pattern comparison method; however, the obtained frequent sequence is the behavior that is connected before and after in the behavior sequence, which is inconsistent with the contingency of user behavior and does not take into account the multirepetitive characteristics of user behavior, which makes it have certain limitations in application [12]. behavior sequences.…”