Classification of Cancer of The Lungs Using SVM and ANN

Fenwa, O. D.; Ajala, F. A.; Adigun, Adebisi A.

doi:10.24297/ijct.v15i1.1715

Cited by 6 publications

(6 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Paper [4,5] gives a carcinoma detection machine victimization picture technique and device learning are hired to categorize the presence of carcinoma at some point of a CT-scan images and blood samples. Fenwa et al, [6] proposed a version in which they extracted the properties like brightness, the contrast from picture dataset. The use of extracting texture-primarily based features and on the one's varieties of ML set of rules are carried out one is ANN any other one is SVM, after which overall performance has been evaluated on each the set of rules to examine which set of rules is giving extra accuracy.…”

Section: Literature Surveymentioning

confidence: 99%

“…The picks created with the aid of using the doctors are the most vital elements in designation but recently, the utility of diverse AI class strategies is evidenced in helping doctors to facilitate their techniques. Possible mistakes which can also additionally arise due to unskilled doctors are regularly reduced with the aid of using mistreatment class strategies [6]. Machine getting to know (ML) is a utility of AI which suggests flexibility to robotically study and enhance its performance by experience without programming.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Prediction of Lung Cancer using Ensemble Classifiers

Shanbhag

Prabhu

Reddy

et al. 2022

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

Carcinoma detection from CT scan images is extremely necessary for numerous diagnostic and healing applications. Because of the excessive amount of information in CT scan images and blurred boundaries, tumor segmentation and class are extremely laborious. The intention is to categorize carcinoma into benign and malignant categories. In MR pictures, the number of facts is a lot for interpreting and evaluating manually. Over the previous few years, carcinoma detection in CT has grown to be a rising evaluation space in the area of the scientific imaging system. Correct detection of length and site of lung cancer performs a vital position in the designation of carcinoma. In this paper, we introduce a novel carcinoma detection methodology that helps in predicting the carcinoma from the CT scanned images. The methodology has 4 different stages, pre-processing the image data, segmentation, extracting features, and classification stage to categorize the benign and malignant. This work makes use of extraordinary models for detecting carcinoma in a CT test via way of means of constructing an ensemble classifier. Techniques proposed in the paper helped us achieve an accuracy of 85% using Ensemble-Classifier which showcases that model has the capability of predicting the malignant cases correctly. The ensemble classifier consists of 5 machine learning models like SVM, LR, MLP, decision tree, and KNN. The inevitable parameters like accuracy, recall, and precision is calculated to determine the accurate results of the classifier.

show abstract

Section: Literature Surveymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prediction of Lung Cancer using Ensemble Classifiers

Shanbhag

Prabhu

Reddy

et al. 2022

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

show abstract

“…Based on classification precision rate prediction with an online decision support platform can aid doctors to provide patient-specific and evidence-based treatment that would benefit the patient. Olusayo D. FENWA et al, [6] has used SVM with kernel function linear and RBF (Radial Basis Function) for classification of images into two classes namely "Idiopathic Pulmonary Diseases" and "Chronic Obstructive Pulmonary Disease". The labels for these classes are using "1" and "2" for "Normal" and "Abnormal" respectively, based on classification confusion matrix is created to show classification and misclassification.…”

Section: Fig7 Scenario 5 After Using Kernelmentioning

confidence: 99%

“…Not only it classify whether the tumour is cancerous or not but also it display the range of the tumour. Olusayo D. FENWA et al, [6] the artificial neural network extracts features such as texture and roughness from the images and performs accurate classification sequence, together with the train sets. Here Backpropagation algorithm has been used for training multilayer artificial neural network.…”

Section: Use Of Neural Network In Lung Cancer Predictionmentioning

confidence: 99%

Machine Learning Techniques for Prediction of Lung Cancer

Banerjee¹,

Das²

2020

IJRTE

View full text Add to dashboard Cite

Lung cancer has been one of the deadliest diseases in today’s decades. It has become one of the causes of death in both man and woman. There are various reasons for which lung cancer occurs but classification of tumor and predicting it in the right stage is the most important part. This paper focused on the numerous approaches has been derived for lung cancer detection from different literature survey to advance the ability of detection of cancer. Digital image processing and data mining both are equally important because for prediction either image dataset or statistical dataset is used so for pre-processing the image dataset digital image processing is applied for statistical dataset data mining is applied. After pre-processing, segmentation and feature extraction we apply various machine learning algorithm for the prediction of lung cancer. So first we have provided a sketch of Machine learning and then various fields like in image data or statistical data where machine learning has been used for classification. Once the classification is done confusion matrix is generated for calculating accuracy, sensitivity, precision, these method is used to measure the rate of accuracy of the proposed model.

show abstract

“…In addition, hybridization between SVM and other methods was considered a novel method. FENWA [25] proposed an evolutionary-driven support vector machine for determining the degree of liver fibrosis in COPD.…”

Section: Related Workmentioning

confidence: 99%

Diagnosis of COPD Based on a Knowledge Graph and Integrated Model

Fang

Wang

et al. 2019

IEEE Access

View full text Add to dashboard Cite

Chronic obstructive pulmonary disease (COPD) is a chronic lung disease that causes a progressive decline in respiratory function. Diagnosing COPD in the early curable stages is very important and may even save the life of a patient. In this paper, we present an integrated model for diagnosing COPD based on a knowledge graph. First, we construct a knowledge graph of COPD to analyze the relationship between feature subsets and further discover the knowledge of implied diseases from the data. Second, we propose an algorithm for sorting features and an adaptive feature subset selection algorithm CMFS-η, which selects an optimal subset of features from the original high-dimensional set. Finally, the DSA-SVM integrated model is suggested to build the classifier for the diagnosis and prediction of COPD. We performed extensive experiments on the dataset from the hospital outpatient electronic medical record database. The classification accuracy of our method was 95.1%. It is superior to some state-of-the-art classification methods for this problem.

show abstract

Classification of Cancer of The Lungs Using SVM and ANN

Cited by 6 publications

References 7 publications

Prediction of Lung Cancer using Ensemble Classifiers

Prediction of Lung Cancer using Ensemble Classifiers

Machine Learning Techniques for Prediction of Lung Cancer

Diagnosis of COPD Based on a Knowledge Graph and Integrated Model

Contact Info

Product

Resources

About