Classification of lung nodules and arteries in computed tomography scan image using principle component analysis

Widodo, Sri; Rohmah, Ratnasari Nur; Handaga, Bana

doi:10.1109/icitisee.2017.8285485

Cited by 10 publications

(16 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As a result, the prospecting range for lung nodules has been narrowed and the data have been normalized [10]. These include the weiner filter, the grayscale filter, the histogram equalization filter, the linear mapping filter, and the Gaussian lowpass filter [53], [54].…”

Section: Computer-aided Design For Lung Nodule Detectionmentioning

confidence: 99%

“…A broader range of possible dose-response interactions could be accommodated [62], [63]. The mean filter, the Gaussian filter, anisotropic diffusion, the median filter, the adaptive median filter, conservative smoothing, and the alpha trim mean filter are other image smoothing methods that can be used to improve low-level distortion in lung images [37], [53]- [55], [58], [64].…”

Section: Image Smoothingmentioning

confidence: 99%

See 1 more Smart Citation

A review of convolutional neural network-based computer-aided lung nodule detection system

Sari

Sutikno²,

Soesanti³

et al. 2023

IJ-AI

View full text Add to dashboard Cite

Worldwide, lung cancer is the leading cause of mortality and rapidly spreads. Lung tissue that is benign does not grow significantly, but lung tissue that is malignant grows rapidly and attacks the body, posing a grave threat to one's health. This paper provides a literature review of computer-aided detection (CAD) systems for lung cancer diagnosis. Preprocessing, segmentation, detection, and classification are the stages of the CAD system. This review divides the preprocessing into three stages: image smoothing, edge sharpening, and noise removal. Additionally, lung segmentation is divided into three stages: histogram-based thresholding, linked component analysis, and lung extraction. The detecting phase aids in decreasing the workload. Several techniques are briefly described, including random forest, support vector machine (SVM), naive bayes, K-nearest neighbor (KNN), and convolutional neural network (CNN). Classification is the final stage; the image is then identified as containing or not possessing nodules. The prospect of incorporating CNN-based deep learning techniques into the CAD system is discussed. This paper is superior to other review studies on this topic due to its comprehensive examination of pertinent literature and structured presentation. We anticipate that our study may aid professional researchers and radiologists in developing more effective CAD systems for detecting lung cancer.

show abstract

Section: Computer-aided Design For Lung Nodule Detectionmentioning

confidence: 99%

Section: Image Smoothingmentioning

confidence: 99%

A review of convolutional neural network-based computer-aided lung nodule detection system

Sari

Sutikno²,

Soesanti³

et al. 2023

IJ-AI

View full text Add to dashboard Cite

show abstract

“…The image used is a CT-Scan image that contains Covid and those that do not contain Covid which is obtained from the segmentation process. The first segmentation is lung field segmentation using Active Shape Model (ASM) (Widodo & Wijiyanto, 2014), second segmentation is segmentation of Covid-19 candidates using morphological mathematics (Widodo, Rohmah & Handaga, 2019;Widodo, 2017;. The results of morphological process are described in Figure 4.…”

Section: Detection Of Covid-19 Using a Deep Learning Convolution Neur...mentioning

confidence: 99%

Detection of Covid-19 on Localized Ct-scan Images Using Deep Learning Convolution Neural Network

Widodo,

Sulistiyanti,

Yudistira

2022

IJAEMR

Self Cite

View full text Add to dashboard Cite

Pneumonia Coronavirus Disease 2019 (COVID-19) is an inflammation of the lung parenchyma caused by Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Supporting examinations carried out to establish a diagnosis of Covid-19 is through radiological examinations, one of which is Computed Tomography Scan (CT-Scan). The current method used to diagnose COVID-19 from CT scan images is by studying the 2-D CT Scan image data set using naked eye, then interpreting the data one by one. This procedure is ineffective. Research aim is to develop Covid-19 detection application on localized CT-Scan images using Deep Learning Convolution Neural Network (CNN). Pre-Trained Model used is ResNet-50. Test was carried out 3 times. First test uses original CT image, with an accuracy of 92.5%. Second test used pulmonary CT data that had been separated from surrounding tissue, with an accuracy of 95%. The last test used localized CT data (Covid Candidate), the accuracy obtained was 98%.

show abstract

“…In this paper we do not discuss about Principle Componen Analysis (PCA). This is because PCA has been discussed in another paper [6]. Algorithms for detecting lung disease caused by smoking can be explained as follows: a.…”

Section: Detection Of Lung Diseases Using Svmmentioning

confidence: 99%

Untitled

2019

TELKOMNIKA

View full text Add to dashboard Cite

Type of lung disease is very much manifold, but type of lung disease caused by smoking there are only 4, namely Bronchitis, Pneumonia, Emphysema and Lung Cancer. Doctors usually diagnose lung disease from CT scans using the naked eye, then interpret data one by one.This procedure is not effective. The aim of this research is improvement accuracy of lung diseases detection caused by smoking using support vector machine on computed tomography scan (CT scan) images. This study includes 4 (four) main points. First is the development of software for segmentation of lung organ automatically using Active Shape Model (ASM) method. Second is the segmentation of candidates who are considered illness by using Morphology Mathematics. The third process of lung disease detection using Support Vector Machine (SVM). Fourth is visualization of disease or lung disorder using Volume Rendering.

show abstract

Classification of lung nodules and arteries in computed tomography scan image using principle component analysis

Cited by 10 publications

References 4 publications

A review of convolutional neural network-based computer-aided lung nodule detection system

A review of convolutional neural network-based computer-aided lung nodule detection system

Detection of Covid-19 on Localized Ct-scan Images Using Deep Learning Convolution Neural Network

Untitled

Contact Info

Product

Resources

About