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

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.

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Segmentation of Lung Nodules in CT Scan Data

Medical Imaging ◽

10.4018/978-1-5225-0571-6.ch023 ◽

2017 ◽

pp. 601-613 ◽

Cited By ~ 1

Author(s):

Shehzad Khalid ◽

Anwar C. Shaukat ◽

Amina Jameel ◽

Imran Fareed

Keyword(s):

Lung Cancer ◽

Lung Nodule ◽

Clinical Importance ◽

Pulmonary Nodules ◽

Cad System ◽

Nodule Detection ◽

Scan Data ◽

Effective Domain ◽

Aided Diagnosis

Developing an effective computer-aided diagnosis (CAD) system for lung cancer is of great clinical importance and can increase the patient's chance of survival. For this reason, CAD systems for lung cancer have been investigated in a huge number of research studies. Several studies have shown the feasibility and robustness of automated matching of corresponding nodule pairs between follow up examinations. Different image pre-processing and segmentation techniques are used in various research sides to segment different tumors or ulcers from different images. This paper aims to make a review on the existing segmentation algorithms used for CT images of pulmonary nodules and presents a study of the existing methods on automated lung nodule detection. It provides a comparison of the performance of the existing approaches in regards to effective domain results.

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

JAREE (Journal on Advanced Research in Electrical Engineering) ◽

10.12962/jaree.v5i2.125 ◽

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).

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Computer-aided lung cancer screening with CT: A clinically usable nodule detection and assessment system.

Journal of Clinical Oncology ◽

10.1200/jco.2013.31.15_suppl.7562 ◽

2013 ◽

Vol 31 (15_suppl) ◽

pp. 7562-7562

Author(s):

Pechin Lo ◽

Matthew S. Brown ◽

Jonathan Goldin ◽

Eran Barnoy ◽

Hyun J. Kim ◽

...

Keyword(s):

Lung Cancer ◽

Cancer Screening ◽

False Positive ◽

Lung Nodule ◽

Lung Cancer Screening ◽

Assessment System ◽

Slice Thickness ◽

Cad System ◽

Nodule Detection ◽

Computer Aided

7562 Background: The National Lung Screening Trial (NLST) recently demonstrated that lung cancer screening with low-dose CT reduces mortality. Current protocols use 4–8 mm nodules as positive screens. While there are some computer-aided nodule detection (CAD) systems currently available, they are rarely used in clinical practice because they generate too many false positives and lack reliable measurement tools. The purpose of this work is to develop a new CAD system to overcome these limitations and evaluate it against an expert panel of radiologists. Methods: The CAD system developed for lung nodule detection and measurement incorporates computer vision techniques including intensity thresholding, Euclidean Distance Transformation, and watershed segmentation. Rules pertaining to volume and shape were applied to automatically discriminate between nodules and bronchovascular anatomy. CAD system performance was assessed using 108 consecutive cases from the publically available Lung Imaging Database Consortium (LIDC), in which four radiologists reviewed each case. CT slice thickness ranged from 0.6–3.0 mm. Nodules were included that were: (a) ≥ 4mm, and (b) marked by a majority of the LIDC readers, and (c) ≥ 4 x CT slice thickness (to ensure adequate spatial resolution). Results: 44 of 108 subjects had one or more nodules meeting criteria. Median CAD sensitivity per subject for these 44 cases is reported for all nodules ≥ 4mm and the subset of nodules ≥ 8mm. The false positive (FP) rate per subject is reported for all 108 cases. The overall concordance correlation coefficient (CCC) between the CAD volume of each nodule and the LIDC reference volume was measured. Conclusions: Based on clinical CT screening protocols, a CAD system has been developed with high nodule sensitivity and a much lower false positive rate than previously reported systems. Automated volume measurements show strong agreement with the reference standard, providing a comprehensive detection and assessment workflow for lung cancer screening. [Table: see text]

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Segmentation of Lung Nodules in CT Scan Data

International Journal of Privacy and Health Information Management ◽

10.4018/ijphim.2015070104 ◽

2015 ◽

Vol 3 (2) ◽

pp. 66-77 ◽

Cited By ~ 2

Author(s):

Shehzad Khalid ◽

Anwar C. Shaukat ◽

Amina Jameel ◽

Imran Fareed

Keyword(s):

Lung Cancer ◽

Lung Nodule ◽

Clinical Importance ◽

Pulmonary Nodules ◽

Cad System ◽

Nodule Detection ◽

Scan Data ◽

Effective Domain ◽

Aided Diagnosis

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Development of CAD System for Automatic Lung Nodule Detection: A Review

BIO Web of Conferences ◽

10.1051/bioconf/20214104001 ◽

2021 ◽

Vol 41 ◽

pp. 04001

Author(s):

Sekar Sari ◽

Indah Soesanti ◽

Noor Akhmad Setiawan

Keyword(s):

Lung Cancer ◽

Feature Extraction ◽

Cancer Detection ◽

False Positive ◽

Lung Nodule ◽

Cad Systems ◽

Cad System ◽

Lung Nodule Detection ◽

Nodule Detection ◽

Lung Cancer Detection

Lung cancer is a type of cancer that spreads rapidly and is the leading cause of mortality globally. The Computer-Aided Detection (CAD) system for automatic lung cancer detection has a significant influence on human survival. In this article, we report the summary of relevant literature on CAD systems for lung cancer detection. The CAD system includes preprocessing techniques, segmentation, lung nodule detection, and false-positive reduction with feature extraction. In evaluating some of the work on this topic, we used a search of selected literature, the dataset used for method validation, the number of cases, the image size, several techniques in nodule detection, feature extraction, sensitivity, and false-positive rates. The best performance CAD systems of our analysis results show the sensitivity value is high with low false positives and other parameters for lung nodule detection. Furthermore, it also uses a large dataset, so the further systems have improved accuracy and precision in detection. CNN is the best lung nodule detection method and need to develop, it is preferable because this method has witnessed various growth in recent years and has yielded impressive outcomes. We hope this article will help professional researchers and radiologists in developing CAD systems for lung cancer detection.

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Palm print recognition based on harmony search algorithm

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v11i5.pp4113-4124 ◽

2021 ◽

Vol 11 (5) ◽

pp. 4113

Author(s):

Raniah Ali Mustafa ◽

Haitham Salman Chyad ◽

Rafid Aedan Haleot

Keyword(s):

Hong Kong ◽

Gaussian Distribution ◽

Search Algorithm ◽

Harmony Search ◽

Region Of Interest ◽

Recognition System ◽

Harmony Search Algorithm ◽

Identification Methods ◽

Palm Print ◽

College Of Engineering

Due to its stabilized and distinctive properties, the palmprint is considered a physiological biometric. Recently, palm print recognition has become one of the foremost desired identification methods. This manuscript presents a new recognition palm print scheme based on a harmony search algorithm by computing the Gaussian distribution. The first step in this scheme is preprocessing, which comprises the segmentation, according to the characteristics of the geometric shape of palmprint, the region of interest (ROI) of palmprint was cut off. After the processing of the ROI image is taken as input related to the harmony search algorithm for extracting the features of the palmprint images through using many parameters for the harmony search algorithm, Finally, Gaussian distribution has been used for computing distance between features for region palm print images, in order to recognize the palm print images for persons by training and testing a set of images, The scheme which has been proposed using palmprint databases, was provided by College of Engineering Pune (COEP), the Hong Kong Polytechnic University (HKPU), Experimental results have shown the effectiveness of the suggested recognition system for palm print with regards to the rate of recognition that reached approximately 92.60%.

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