Comparison Analysis of Linear Discriminant Analysis and Cuckoo-Search Algorithm in the Classification of Breast Cancer from Digital Mammograms

Chakravarthy, S. R. Sannasi; Rajaguru, Harikumar

doi:10.31557/apjcp.2019.20.8.2333

Cited by 17 publications

(4 citation statements)

References 16 publications

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“…The outcomes revealed that SVM attained good classification accuracy using the features selected by WOA. Chakaravarthy et al 13 designed a system to help physicians analyze digital mammogram images more accurately. Different WTs are employed to extract features from the mammogram images.…”

Section: Related Workmentioning

confidence: 99%

Enhanced grasshopper optimization‐based selection of ultrasound and elastography features for breast lesion classification

Rengarajan

Devasena²,

Gopu³

2023

Int J Imaging Syst Tech

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Breast cancer (BC) is the second leading cause of death in women worldwide, caused by the uncontrolled proliferation of malignant cells in the breast. It is very tough to differentiate cancer from non‐cancer cells because of their shape and structure. Therefore, early diagnosis and classification of breast tumors as benign is a hot research topic. However, existing methods for early diagnosis have shortcomings like high computational cost and low classification accuracy. To trounce those limitations, this paper presented a computer‐aided diagnosis (CAD) system employing metaheuristic algorithms and machine learning classifiers. The proposed system uses fused features of ultrasound (US) and elastography images to enhance classification accuracy. Firstly, both US and elastography images are separately preprocessed using an enhanced wavelet thresholding method to diminish speckle noise. Secondly, a new segmentation algorithm is proposed to extract the lesion region from the preprocessed US image. Then the contour of the segmented lesion is mapped on an elastography image to obtain the corresponding region of interest (ROI), and texture and morphology features are separately extracted and concatenated. Subsequently, the enhanced grasshopper optimization algorithm (EGOA) is adopted to select salient features and reduce the dimension. Finally, based on the selected features, two machine learning classifiers, linear discriminant analysis (LDA) and support vector machine (SVM), are employed to classify the lesions into benign and malignant. The effectiveness of the proposed method is validated on 520 tumor images, including both benign and malignant samples. The suggested performance has been assessed by computing the confusion matrix and receiver operating characteristic curve. The developed method, EGOA‐SVM, outperforms the other classifiers by reaching a higher classification rate of 99.4%, specificity of 100%, the sensitivity of 99.2%, and Matthew's coefficient of 0.979. The introduced method can improve the radiologist's performance in categorizing breast lesions with a good classification rate. It could be a promising tool for clinical use.

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Section: Related Workmentioning

confidence: 99%

Enhanced grasshopper optimization‐based selection of ultrasound and elastography features for breast lesion classification

Rengarajan

Devasena²,

Gopu³

2023

Int J Imaging Syst Tech

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“…In our previous work, the CuSA and firefly optimization algorithm are modified for the classification of mammogram. 31,32 To the extent of our knowledge, the application of the crow-search optimization (CSO) algorithm for the problem of classification is not reported in the literature. This research paper is one of the first reports of the implementation of nature-inspired original CSO (OCSO) algorithm to address the microcalcification classification in digital mammograms.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, it is very clear that the research related to the usage of optimization algorithms for the task of classification is quite less as in the literature survey. In our previous work, the CuSA and firefly optimization algorithm are modified for the classification of mammogram 31,32 . To the extent of our knowledge, the application of the crow‐search optimization (CSO) algorithm for the problem of classification is not reported in the literature.…”

Section: Introductionmentioning

confidence: 99%

A novel improved crow‐search algorithm to classify the severity in digital mammograms

Chakravarthy

Rajaguru

2020

Int J Imaging Syst Tech

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The survival rates of breast cancer are going up due to the emerging increase in its screening and diagnosis methods. However, breast cancer is yet the most intrusive disease found in women. Many techniques are emerging during recent years for the investigation of breast cancer using imaging modalities. The paper intends to categorize the severity present in the digital mammography images as either benign (B) or malignant (M) using an improved crow‐search optimization algorithm (ImCSOA). In the literature, the CSOA is generally used for solving several feature selection and numerical optimization problems. The objective is to utilize this popular optimization algorithm for the problem of biomedical image classification. However, if this algorithm is applied directly to classification problems, then it will result in poor classification of data. Hence, the original CSO (OCSO) algorithm undergoes suitable enhancements using a novel controlled parameter tuning, control operator and chaotic‐maps‐based controlled randomness. Four distinct chaotic maps are used for controlling the randomness in the OCSO algorithm. The mammogram images are obtained from the Mammographic Image Analysis Society and Digital Database for Screening Mammography data sets for the evaluation. The classification is accomplished through discrete wavelet transform‐based statistical features that are extracted at two levels [level 4 (L4) and level 6 (L6)] of decomposition. For both data sets, the ImCSOA with L4 and L6 decomposed bior4.4 wavelet features provides the maximum accuracy of around 85% to 86%, which is approximately 62% to 88% better than the OCSO algorithm with L4 and L6 decomposed bior4.4 wavelet features.

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“…Breast cancer (BRCA) is the most common malignancy in women, with a significantly increasing incidence during the reproductive years [ 1 ]. BRCA is a primary cause of disease and death for women worldwide, [ 2 , 3 ], and most BRCA-related deaths are due to metastasis [ 4 ]. However, because breast cancer is a heterogeneous tumor, there are great differences in histomorphology, immunophenotype, biological behavior, and treatment response.…”

Section: Introductionmentioning

confidence: 99%

Construction of a Potential Breast Cancer-Related miRNA-mRNA Regulatory Network

Liu

Chen

Tan

et al. 2020

BioMed Research International

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Background. Breast cancer is a malignant tumor that occurs in the epithelial tissue of the breast gland and has become the most common malignancy in women. The regulation of the expression of related genes by microRNA (miRNA) plays an important role in breast cancer. We constructed a comprehensive breast cancer-miRNA-gene interaction map. Methods. Three miRNA microarray datasets (GSE26659, GSE45666, and GSE58210) were obtained from the GEO database. Then, the R software “LIMMA” package was used to identify differential expression analysis. Potential transcription factors and target genes of screened differentially expressed miRNAs (DE-miRNAs) were predicted. The BRCA GE-mRNA datasets (GSE109169 and GSE139038) were downloaded from the GEO database for identifying differentially expressed genes (DE-genes). Next, GO annotation and KEGG pathway enrichment analysis were conducted. A PPI network was then established, and hub genes were identified via Cytoscape software. The expression and prognostic roles of hub genes were further evaluated. Results. We found 6 upregulated differentially expressed- (DE-) miRNAs and 18 downregulated DE-miRNAs by analyzing 3 Gene Expression Omnibus databases, and we predicted the upstream transcription factors and downstream target genes for these DE-miRNAs. Then, we used the GEO database to perform differential analysis on breast cancer mRNA and obtained differentially expressed mRNA. We found 10 hub genes of upregulated DE-miRNAs and 10 hub genes of downregulated DE-miRNAs through interaction analysis. Conclusions. In this study, we have performed an integrated bioinformatics analysis to construct a more comprehensive BRCA-miRNA-gene network and provide new targets and research directions for the treatment and prognosis of BRCA.

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Comparison Analysis of Linear Discriminant Analysis and Cuckoo-Search Algorithm in the Classification of Breast Cancer from Digital Mammograms

Cited by 17 publications

References 16 publications

Enhanced grasshopper optimization‐based selection of ultrasound and elastography features for breast lesion classification

Enhanced grasshopper optimization‐based selection of ultrasound and elastography features for breast lesion classification

A novel improved crow‐search algorithm to classify the severity in digital mammograms

Construction of a Potential Breast Cancer-Related miRNA-mRNA Regulatory Network

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