An Effective Approach of CT Lung Segmentation Using Possibilistic Fuzzy C-Means Algorithm and Classification of Lung Cancer Cells with the Aid of Soft Computing Techniques

Paulraj, Tharcis; Chellliah, Kezi Selva Vijila

doi:10.2174/1573405612999160510174336

Cited by 3 publications

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On top of this, Dipanjan et al utilized a one-dimensional CNN to precisely detect NSCLC tumors [16]. Up to now, most pulmonary cancer classification algorithms are based on gray images, color images and gene expression [5,17,18].…”

Section: Introductionmentioning

confidence: 99%

3D‐PulCNN: Pulmonary cancer classification from hyperspectral images using convolution combination unit based CNN

Zhang

Wang

Song

et al. 2021

Journal of Biophotonics

View full text Add to dashboard Cite

Pulmonary cancer is one of the most common malignancies worldwide. Accurate classification of its subtypes is required in differential diagnosis. However, existing algorithms are mostly based on color images, and the improvement of accuracy is quite challenging. In this study, we propose a convolution combination unit (CCU)-based three-dimensional convolutional neural network (3D-PulCNN) for classifying pulmonary cancer presented in microscopic hyperspectral image with both spatial and spectral information. CCU is designed to fuse the features acquired by different convolution scales. Compared with VGGNet, only two fully connected layers are used in this model, reducing the network parameters and model complexity. Experimental results show that 3D-PulCNN achieves overall average (OA) of 0.962 and Precision, Recall, and Kappa of more than 0.920, superior to 2D-VGGNet. Then, 3D-UNet is leveraged to segment cancer cells, and their morphological characteristics are calculated to supply quantitative virtual analysis data for classification results explanation and prognosis assessment.

show abstract

Section: Introductionmentioning

confidence: 99%

3D‐PulCNN: Pulmonary cancer classification from hyperspectral images using convolution combination unit based CNN

Zhang

Wang

Song

et al. 2021

Journal of Biophotonics

View full text Add to dashboard Cite

show abstract

Lung computed axial tomography image segmentation using possibilistic fuzzy C‐means approach for computer aided diagnosis system

Paulraj¹,

Chelliah²,

Chinnasamy³

2019

Int J Imaging Syst Tech

Self Cite

View full text Add to dashboard Cite

Soft computing is an associate rising field that plays a crucial half in the area of engineering and science. One of the most significant applications of soft computing is image segmentation. It focuses on an exploiting tolerance of imprecision and uncertainty. Segmentation supported soft computing remains a difficult task within the medical field. Medical images are habitually used in the segmentation process to extract the meaningful portions and to know and clarify the condition of the particular patient. In this article, we implement an efficient possibilistic fuzzy C‐means (PFCM) approach to segment the lung portion in the computed tomography (CT) image and the result shows that it improves the segmentation accuracy upto 98.5012% and results are compared with existing segmenting approaches like fuzzy possibilistic C‐means method, fuzzy bitplane method and so forth. Also, the PFCM approach increases the diagnostic accuracy of the computer aided diagnosis system using CT images. The radiologist may utilize this computer aided diagnosis system results as a second opinion of their diagnosed results.

show abstract

A novel lung extraction approach for LDCT images using discrete wavelet transform with adaptive thresholding and Fuzzy C-means clustering enhanced by genetic algorithm

2022

View full text Add to dashboard Cite

An Effective Approach of CT Lung Segmentation Using Possibilistic Fuzzy C-Means Algorithm and Classification of Lung Cancer Cells with the Aid of Soft Computing Techniques

Cited by 3 publications

References 0 publications

3D‐PulCNN: Pulmonary cancer classification from hyperspectral images using convolution combination unit based CNN

3D‐PulCNN: Pulmonary cancer classification from hyperspectral images using convolution combination unit based CNN

Lung computed axial tomography image segmentation using possibilistic fuzzy C‐means approach for computer aided diagnosis system

A novel lung extraction approach for LDCT images using discrete wavelet transform with adaptive thresholding and Fuzzy C-means clustering enhanced by genetic algorithm

Contact Info

Product

Resources

About