Classification of lung image and nodule detection using fuzzy inference system

Roy, Tanushree Sinha; Sirohi, Neeraj; Patle, Arti

doi:10.1109/ccaa.2015.7148560

Cited by 40 publications

(14 citation statements)

References 8 publications

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“…This model performs classification using Neural network and the features such as entropy, correlation, integrity, BSNR, and SSIM are extracted using the gray-state co-occurrence matrix (GLCM) to achieve 90.7% accuracy [6]. The authors [7] developed a method for diagnosing lung cancer using an ambiguous interference system using gray conversion and binarization and an active margin model for decomposing the result. Classification of cancer cells is performed using the fuzzy inference system (FIS) which utilizes the extracted features for its training.…”

Section: Literature Reviewmentioning

confidence: 99%

A Novel Diagnostic Model For Lung Cancer Detection using Mumford-Shah and SVM Classifier

Sebastian*,

Devi

2020

IJITEE

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Lung cancer is one of the very deadly diseases in the world. However, diagnosing it at an early stage and treating it properly can protect lives. Although Computer Tomography (CT) scan imaging is one of the fruitful imaging in the field of medicine, it is the hardest for clinicians to clarify and recognize cancer from those images. And it is carried out with Mumford and Shah functional model, and support vector machine (SVM) classifier. Also, the system takes less computation time and thus, is highly efficient than existing algorithms which grab 98% accuracy. Further, the performance analysis of the proposed system is executed using seven assessment metrics namely Classification Accuracy, sensitivity, specificity, AUC, F measure, Precision, Brier Score, MCC. Finally, the results of SVM are compared with KNN, Decision-Tree, and Adaptive Boosting algorithms in terms of the seven metrics. The results show that there is significant progress in the above measures than the existing method.

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Section: Literature Reviewmentioning

confidence: 99%

A Novel Diagnostic Model For Lung Cancer Detection using Mumford-Shah and SVM Classifier

Sebastian*,

Devi

2020

IJITEE

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“…Early research work has also focused on using fuzzy logic for the purpose of classification of the lung nodules. Roy et al [25] have used contour-based model for accomplishing segmentation. Hence, the study was more inclined towards detection and less toward classification.…”

Section: Related Workmentioning

confidence: 99%

Comprehensive Classification Model for Diagnosing Multiple Disease Condition from Chest X-Ray

Savitha¹

2018

ijacsa

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Classification plays a significant role in the diagnosis of any form of radiological images in the healthcare sector. After reviewing existing classification approaches carried out over chest radiographs, it was explored that existing techniques are highly restricted to perform binary classification that is not comprehensive for assisting in an effective diagnosis process of chest disease condition. This paper presents a novel approach to classifying chest x-rays on the basis of the practical disease condition. Harnessing the potential features of contentbased image retrieval, the proposed system introduces a novel concept of attribute map that not only performs comprehensive classification but also makes the complete computational model extremely lightweight. The study outcome proved to offer better accuracy with the proposed non-iterative process in contrast to existing classifier design.

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“…The classification is made by artificial neural network. Fuzzy inference system based lung nodule classification and detection is described in [12]. After image enhancement, segmentation is made by active contour model and spatial features are extracted.…”

Section: Introductionmentioning

confidence: 99%

Image Fusion Based Lung Nodule Detection Using Structural Similarity and Max Rule

R¹,

Venkatesan²

2019

International Journal of Advances in Signal and Image Sciences

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The uncontrollable cells in the lungs are the main cause of lung cancer that reduces the ability to breathe. In this study, fusion of Computed Tomography (CT) lung image and Positron Emission Tomography (PET) lung image using their structural similarity is presented. The fused image has more information compared to individual CT and PET lung images which helps radiologists to make decision quickly. Initially, the CT and PET images are divided into blocks of predefined size in an overlapping manner. The structural similarity between each block of CT and PET are computed for fusion. Image fusion is performed using a combination of structural similarity and MAX rule. If the structural similarity between CT and PET block is greater than a particular threshold, the MAX rule is applied; otherwise the pixel intensities in CT image are used. A simple thresholding approach is employed to detect the lung nodule from the fused image. The qualitative analyses show that the fusion approach provides more information with accurate detection of lung nodules.

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Classification of lung image and nodule detection using fuzzy inference system

Cited by 40 publications

References 8 publications

A Novel Diagnostic Model For Lung Cancer Detection using Mumford-Shah and SVM Classifier

A Novel Diagnostic Model For Lung Cancer Detection using Mumford-Shah and SVM Classifier

Comprehensive Classification Model for Diagnosing Multiple Disease Condition from Chest X-Ray

Image Fusion Based Lung Nodule Detection Using Structural Similarity and Max Rule

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