scholarly journals Lung Nodule Detection of CT and Image-Based GLCM and RLM CT Scan Using the Support Vector Machine (SVM) Method

2021 ◽  
Vol 5 (2) ◽  
Author(s):  
Zaimah Permatasari ◽  
Mauridhi Hery Purnomo ◽  
I Ketut Eddy Purnama
Keyword(s):  
Lung Cancer ◽  
Support Vector Machine ◽  
Lung Nodule ◽  
Region Of Interest ◽  
Medical Personnel ◽  
Ct Images ◽  
Support Vector ◽  
Nodule Detection ◽  
The Status ◽  
Keywords Cancer

Lung cancer is the most common cause of cancer death globally. Early detection of lung cancer will greatly beneficial to save the patient. This study focused on the detection of lung cancer using classification with the Support Vector Machine (SVM) method based on the features of Gray Level Co-occurrence Matrices (GLCM) and Run Length Matrix (RLM). The lung data used were obtained from the Cancer imaging archive Database, consisting of 500 CT images. CT images were grouped into 2 clusters, including normal and lung cancer. The research steps include: image processing, region of interest segmentation, and feature extraction. The results indicate that the system can detect the CT-image of SVM classification where the default parameter only provides an accuracy of 85.63%. It is expected that the results will be useful to help medical personnel and researchers to detect the status of lung cancer. These results provide information that detection of lung nodules based on GLCM and RLM features that can be detected is better. Furthermore, selecting parameters C and γ on SVM. Keywords: cancer, nodule, support vector machine (SVM).

scholarly journals Detection of Lung Cancer Lesions Using 3D Convolutional Neural Networks and Segmentation for Accurate Detection

2021 ◽  
pp. 435-439
Author(s):  
Rajani Kumari ◽  
C. Thanuja ◽  
K. Sai Thanvi ◽  
K. Lakshmi ◽  
U. Lavanya
Keyword(s):  
Lung Cancer ◽  
Neural Networks ◽  
Lung Nodule ◽  
Region Of Interest ◽  
Texture Features ◽  
Support Vector ◽  
Lung Nodules ◽  
Shape Features ◽  
Rule Based ◽  
Occurrence Matrix

Lung cancer is a leading cause of death worldwide; it refers to the uncontrolled growth of abnormal cells in the lung. A computed tomography (CT) scan of the thorax is the most sensitive method for detecting cancerous lung nodules. A lung nodule is a round lesion which can be either non-cancerous or cancerous. In the CT, the lung cancer is observed as round white shadow nodules. In existing method, the candidate ROIs shape features are calculated, and some blood vessels are get rid of using rule-based according to shape features; secondly, the remainder candidates gray and texture features are calculated; finally, the shape, gray and texture features are taken as the inputs of the SVM (Support Vector Machine) classifier to classify the candidates. Experimental results show that the rule-based approach has no omission, but the misclassification probability is too large; Hence, in the proposed method the nodules were characterized by the computation of the texture features obtained from the gray level co-occurrence matrix (GLCM) in the wavelet domain and were classified using a SVM with radial basis function in order to classify CT images into two categories: with cancerous lung nodules and without lung nodules. The stages of the proposed methodology to design the CADx system are: 1) Extraction of the region of interest, 2) Wavelet transform, 3) Feature extraction, 4) Attribute and sub-band selection and 5) Classification. The same classification is implemented for the convolution neural networks. The final comparison is done between these two networks based on the accuracy.

Lung Nodule Classification on CT Images Using Deep Convolutional Neural Network Based on Geometric Feature Extraction

2020 ◽  
Vol 10 (9) ◽  
pp. 2042-2052
Author(s):  
Nikitha Johnsirani Venkatesan ◽  
ChoonSung Nam ◽  
Dong Ryeol Shin
Keyword(s):  
Lung Cancer ◽  
Cancer Detection ◽  
Lung Nodule ◽  
Region Of Interest ◽  
Histogram Equalization ◽  
Ct Images ◽  
Geometric Feature ◽  
Test Accuracy ◽  
Non Gaussian ◽  
Lung Cancer Detection

Lung cancer detection in the earlier stage is essential to improve the survival rate of the cancer patient. Computed Tomography [CT] is a first and preferred modality of imaging for detecting cancer with an enhanced rate of diagnosis accuracy owing to its function as a single scan process. Visual inspections of the CT images are prone to error, as it is more complex to distinguish lung nodules from the background tissues which are subjective to intra and interobserver variability. Hence, computer-aided diagnosis is essential to support radiologists for accurate lung nodule prediction. To overcome this issue, we propose a deep learning approach for automatic lung cancer detection from a low dose CT images. We also propose image pre-processing using Efficient Adaptive Histogram Equalization based Region of Interest [EAHE-ROI] to enhance the CT scan and to eliminate artefacts which occur due to noise and variations of the image. The ROI is extracted from CT scans using morphological operators, thus reducing the number of false positives. We chose geometric features as they extract more geometric elements like curves, lines and points of cancer nodules. Our Non-Gaussian Convolutional Neural Networks [NG-CNN] architecture contains feature extractor and classifier, which has been applied on training, validation and test dataset. Our proposed methodology offers better-classified outcome and effectual cancer detection by outperforming the other competing methods and gives a test accuracy of 94.97% and AUC 0.896.

scholarly journals Lung Nodule Detection in CT Images Using Statistical and Shape-Based Features

2020 ◽  
Vol 6 (2) ◽  
pp. 6
Author(s):  
Noor Khehrah ◽  
Muhammad Shahid Farid ◽  
Saira Bilal ◽  
Muhammad Hassan Khan
Keyword(s):  
Lung Tumor ◽  
Lung Nodule ◽  
Ct Images ◽  
Occurrence Rate ◽  
Support Vector ◽  
High Death Rate ◽  
Internal Structures ◽  
Nodule Detection ◽  
High Death ◽  
Sensitivity Rate

The lung tumor is among the most detrimental kinds of malignancy. It has a high occurrence rate and a high death rate, as it is frequently diagnosed at the later stages. Computed Tomography (CT) scans are broadly used to distinguish the disease; computer aided systems are being created to analyze the ailment at prior stages productively. In this paper, we present a fully automatic framework for nodule detection from CT images of lungs. A histogram of the grayscale CT image is computed to automatically isolate the lung locale from the foundation. The results are refined using morphological operators. The internal structures are then extracted from the parenchyma. A threshold-based technique is proposed to separate the candidate nodules from other structures, e.g., bronchioles and blood vessels. Different statistical and shape-based features are extracted for these nodule candidates to form nodule feature vectors which are classified using support vector machines. The proposed method is evaluated on a large lungs CT dataset collected from the Lung Image Database Consortium (LIDC). The proposed method achieved excellent results compared to similar existing methods; it achieves a sensitivity rate of 93.75%, which demonstrates its effectiveness.

scholarly journals Design of CAD System of Solitary Pulmonary Nodule with Harmony Classification and Fuzzy System

2017 ◽  
Vol 1 (4) ◽  
pp. 102-102
Author(s):  
Maryam Sadat Mahmoodi ◽  
Seyed Abbas Mahmoodi
Keyword(s):  
Lung Cancer ◽  
Fuzzy System ◽  
Search Algorithm ◽  
Harmony Search ◽  
Lung Nodule ◽  
Region Of Interest ◽  
Harmony Search Algorithm ◽  
Cad System ◽  
Nodule Detection ◽  
Base Method

Introduction: Lung cancer is the most wide spread from of cancer, with the highest mortality rate worldwide. In this study, a computer-aided detection (CAD) system was developed for lung nodule detection, segmentation and recognition using CT images. So, we use a highly accurate supervised that uses lung images with the aim of assisting physicians in early detection of lung cancer. Methods: First, we segmented the lung area by masking techniques to isolated nodules and determined region of interest. Then, 24 features were extracted from images that included morphological, statistical and histogram. Important features  were derived from the images for their posterior analysis with the aid of a harmony search algorithm and fuzzy systems. Results: In order to evaluate the performance of the proposed method, we used the LIDC database. the number of images included a database of  97 images whom 47 were diagnosed with lung cancer. Results of the base method show a sensitivity of 93%. Conclusion: The harmony search algorithm is optimized using fuzzy system for classification. The CAD system provides 93.1%  accuracy.

Lung Cancer Detection using Image Processing Techniques and its Classification

2021 ◽  
Vol 9 (9) ◽  
pp. 1578-1583
Author(s):  
Aishwarya .R
Keyword(s):  
Lung Cancer ◽  
Image Processing ◽  
Support Vector Machine ◽  
Cell Carcinoma ◽  
Mortality Rates ◽  
The Body ◽  
Support Vector ◽  
Lung Tumour ◽  
Image Processing Techniques ◽  
Processing Techniques

Abstract: Lung cancer has been a major contribution to mortality rates world-wide for many years now. There is a need for early diagnosis of lung cancer which if implemented, will help in reducing mortality rates. Recently, image processing techniques have been widely applied in various medical facilities for accurate detection and diagnosis of abnormality in the body images like in various cancers such as brain tumour, breast tumour and lung tumour. This paper is a development of an algorithm based on medical image processing to segment the lung tumour in CT images due to the lack of such algorithms and approaches used to detect tumours. The work involves the application of different image processing tools in order to arrive at the desired result when combined and successively applied. The segmentation system comprises different steps along the process. First, Image preprocessing is done where some enhancement is done to enhance and reduce noise in images. In the next step, the different parts in the images are separated to be able to segment the tumour. In this phase threshold value was selected automatically. Then morphological operation (Area opening) is implemented on the thresholded image. Finally, the lung tumour is accurately segmented by subtracting the opened image from the thresholded image. Support Vector Machine (SVM) classifier is used to classify the lung tumour into 4 different types: Adenocarcinoma(AC), Large Cell Carcinoma(LCC) Squamous Cell Carcinoma(SCC), and No tumour (NT). Keywords: Lung tumour; image processing techniques; segmentation; thresholding; image enhancement; Support Vector Machine; Machine learning;

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