Robust and Automated Lung Nodule Diagnosis from CT Images Based on Fuzzy Systems

Kumar, S. Aravind; Ramesh, J.; Vanathi, P. T.; Gunavathi, K.

doi:10.1109/pacc.2011.5979050

Cited by 15 publications

(9 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…References 82-86 and 95-98 were included in the table based on relevance, and the results of some other studies [100][101][102] were omitted due to the absence of relevant information. Collectively, the studies included in Table 4 indicate that the major challenge for lung nodule detection systems is robustness to diverse clinical data of varying quality.…”

Section: Discussionmentioning

confidence: 99%

Computer-aided detection of lung nodules: a review

2019

View full text Add to dashboard Cite

We present an in-depth review and analysis of salient methods for computer-aided detection of lung nodules. We evaluate the current methods for detecting lung nodules using literature searches with selection criteria based on validation dataset types, nodule sizes, numbers of cases, types of nodules, extracted features in traditional feature-based classifiers, sensitivity, and false positives (FP)/scans. Our review shows that current detection systems are often optimized for particular datasets and can detect only one or two types of nodules. We conclude that, in addition to achieving high sensitivity and reduced FP/scans, strategies for detecting lung nodules must detect a variety of nodules with high precision to improve the performances of the radiologists. To the best of our knowledge, ours is the first review of the effectiveness of feature extraction using traditional feature-based classifiers. Moreover, we discuss deep-learning methods in detail and conclude that features must be appropriately selected to improve the overall accuracy of the system. We present an analysis of current schemes and highlight constraints and future research areas.

show abstract

Section: Discussionmentioning

confidence: 99%

Computer-aided detection of lung nodules: a review

2019

View full text Add to dashboard Cite

show abstract

“…Fuzzy Clustering has been used in many fields like pattern recognition and Fuzzy identification. A variety of Fuzzy clustering methods have been proposed and most of them are based upon distance criteria [14]. The most widely used algorithm is the Fuzzy C-Mean algorithm (FCM), it uses reciprocal distance to compute fuzzy weights.…”

Section: Fuzzy Clusteringmentioning

confidence: 99%

Lung Cancer Detection by Using Artificial Neural Network and Fuzzy Clustering Methods

Taher¹,

Werghi²,

Al-Ahmad³

et al. 2012

AJBE

View full text Add to dashboard Cite

The early detection of lung cancer is a challenging problem, due to the structure of the cancer cells, where most of the cells are overlapped with each other. This paper presents two segmentation methods, Hopfield Neural Network (HNN) and a Fuzzy C-Mean (FCM) clustering algorithm, for segmenting sputum color images to detect the lung cancer in its early stages. The manual analysis of the sputum samples is time consuming, inaccurate and requires intensive trained person to avoid diagnostic errors. The segmentation results will be used as a base for a Computer Aided Diagnosis (CAD) system for early detection of lung cancer which will improve the chances of survival for the patient. However, the extreme variation in the gray level and the relative contrast among the images make the segmentation results less accurate, thus we applied a thresholding technique as a pre-processing step in all images to extract the nuclei and cytoplasm regions, because most of the quantitative procedures are based on the nuclear feature. The thresholding algorithm succeeded in extracting the nuclei and cytoplasm regions. Moreover, it succeeded in determining the best range of thresholding values. The HNN and FCM methods are designed to classify the image of N pixels among M classes. In this study, we used 1000 sputum color images to test both methods, and HNN has shown a better classification result than FCM, the HNN succeeded in extracting the nuclei and cytoplasm regions.

show abstract

“…Others studies [8,10,11,23,24,28,28,30,31,44], have been conducted concerning the diagnosis of lung nodules as malignant and benign, with the goal of increasing accuracy rates of CADx systems for detection and diagnosis of lung cancer. Tables 4 and 5 present a recent literature review in the area of diagnosis of lung nodules, showing the following details: work, techniques, database, and results.…”

Section: Introductionmentioning

confidence: 99%

Computer-Aided Diagnosis of Lung Nodules in Computed Tomography by Using Phylogenetic Diversity, Genetic Algorithm, and SVM

et al. 2017

View full text Add to dashboard Cite

Lung cancer is pointed as the major cause of death among patients with cancer throughout the world. This work is intended to develop a methodology for diagnosis of lung nodules using images from the Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI). The proposed methodology uses image processing and pattern recognition techniques. In order to differentiate between the patterns of malignant and benign nodules, we used phylogenetic diversity by means of particular indexes, that are: intensive quadratic entropy, extensive quadratic entropy, average taxonomic distinctness, total taxonomic distinctness, and pure diversity indexes. After that, we applied the genetic algorithm for selection of the best model. In the tests' stage, we applied the proposed methodology to 1405 (394 malignant and 1011 benign) nodules. The proposed work presents promising results at the classification into malignant and benign, achieving accuracy of 92.52%, sensitivity of 93.1% and specificity of 92.26%. The results demonstrated a good rate of correct detections using texture features. Since a precocious detection allows a faster therapeutic intervention, thus a more favorable prognostic to the patient, we propose herein a methodology that contributes to the area in this aspect.

show abstract

Robust and Automated Lung Nodule Diagnosis from CT Images Based on Fuzzy Systems

Cited by 15 publications

References 13 publications

Computer-aided detection of lung nodules: a review

Computer-aided detection of lung nodules: a review

Lung Cancer Detection by Using Artificial Neural Network and Fuzzy Clustering Methods

Computer-Aided Diagnosis of Lung Nodules in Computed Tomography by Using Phylogenetic Diversity, Genetic Algorithm, and SVM

Contact Info

Product

Resources

About