Hybrid Approach for Feature Extraction of Lung Cancer Detection

Patel, Twinkal; Nayak, V. Narasimha

doi:10.1109/icicct.2018.8473303

Cited by 12 publications

(4 citation statements)

References 9 publications

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“…For the sake of research trials, the JSRT and clinical dataset was used. This procedure will result in a more universal process that can be used to any sort of dataset, and this method has the potential to provide better outcomes than the one that was used before [20].…”

Section: Reviews On Lung Image Classification Systemsmentioning

confidence: 99%

CT Lung Image Classification Systems - A Systematic Review

Prabhakar¹,

Sivasubramanian²,

Sundaram³

et al. 2023

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Computer Aided Detection (CAD) and diagnostic technologies and their application have emerged in the last decade. CAD applications have concentrated on identification of the region of interest, for example nodules in the lung in the images, and analysis of the characteristics of objects. CAD technologies take advantages of computer systems as well as image processing and analysis techniques to overcome perceptual and interpretive errors caused by radiologists in the process of image observation and interpretation. This fact is inevitable in the context of medical practice since experience of image readers may vary and each observer (such as a radiologist) has a unique visual search on an image. Furthermore, there are always inter and intra observer variability for interpretation of an image. To overcome these difficulties, different CAD systems are developed for medical image analysis. This paper presents a systematic review on different CAD systems for lung cancer classification using computed tomography images.

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Section: Reviews On Lung Image Classification Systemsmentioning

confidence: 99%

CT Lung Image Classification Systems - A Systematic Review

Prabhakar¹,

Sivasubramanian²,

Sundaram³

et al. 2023

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“…The aim of feature extraction is spatial property reduction [20] options that found square measure nodule size, shape, energy, entropy, contrast native Energy-based form bar graph (LESH), a feature extraction technique was recently meant for carcinoma diagnosing. Feature choice is predicated on the applied math options by applying consecutive forward algorithmic program.…”

Section: Feature Extractionmentioning

confidence: 99%

A Survey on Convolutional Neural Network (Deep-Learning Technique) -Based Lung Cancer Detection

Gumma

Thiruvengatanadhan

Kurakula³

et al. 2021

SN COMPUT. SCI.

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“…The algorithm identifies whether the input image contains tumor cell or not and anticipates whether there is any likelihood of growth. Patel & Nayak (2018) [11] have propounded an algorithm that is better than the existing algorithms as the efficiency of decision making process improves. In addition, the features extracted are evaluated for classification.…”

Section: Related Workmentioning

confidence: 99%

A Deep Convolutional Neural Network Based Lung Disorder Diagnosis

Mercina¹,

Madhumathi²,

Priyanga³

et al. 2019

IJSRCSEIT

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Lungs play an important role in human respiratory system. There are diseases that affect the functioning of lungs. To analyse lung diseases in the chest region using X-ray based Computer-Aided Diagnosis (CAD) system, it is necessary to determine the lung regions subject to analysis. In this paper, an intelligent system is proposed for lung disease detection. In this paper, Interstitial Lung Disease (ILD) patterns are classified using Convolutional Neural Networks (CNN). The proposed system involves five convolutional layers and three dense layers. The performance of the classification demonstrates the potential of CNN in analysing lung patterns.

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Hybrid Approach for Feature Extraction of Lung Cancer Detection

Cited by 12 publications

References 9 publications

CT Lung Image Classification Systems - A Systematic Review

CT Lung Image Classification Systems - A Systematic Review

A Survey on Convolutional Neural Network (Deep-Learning Technique) -Based Lung Cancer Detection

A Deep Convolutional Neural Network Based Lung Disorder Diagnosis

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