Development of Computational Tool for Lung Cancer Prediction Using Data Mining

Chauhan, Divya; Jaiswal, Varun

doi:10.7753/ijcatr0507.1001

Cited by 3 publications

(2 citation statements)

References 19 publications

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“…In accordance to the relevant ANN, 38 KNN, 39 and SVM, 15,16,40 the efficiency of the proposed approach for clinical brain tumor analysis is done by using sensitivity, specificity, accuracy, precision, recall, F ‐measure, negative predictive value (NPV), and Mathews correlation coefficient (MCC). Specific performance measures are quantified with true negative ( T N ), true positive ( T P ), false negative ( F N ), and false positive ( F P ).…”

Section: Experimental Outcomesmentioning

confidence: 99%

Automated brain tumor detection and classification using weighted fuzzy clustering algorithm, deep auto encoder with barnacle mating algorithm and random forest classifier techniques

Anantharajan¹,

Gunasekaran²

2021

Int J Imaging Syst Tech

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Magnetic resonance imaging (MRI) scan analysis is an effective tool that accurately detects abnormal brain tissue. This manuscript proposes the strategy of segmentation of brain tumors in MRI images and uses the technique of weighted fuzzy factor based on kernel metrics. Here, a deep auto encoder (DAE) with barnacle mating algorithm (BMOA) and random forest (RF) classifier are used to tumor stage classification to enhance the accuracy of prediction. This manuscript presents a deep‐neural network structure, integrating DAE and RF, with a classification unit, which is used for the classification of brain MRI. Finally, the segmented features are graded by the DAE with BMOA and RF. The proposed method is executed in MATLAB site and the performance is analyzed with existing methods. The experimental outcomes of the proposed method are assessed and validated in MR brain images depending on accuracy, sensitivity, and specificity for performance with quality analysis.

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Section: Experimental Outcomesmentioning

confidence: 99%

Automated brain tumor detection and classification using weighted fuzzy clustering algorithm, deep auto encoder with barnacle mating algorithm and random forest classifier techniques

Anantharajan¹,

Gunasekaran²

2021

Int J Imaging Syst Tech

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“…This air is rich in oxygen purifies the blood in the lungs. The sample lungs image is presented in fig 2 [3].…”

Section: Introductionmentioning

confidence: 99%

An Improved Lung Cancer Prediction System using Image Processing

Kalyani*¹,

Raghavendra*²,

Prasad*³

2019

IJRTE

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Lung cancer is a disease that causes the cells present in the lungs which divide uncontrollably. This uncontrollable division of cells causes tumours which in turn decrease a person’s respiration. Early identification and diagnosis will help people to seek treatment and recover soon. Tumours are an abnormal mass of tissue that results when cells divide more than they should or do not die when they should. Identifying lung cancer in its early stages is very difficult but knowing about its symptoms is quite easy. Symptoms may be similar to those of respiratory problems or infections and sometimes there may be no symptoms at all. In this work mainly deals with the lung cancer detection using image processing techniques were involving all the intermediate stages such as preprocessing stage, noise removal, processing stage, postprocessing stage which finally gives output image after all those stages. Doctors can categorize tumour stage as initial or advanced based on patient CT scan report. The abnormal images are subjected to segmentation (threshold segmentation, watershed transformation) to focus on tumour portion. It mainly deals with image quality and clarity. Gabor filter algorithm plays a vital role for image enhancement in removing noise from an image. The ANN method gives us the best performance as it neglects the background and displays the required portion of an image that we need. This image processing technique is one of the most efficient way of detecting lung cancer.

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Machine Learning Based Support System for Students to Select Stream (Subject)

Sethi

Jaiswal

Ansari

2020

RACSC

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Background: In most of the countries, students have to select a subject/stream in the secondary education phase. Selection of subject/stream is crucial for students because further their career proceeds according to their selection. Mostly subject/stream selection cannot be changed in the further career. Inappropriate selection of subjects due to parental pressure, lack of information etc. can lead to limited success in the selected stream. Guidance for subject/stream selection based on information of successful scholars of their stream and information of students such as interest, family background, previous education and other associated can enhance the success in career. Methods: Data mining and machine learning based methods were developed on the above information. Data from the different institutions and students of two different streams were used for training and testing purposes. Different machine learning algorithms were used and methods with high accuracy (86.72) were developed. Result: Developed methods can be extended and used for different subject/stream selection.

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Development of Computational Tool for Lung Cancer Prediction Using Data Mining

Cited by 3 publications

References 19 publications

Automated brain tumor detection and classification using weighted fuzzy clustering algorithm, deep auto encoder with barnacle mating algorithm and random forest classifier techniques

Automated brain tumor detection and classification using weighted fuzzy clustering algorithm, deep auto encoder with barnacle mating algorithm and random forest classifier techniques

An Improved Lung Cancer Prediction System using Image Processing

Machine Learning Based Support System for Students to Select Stream (Subject)

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