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.