Naïve Bayes Algorithm for Lung Cancer Diagnosis Using Image Processing Techniques

Adi, Kusworo; Widodo, Catur Edi; Widodo, Aris Puji; Gernowo, Rahmat; Pamungkas, Adi; Syifa, Rizky Ayomi

doi:10.1166/asl.2017.8654

Cited by 14 publications

(6 citation statements)

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“…Lung cancer has a 5-year survival rate of just 17 percent, but if diagnosed early on, the survival rate jumps to 54 percent. The seminal National Lung Screening Trial (NLST) demonstrated a 20% mortality reduction for people experiencing CT compared to plain chest radiography, making CT the de facto imaging modality for screening and identifying nascent lung cancers [1]. CT produces high-resolution, volumetric datasets that can resolve small and/or low-contrast nodules, as opposed to traditional chest radiography.However, there are many obstacles to accurate detection and successful screening when CT is used in this environment [2].…”

Section: Introductionmentioning

confidence: 99%

A Novel Bayesian Framework For Multi-State Disease Progression Of Lung Cancer

Karthikayani¹

2021

TURCOMAT

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CT screening has been commonly used to identify and diagnose lung cancer in its early stages. CT has been shown in clinical studies to reduce lung cancer mortality by 20% as compared to plain chest radiography; however, existing CT screening services face obstacles such as high over diagnosis rates, high costs, and elevated radiation exposure.The study develops computer and deep learning models for predictive lung cancer diagnosis and disease progression prediction in an effort to solve these difficulties. Using a symmetric chain code method and a machine learning system, a novel lung segmentation approach was first developed. The lung nodules connected to the lung wall are included in this process, which minimises over-segmentation error. Finally, to predict the inter disease progression of lung cancer, a Bayesian method was coupled with a prolonged Markov model.The resultant model calculates specific lung cancer state transition data, which can be used to make customised screening recommendations. Extensive trials and results have shown the efficacy of these approaches, paving the way for current CT screening systems to be optimised and improved.

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Section: Introductionmentioning

confidence: 99%

A Novel Bayesian Framework For Multi-State Disease Progression Of Lung Cancer

Karthikayani¹

2021

TURCOMAT

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“…Early image segmentation methods were mainly based on point, line, and edge detection and segmentation methods, using Robert operator [10], Canny operator [11], Sobel edge detection operator [12], and so on. Statisticsbased segmentation methods include unsupervised k-means clustering, FCM clustering, and Markov random field, and the other is supervised support vector machine (SVM) [13], naive bayes (NB) [14], and random forest model [15]. Compared with the unsupervised segmentation algorithm, the supervised one obtains certain prior knowledge through training and performs better in image segmentation.…”

Section: Introductionmentioning

confidence: 99%

An Autocontouring Method for the Kidneys Using an Adaptive Weighted Multikernel Support Vector Machines

2021

Scientific Programming

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In radiotherapy, the location of the target area is very important. If the target area is small, the treatment is not complete, so the location of the target area is generally larger than the actual cancerous site. However, the damage of radiotherapy to normal cells is the same. In order to reduce the damage to the body as much as possible, we need to complete the most suitable target area. This paper uses an adaptive weighted multikernel support vector machine, which solves the parameter problem in the traditional multikernel support vector machine. The new AW-SVM can adjust the kernel weights adaptively. We completed our experiment on the abdominal MR dataset, using DSI as an evaluation indicator, and the experimental results showed its excellent classification performance. The minimum value of DSI in all results is 0.9654 (more than 0.7 is acceptable).

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“…According to the number of classifiers, the classification methods can be further divided into single classifier algorithms and multi-classifier algorithms. Among them, decision tree [1], support vector machine (SVM) [2], and naive Bayes (NB) [3] are typical algorithms based on single classifier. To address the performance limitations of single classifiers, multi-classifiers were developed.…”

Section: Introductionmentioning

confidence: 99%

Similarity based on the Importance of Common Features in Random Forest

Xiao¹

2019

IJPE

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In the existing methods for calculating the similarity between samples in random forests, the only case considered is where different samples fall on the same leaf node of the decision tree. The cases where there are leaf nodes in different positions of the decision tree or the sample falls on different leaves are neglected, thus affecting the accuracy of the similarity. In this paper, firstly, according to the difference of the leaf nodes in different positions of the decision tree, the importance of the sample features to which the leaf nodes belong are used as an attribute to describe the similarity. Secondly, for the case that the samples fall on different leaf nodes, the common features between samples are taken as another attribute to describe the similarity. Therefore, the measure method SICF (similarity between samples based on the importance of common features) is proposed. Finally, it is applied to the K-nearest neighbor classification algorithm, and the validity and correctness of the similarity are verified by the OOB index. The experimental results show that for the UCI data set, compared with two classical methods, the similarity SICF achieves better classification results.

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Naïve Bayes Algorithm for Lung Cancer Diagnosis Using Image Processing Techniques

Cited by 14 publications

References 0 publications

A Novel Bayesian Framework For Multi-State Disease Progression Of Lung Cancer

A Novel Bayesian Framework For Multi-State Disease Progression Of Lung Cancer

An Autocontouring Method for the Kidneys Using an Adaptive Weighted Multikernel Support Vector Machines

Similarity based on the Importance of Common Features in Random Forest

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