Lung Lesion Classification Scheme Using Optimization Techniques and Hybrid (KNN-SVM) Classifier

Rani, K. Vijila; Jawhar, S. Joseph

doi:10.1080/03772063.2019.1654935

Cited by 24 publications

(7 citation statements)

References 26 publications

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“…Several researchers have employed a wide range of features (textural features and/or morphological). These features represent some characteristics of the region that are not entirely shared by the healthy and diseased image [22, 24]. The features extracted from shape and contour properties are defined as morphological features [25].…”

Section: Proposed Methodsmentioning

confidence: 99%

“…Even though it is a linear classifier, mapping features are used with non‐linear problems. For this mapping to a higher dimension, SVM makes use of some functions known as kernel functions [24]. Standard kernel functions include polynomial kernels, radial basis kernels, and sigmoid kernels.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…Finally, the SVM‐based SMO technique is proposed for training the classifier for classification purpose. Iterative ASMO algorithm solves the QP problem that arises in SVMs [24].…”

Section: Proposed Methodsmentioning

confidence: 99%

See 2 more Smart Citations

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

Rani¹,

Jawhar²

2020

IET image process

Self Cite

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Section: Proposed Methodsmentioning

confidence: 99%

Section: Proposed Methodsmentioning

confidence: 99%

See 1 more Smart Citation

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

Rani¹,

Jawhar²

2020

IET image process

Self Cite

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“…30 Vijila Rani et al proposed a new lung cancer detection technique that uses SVM optimized by gray wolf optimization (GWO) and WOA to classify normal and abnormal lung images from CT scans. 31 Babu et al proposed a system for detecting colon cancer that uses Bayesian techniques to optimize SVM classifiers to improve the accuracy of automated patient diagnosis and prognosis. 32 Wang et al proposed an improved support vector machine based on cuckoo search algorithm optimization to predict the prognosis of ESCC patients.…”

Section: Related Workmentioning

confidence: 99%

A survival prediction model based on PCA-HSIDA-LSSVM for patients with esophageal squamous cell carcinoma

Wang,

Xia,

Ling

et al. 2023

Proc Inst Mech Eng H

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Esophageal squamous cell carcinoma (ESCC) is a type of cancer and has some of the highest rates of both incidence and mortality globally. Developing accurate models for survival prediction provides a basis clinical judgment and decision making, improving the survival status of ESCC patients. Although many predictive models have been developed, there is still lack of highly accurate survival prediction models for ESCC patients. This study proposes a novel survival prediction model for ESCC patients based on principal component analysis (PCA) and least-squares support vector machine (LSSVM) optimized by an improved dragonfly algorithm with hybrid strategy (HSIDA). The original 17 blood indicators are condensed into five new variables by PCA, reducing data dimensionality and redundancy. An improved dragonfly algorithm based on hybrid strategy is proposed, which addresses the limitations of dragonfly algorithm, such as slow convergence, low search accuracy and insufficient vitality of late search. The proposed HSIDA is used to optimize the regularization parameter and kernel parameter of LSSVM, improving the prediction accuracy of the model. The proposed model is validated on the dataset of 400 patients with ESCC in the clinical database of First Affiliated Hospital of Zhengzhou University and the State Key Laboratory of Esophageal Cancer Prevention and Control of Henan Province. The experiment results demonstrate that the proposed HSIDA-LSSVM has the best prediction performance than LSSVM, HSIDA-BP, IPSO-LSSVM, COA-LSSVM and IBA-LSSVM. The proposed model achieves the accuracy of 96.25%, sensitivity of 95.12%, specificity of 97.44%, precision of 97.50%, and F1 score of 96.30%.

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“…While this may be an easy task for a human to perform, it is a highly complicated thing for a machine: computer or robot 5 . Shape‐based lung lesion segmentation algorithm proposed by Bach et al 6 Image segmentation has a variety of usage in biomedical research it includes localize some specific cells and tissues, for example, lesion or tumor, in computed tomography (CT) images Vijila Rani et al 7 …”

Section: Introductionmentioning

confidence: 99%

Superpixel with nanoscale imaging and boosted deep convolutional neural network concept for lung tumor classification

Rani

Jawhar

2020

Int J Imaging Syst Tech

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Lung tumor is a complex illness caused by irregular lung cell growth. Earlier tumor detection is a key factor in effective treatment planning. When assessing the lung computed tomography, the doctor has many difficulties when determining the precise tumor boundaries. By offering the radiologist a second opinion and helping to improve the sensitivity and accuracy of tumor detection, the use of computer‐aided diagnosis could be near as effective. In this research article, the proposed Lung Tumor Detection Algorithm consists of four phases: image acquisition, preprocessing, segmentation, and classification. The Advance Target Map Superpixel‐based Region Segmentation Algorithm is proposed for segmentation purposes, and then the tumor region is measured using the nanoimaging theory. Using the concept of boosted deep convolutional neural network yields 97.3% precision, image recognition can be achieved. In the types of literature with the current method, which shows the study's proposed efficacy, the implementation of the proposed approach is found dramatically.

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Lung Lesion Classification Scheme Using Optimization Techniques and Hybrid (KNN-SVM) Classifier

Cited by 24 publications

References 26 publications

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

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

A survival prediction model based on PCA-HSIDA-LSSVM for patients with esophageal squamous cell carcinoma

Superpixel with nanoscale imaging and boosted deep convolutional neural network concept for lung tumor classification

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