Deep learning for lung Cancer detection and classification

Asuntha, A; Srinivasan, Andy

doi:10.1007/s11042-019-08394-3

Cited by 179 publications

(60 citation statements)

References 32 publications

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“…Support Vector Machine is a supervised learning algorithm that uses the Classification method to analyze data and predicate patterns. The texture is divided into two categories or classes by the SVM classifier: regular and abnormal pictures [44]. It is used to effectively map the nodule.…”

Section: Support Vector Machinementioning

confidence: 99%

Lung cancer Prediction and Classification based on Correlation Selection method Using Machine Learning Techniques

Abdullah

Abdulazeez

Sallow

2021

QAJ

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Lung cancer is one of the leading causes of mortality in every country, affecting both men and women. Lung cancer has a low prognosis, resulting in a high death rate. The computing sector is fully automating it, and the medical industry is also automating itself with the aid of image recognition and data analytics. This paper endeavors to inspect accuracy ratio of three classifiers which is Support Vector Machine (SVM), K-Nearest Neighbor (KNN)and, Convolutional Neural Network (CNN) that classify lung cancer in early stage so that many lives can be saving. Basically, the informational indexes utilized as a part of this examination are taken from UCI datasets for patients affected by lung cancer. The principle point of this paper is to the execution investigation of the classification algorithms accuracy by WEKA Tool. The experimental results show that SVM gives the best result with 95.56%, then CNN with CNN 92.11% and KNN with 88.40%.

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Section: Support Vector Machinementioning

confidence: 99%

Lung cancer Prediction and Classification based on Correlation Selection method Using Machine Learning Techniques

Abdullah

Abdulazeez

Sallow

2021

QAJ

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“…Monkam et al (2019) provided survey on importance of Convolutional neural network for predicting lung module with almost greater than 90% accuracy [ 21 ]. Asuntha and Andy Srinivasan (2019) proposed fuzzy particle swarm optimization with deep neural network on lung cancer images to achieve an accuracy of 99.2% [ 5 ]. Ganggayah et al (2019) used various classifiers on breast cancer data having 8066 record with 23 predictor and concluded that random forest classifier gives 82% better accuracy [ 9 ].…”

Section: Related Workmentioning

confidence: 99%

Prediction of Lung Cancer Using Machine Learning Classifier

Patra

2020

Communications in Computer and Information Science

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Lung cancer generally occurs in both male and female due to uncontrollable growth of cells in the lungs. This causes a serious breathing problem in both inhale and exhale part of chest. Cigarette smoking and passive smoking are the principal contributor for the cause of lung cancer as per world health organization. The mortality rate due to lung cancer is increasing day by day in youths as well as in old persons as compared to other cancers. Even though the availability of high tech Medical facility for careful diagnosis and effective medical treatment, the mortality rate is not yet controlled up to a good extent. Therefore it is highly necessary to take early precautions at the initial stage such that it’s symptoms and effect can be found at early stage for better diagnosis. Machine learning now days has a great influence to health care sector because of its high computational capability for early prediction of the diseases with accurate data analysis. In our paper we have analyzed various machine learning classifiers techniques to classify available lung cancer data in UCI machine learning repository in to benign and malignant. The input data is prepossessed and converted in to binary form followed by use of some well known classifier technique in Weka tool to classify the data set in to cancerous and non cancerous. The comparison technique reveals that the proposed RBF classifier has resulted with a great accuracy of 81.25% and considered as the effective classifier technique for Lung cancer data prediction.

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“…Lung cancer is considered among the fiercest forms of diseases across the world. World health organization (WHO) estimates that more than 1.76 million perish annually due to this type of cancer, [1], [2]. One of the most important tools for the diagnosis of lung cancer is computed tomography (CT).…”

Section: Introductionmentioning

confidence: 99%

Transfer learning with GoogLeNet for detection of lung cancer

Al-Huseiny

Sajit

2021

IJEECS

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<p class="p1">The use of computer algorithms has gained momentum in filling/assisting roles of specialists especially in early diagnosis scenarios. This paper proposes the employment of deep neural networks (DNN) to detect images with malignant nodules of lung computed tomography (CT). The method includes subjecting input images to a simple and fast pre-processing which isolates regions of interest (ROI), that’s the lungs dominated area, ridding the images of other surrounding tissues and artefacts. Centered and size normalized images are then fed to a deep neural network for training and validation. In this work transfer learning is used to readjust GoogLeNet DNN to learn this medical data. This includes allowing final layers of the DNN to evolve while restricting deep layers. In this setting, a rough, unprocessed dataset, the IQ-OTH/NCCD lung cancer dataset was used to train/validate the proposed algorithm. Experimental results show that this algorithm scores 94.38% accuracy, which outperforms benchmark method previously used with this dataset.</p>

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Deep learning for lung Cancer detection and classification

Cited by 179 publications

References 32 publications

Lung cancer Prediction and Classification based on Correlation Selection method Using Machine Learning Techniques

Lung cancer Prediction and Classification based on Correlation Selection method Using Machine Learning Techniques

Prediction of Lung Cancer Using Machine Learning Classifier

Transfer learning with GoogLeNet for detection of lung cancer

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