A fast automated lung segmentation method for the diagnosis of lung cancer

Huidrom, Ratishchandra; Chanu, Yambem Jina; Singh, Kh. Manglem

doi:10.1109/tencon.2017.8228094

Cited by 10 publications

(4 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A new method of lung segmentation has been developed, and it is far quicker than any of the other methods now in use without sacrificing the precision of the segmentation [3]. It is possible to determine the correlations between patient features and tumor response in advanced NSCLC by using a technique called frequent pattern mining.…”

Section: Related Workmentioning

confidence: 99%

Pattern Knowledge Discovery Based Lung Cancer Classification System

Mangayarkarasi¹,

Chithrakkannan²,

Karthikeyan³

2023

View full text Add to dashboard Cite

An accurate detection of abnormal lung nodule detection is very important for effective treatment and surgical procedure to remove the nodules. This paper introduces an efficient deep learning model to classify lung cancer in both left and right lung. It consists of three important stages; preprocessing, lung region detection and abnormal lung nodule detection. Further, a detailed discussion about the performance of the system is given using two benchmark databases; 30 lung CT images taken from the ELCAP dataset and 130 lung CT images taken from the LIDC dataset. An algorithmic framework is first created for the purpose of segmenting left and right lung region by a morphological algorithm after removing the noise by a wiener filter. A well defined deep learning architecture is designed for effective classification or detection of abnormal lung nodule detection by semantic classification. The proposed system is validated on LIDC and ELCAP database and provides an average accuracy of 97.86%.

show abstract

Section: Related Workmentioning

confidence: 99%

Pattern Knowledge Discovery Based Lung Cancer Classification System

Mangayarkarasi¹,

Chithrakkannan²,

Karthikeyan³

2023

View full text Add to dashboard Cite

show abstract

“…It can be a picture of any person, an outdoor scene, an electronic element microphotograph, or any medical image. The main advantage of using computed tomography images in this work is because it's simple and provides less distortion and low noise when compared with X-ray and images [11]. Hence, they are considered in this work.…”

Section: Image Capturementioning

confidence: 99%

“…Segmentation is used primarily to separate a digital image into several sets of pixels and to locate the boundaries of features. The key purpose of segmentation is to turn the representation of an image into a clear and functional segment that is easy to manage and interpret the image data, resulting in a series of regions that entirely cover the main image [11]. Two steps of segmentation were utilized as follows:…”

Section: Segmentationmentioning

confidence: 99%

Diagnosis of Lung Cancer Disease Based on Back-Propagation Artificial Neural Network Algorithm

Akkar¹,

Haddad²

2020

ETJ

View full text Add to dashboard Cite

Early stage detection of lung cancer is important for successful controlling of the diseases, also to offer additional chance to the patients in order to survive. So , algorithms that are related with computer vision and Image processing are extremely important for early medical diagnosis of lung cancer. In current work () computed tomography scan images were collected from several patients Classification was done using Back Propagation Artificial Neural Network ( ).It is considered as a powerful artificially intelligent technique with training rule for optimization to update the weights of the overall connections in order to determine the abnormal image. Several pre-processing operations and morphologic techniques were introduced to improve the condition of the image and make it suitable for detection cancer.Histogram and () Gray Level Co-occurrence Matrix were applied toget best features extraction analysis from lung image.Three types of activation functions(trainlm ,trainbr ,traingd) were used which gives a significant accuracy for detecting cancer in scan lung image related to the suggested algorithm. Best results were obtained with accuracy rate 95.9 % in trainlm activation function.. Graphic User Interface ( ) was displaying to show the final diagnosis for lung.

show abstract

“…(Kasturi et al, 2017) proposed an edge detection technique to segment lung cancer in 2D and 3D lung scans. (Huidrom et al, 2017) proposed an approach of thresholding segmentation of lung cancer.…”

Section: Introductionmentioning

confidence: 99%

Automatic Segmentation of Lung Cancer Using Fuzzy K-Means Algorithm

Umamaheswari

Bakiyalakshmi

Joshi

et al. 2020

ijrps

View full text Add to dashboard Cite

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.

show abstract

A fast automated lung segmentation method for the diagnosis of lung cancer

Cited by 10 publications

References 15 publications

Pattern Knowledge Discovery Based Lung Cancer Classification System

Pattern Knowledge Discovery Based Lung Cancer Classification System

Diagnosis of Lung Cancer Disease Based on Back-Propagation Artificial Neural Network Algorithm

Automatic Segmentation of Lung Cancer Using Fuzzy K-Means Algorithm

Contact Info

Product

Resources

About