Performance analysis of various machine learning-based approaches for detection and classification of lung cancer in humans

Singh, Gur Amrit Pal; Gupta, Praveen

doi:10.1007/s00521-018-3518-x

Cited by 133 publications

(58 citation statements)

References 19 publications

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“…Naveen and Pradeep (2018) proposed that among SVM, Naive Bayes and C4.5 classifier, C4.5 performs better on North central cancer treatment group (NCCTG) lung cancer data with better accuracy and also predicted that C4.5 is better classifier with the increase of lung cancer training data [ 25 ]. Gur Amrit Pal singh and P.K Gupta (2018) proposed new algorithm for feature extraction on image data and applied machine learning classifier to improve the accuracy [ 29 ]. Hussein et al (2019) proposed supervised learning using 3D Convolutional neural network(3D CNN)on lung nodules data set as well as unsupervised learning SVM approach to classify benign and malignant data with a accuracy of 91% [ 12 ].…”

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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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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“…Although the conventional automated system predicts lung cancer successfully, it continues to maintain its accuracy of recognition [19] and also takes longer to handle large volumes of data. The successful method for the identification of lung cancer, which has been discovered by Singh and Gupta [20], classifies photos of lung cancer as benign and malicious. The various computer classifications include Naïve Bay classifier, decision trees classifier, descent classifier, k‐closest neighbour classifier, vector support classifier for supervised learning, random forest classifier, and neural network classification for lung cancer scoring.…”

Section: Related Workmentioning

confidence: 99%

Automatic segmentation and classification of lung tumour using advance sequential minimal optimisation techniques

Rani¹,

Jawhar²

2020

IET image process

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“…Some researchers use other approaches to optimize hyperparameters in Deep Learning. Research conducted by Qolomany et al [4] shows the success of the application of Deep Learning by optimizing parameters using the Particle Swarm Optimization (PSO) method. PSO is very efficient in adjusting the number of optimal hidden layers and neurons.…”

Section: A R T I C L E I N F Omentioning

confidence: 99%

“…Several studies in the health sector develop preprocessing on CT Scan image data to obtain better results. As was done by Singh and Gupta [4] to detect cases of classification of lung cancer, previously, the image was processed and produced texture features (GLCM) and statistical features. It used 7 (seven) different approaches, namely KNN, SVM, Decision Tree, Naïve Bayes, SGD, Random Forest and MLP (one type of Deep Learning architecture).…”

Section: Introductionmentioning

confidence: 99%

Improving stroke diagnosis accuracy using hyperparameter optimized deep learning

Badriyah

Santoso

Syarif

et al. 2019

Int. J. Adv. Intell. Informatics

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Stroke may cause death for anyone, including youngsters. One of the early stroke detection techniques is a Computerized Tomography (CT) scan. This research aimed to optimize hyperparameter in Deep Learning, Random Search and Bayesian Optimization for determining the right hyperparameter. The CT scan images were processed by scaling, grayscale, smoothing, thresholding, and morphological operation. Then, the images feature was extracted by the Gray Level Co-occurrence Matrix (GLCM). This research was performed a feature selection to select relevant features for reducing computing expenses, while deep learning based on hyperparameter setting was used to the data classification process. The experiment results showed that the Random Search had the best accuracy, while Bayesian Optimization excelled in optimization time.

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Performance analysis of various machine learning-based approaches for detection and classification of lung cancer in humans

Cited by 133 publications

References 19 publications

Prediction of Lung Cancer Using Machine Learning Classifier

Prediction of Lung Cancer Using Machine Learning Classifier

Automatic segmentation and classification of lung tumour using advance sequential minimal optimisation techniques

Improving stroke diagnosis accuracy using hyperparameter optimized deep learning

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