Extraction of spiculated parts of mammogram tumors to improve accuracy of classification

Pezeshki, Hamed; Rastgarpour, Maryam; Sharifi, Arash; Yazdani, Saeed

doi:10.1007/s11042-019-7185-4

Cited by 21 publications

(6 citation statements)

References 34 publications

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“…After the detection and diagnosis of breast cancer masses that was performed with high accuracy in this study, according to the morphological and simple features of cancer masses, classification operation was performed well and with high accuracy. The results of this study show the higher performance of the proposed method compared to other methods used in previous related studies [6][7][8][9][10][11][12][13][14][15][16].…”

Section: Discussionmentioning

confidence: 72%

Mammographic Image Processing for Classification of Breast Cancer Masses by Using Support Vector Machine Method and Grasshopper Optimization Algorithm

Safdarian

Dezfoulinejad

2020

J. Arak Univ. Med. Sci.

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Background and Aim Breast cancer is the abnormal cell growth in the breast. In both benign and malignant masses, there is rapid and high cell growth. Nowadays, due to the development of technologies, the diagnosis of diseases has become non-invasive and physicians attempts to diagnose the disease without surgery and based on internal organ images.Methods & Materials In this study, by using images prepared from the Digital Database for Screening Mammography (DDSM), a new method is proposed for detecting cancerous masses in the mammographic images using geometric features extraction and optimization of Support Vector Machine (SVM) parameters to classify breast cancer masses automatically. First, images were pre-processed and then boundaries were determined using threshold method. Next, morphological operators were used to improve these boundaries and the segmentation of images was carried out to classify cancerous masses. Finally, by using the SVM parameter optimization method, Grasshopper Optimization Algorithm (GOA), and 4-fold crossvalidation method, data were classified into two groups of benign and malignant (cancer) masses. Ethical Considerations Images from DDSM database were used in this research, all images are open access in this database. Results The accuracy, sensitivity and specificity values for applying the Radial Basis Function (RBF) kernel in SVM classifier (before optimization process) were obtained 97%, 100% and 96, respectively. After optimization of SVM parameters by the GOA, it was reported 100% for all accuracy, sensitivity and specificity indices for applying linear kernel function, indicating the high accuracy of the proposed method. The average values of accuracy, sensitivity and specificity indices for applying all three SVM kernel functions after optimization were 95.83, 100 and 94.81%, respectively. Conclusion The extracted geometrical features from breast cancer masses are highly efficient for model training and the diagnosis of breast cancer. The GOA could improve the overall accuracy of the proposed method by optimizing the SVM parameters. The results showed the higher performance of the proposed method compared to other methods.

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Section: Discussionmentioning

confidence: 72%

Mammographic Image Processing for Classification of Breast Cancer Masses by Using Support Vector Machine Method and Grasshopper Optimization Algorithm

Safdarian

Dezfoulinejad

2020

J. Arak Univ. Med. Sci.

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“…Pratiwi et al [39] proposed feature selection based on the GLCM method for the same datasets and got the accuracy, sensitivity and specificity were 93.90, 97.20 and 91.50 %, respectively. Pezeshki et al [40] used ANN approaches and got an accuracy of 61 % which is very less as compare to this proposed model. Gupta et al [41] used a deep learning technique using Adam gradient descent accuracy achieved 1.16 % lesser than the proposed method.…”

Section: Resultsmentioning

confidence: 90%

Two-Stage Feature Selection Method Created for 20 Neurons Artificial Neural Networks for Automatic Breast Cancer Detection

Patel

Hadia

2022

Trends Sci

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Breast cancer is a common deadly diseases in women. Initial recognition of breast cancer using mammogram images is a challenging task. Hence, this paper proposed a unique automatic diagnosis model for breast cancer. Initially, the mammogram images are preprocessed with a median filter and contrast limited adaptive histogram equalization (CLAHE). The pre-processed image is automatically segmented using the multilevel threshold method. Subsequently, statistical, texture, shape, and geometric features are extracted from the segmented image. So, the length of the feature vector is high, and it is important to identify optimum features. In this paper, the dimension of the feature vector is reduced by 2-stage feature selection methods. Initially, the feature vector is applied to the best first search method information gain (IG) with rank feature method, and then secondly, apply the Pearson correlation method (PCM). Artificial neural networks (ANNs) are used to increase the classification accuracy of a breast cancer diagnosis. In this model, the selection of appropriate neurons in a single hidden layer is used to avoid overfitting problems in an ANN model. Based on optimum feature selection, the appropriate number of neurons chosen in the hidden layer is 20, which was applied for the proposed IG+PCM+Boosted-ANN model. The proposed model is applied on 2 regular datasets mini-Mammographic Image Analysis Society (mini-MIAS) and Digital Database for Screening Mammography (DDSM). The proposed model was superior to other exiting models and the model in this study achieves the accuracy of 99 and 98.80 % for mini-MIAS and DDSM datasets, respectively.

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“…Additionally, the selection of a classifier suitable for these features enhances the model's performance in classification tasks. The results were compared with those of the studies other authors [35], [36], [37] as listed in Table VII. This demonstrates that the boosting ensemble model outperforms the other methods.…”

Section: Classification Resultsmentioning

confidence: 99%

Enhancing and Denoising Mammographic Images for Tumor Detection using Bivariate Shrinkage and Modified Morphological Transforms

Thi Hoang Hua,

Hong Nguyen,

Bao Binh

et al. 2024

IJCDS

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Breast cancer stands as a prevalent concern for women worldwide. Mammography serves as the frontline defense for early detection, yet its low X-ray dosage often leads to suboptimal image quality. This study proposes a multi-step solution: (i) Image enhancement employs a two-step approach: denoising using bivariate shrinkage and a hybrid median filter based on stationary wavelet transform (SWT) to avoid shift variants, and it is combined with modified morphology operations including the background, a vignette image with the weighting function 1/R 2 . (ii) Segmentation utilizes the fast K-means algorithm with a straightforward technique to select the number of clusters and tumors automatically, within the segment containing the largest centroid. (iii) Classification employs a boosting ensemble model, based on statistical features extracted from SWT coefficients at different levels, for tumor classification to achieve reliable results. Utilizing mammograms from the MIAS (Mammographic Image Analysis Society) public dataset, the proposed method was tested on Gaussian noisy images, demonstrating superior performance compared to existing algorithms in detecting lesions. The segmentation achieves a high accuracy, 98.15% on average and a specificity of 99.56%. However, the ground truth occasionally extends beyond the tumor mass, resulting in a low sensitivity of 62.81%. Finally, classification is also performed using boosting ensemble learning with accuracy of 100% for training, testing and real data.

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Extraction of spiculated parts of mammogram tumors to improve accuracy of classification

Cited by 21 publications

References 34 publications

Mammographic Image Processing for Classification of Breast Cancer Masses by Using Support Vector Machine Method and Grasshopper Optimization Algorithm

Mammographic Image Processing for Classification of Breast Cancer Masses by Using Support Vector Machine Method and Grasshopper Optimization Algorithm

Two-Stage Feature Selection Method Created for 20 Neurons Artificial Neural Networks for Automatic Breast Cancer Detection

Enhancing and Denoising Mammographic Images for Tumor Detection using Bivariate Shrinkage and Modified Morphological Transforms

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