Detection of breast abnormalities in digital mammograms using the electromagnetism-like algorithm

Soulami, Khaoula Belhaj; Saïdi, M.; Honnit, Bouchra; Anibou, Chaimae; Tamtaoui, Ahmed

doi:10.1007/s11042-018-5934-4

Cited by 17 publications

(10 citation statements)

References 23 publications

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“…In this article, the proposed segmentation model performance is simulated by MATLAB (2018a) software tool with Windows 10 operating system (64 bits), 2 TB hard disk, Intel Core i7 processor, and 8 GB RAM. The performance of the proposed model is related with a few benchmark models like U-Net [ 20 ], EML [ 24 ], and Dense-U-Net with attention gates [ 25 ] for evaluating the efficiency of the proposed model over existing models such as FCM, k -means clustering, and traditional EML. Additionally, the performance of proposed multilevel multiobjective EML model is analysed in light of Jaccard coefficient, dice coefficient, specificity, sensitivity, and accuracy on DDSM and MIAS databases.…”

Section: Resultsmentioning

confidence: 99%

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[Retracted] Segmentation of Breast Masses in Mammogram Image Using Multilevel Multiobjective Electromagnetism‐Like Optimization Algorithm

Ittannavar¹,

Havaldar

2022

BioMed Research International

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In recent times, breast mass is the most diagnostic sign for early detection of breast cancer, where the precise segmentation of masses is important to reduce the mortality rate. This research proposes a new multiobjective optimization technique for segmenting the breast masses from the mammographic image. The proposed model includes three phases such as image collection, image denoising, and segmentation. Initially, the mammographic images are collected from two benchmark datasets like Digital Database for Screening Mammography (DDSM) and Mammographic Image Analysis Society (MIAS). Next, image normalization and Contrast-Limited Adaptive Histogram Equalization (CLAHE) techniques are employed for enhancing the visual capability and contrast of the mammographic images. After image denoising, electromagnetism-like (EML) optimization technique is used for segmenting the noncancer and cancer portions from the mammogram image. The proposed EML technique includes the advantages like enhanced robustness to hold the image details and adaptive to local context. Lastly, template matching is carried out after segmentation to detect the cancer regions, and then, the effectiveness of the proposed model is analysed in light of Jaccard coefficient, dice coefficient, specificity, sensitivity, and accuracy. Hence, the proposed model averagely achieved 92.3% of sensitivity, 99.21% of specificity, and 98.68% of accuracy on DDSM dataset, and the proposed model averagely achieved 92.11% of sensitivity, 99.45% of specificity, and 98.93% of accuracy on MIAS dataset.

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Section: Resultsmentioning

confidence: 99%

“…Soulami et al [ 24 ] developed an automated framework for detecting the suspicious portions using the mammographic images. Firstly, two-dimensional median filter was used for enhancing the visual capability of the acquired mammographic images.…”

Section: Related Workmentioning

confidence: 99%

[Retracted] Segmentation of Breast Masses in Mammogram Image Using Multilevel Multiobjective Electromagnetism‐Like Optimization Algorithm

Ittannavar¹,

Havaldar

2022

BioMed Research International

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“…From various vision objects, useful structural information can be derived using Canny edge detection, which decrease total data that needs to be practiced dramatically. On diverse vision systems, edge detection application's requirements are relatively similar as found by Canny (Soulami et al, 2019).…”

Section: Canny Edge Detectionmentioning

confidence: 89%

Untitled

2021

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For women, most common cause of death is Breast tumour and in worldwide, it is the second leading reason for cancer deaths. Due the requirement of breast cancer's early detection and false diagnosis impact on patients, made researchers to investigate Deep Learning (DL) techniques for mammograms. There are four stages in this proposed HIRResCNN framework, namely, Pre-processing, reduction of dimensionality, segmentation and classification. From images, noises are removed using two filtering algorithms called Median and mean filtering in pre-processing stage. Then canny edge detector is used for detecting edges. Gaussian filtering is used in canny edge detector to smoothen the images. In the next dimensionality reduction stage, attributes are correlated using Principal Component Analysis (PCA) inclusive of related features. So, this huge dataset is minimized and only few variables are used for expressing it. In order to detect the breast cancer accurately, foreground and background subtraction is done in the third stage called segmentation stage. At last, for detecting and classifying breast cancer, a Hybrid Inception Recurrent Residual Convolutional Neural Network (HIRResCNN) is introduced, which integrates Harmony Search Optimization (HSO) to tune bias and weight parameters and classification accuracy is enhanced using HIRResCNN-HSO model. Strength of Recurrent Convolutional Neural Network (RCNN), Residual Network (ResNet) and Inception Network (Inception-v4), are combined in a powerful Deep Convolutional Neural Network (DCNN) model called HIRResCNN. using Mammographic Image Analysis Society (MIAS) dataset, various experiments are conduced and results are compared with other available techniques. Around 92.6% accuracy rate is produced using this proposed HIRResCNN classifier in finding breast cancer.

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“…The main problem of this method is that, it needs expert intervention and is time consuming Neto et al [17] Segmentation of masses Swarm optimization, area filters and texture descriptors Reduced false positives. However, the method could not detect small masses in non-dense breast density type and failed to detect mass in case of dense breast density types Soulami et al [18] Segmentation of masses Meta-heuristic algorithm electromagnetism-like optimization (EMC) Unable to remove the muscle completely in some cases and hence achieved less accuracy in segmentation of mass The method considers whole breast region for classification instead of particular abnormal region of interest and obtained satisfactory outputs Divyashree et al [27] Locating the region of interest in mammograms Thresholding method and quad tree decomposition Simple method, very well taken care of information loss during segmentation of breast region and suitable for subtle masses.…”

Section: Region-based Active Contour Modelmentioning

confidence: 99%

Breast Cancer Mass Detection in Mammograms Using Gray Difference Weight and MSER Detector

Divyashree

Kumar

2021

SN COMPUT. SCI.

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Breast cancer is a deadly and one of the most prevalent cancers in women across the globe. Mammography is widely used imaging modality for diagnosis and screening of breast cancer. Segmentation of breast region and mass detection are crucial steps in automatic breast cancer detection. Due to the non-uniform distribution of various tissues, it is a challenging task to analyze mammographic images with high accuracy. In this paper, background suppression and pectoral muscle removal are performed using gradient weight map followed by gray difference weight and fast marching method. Enhancement of breast region is performed using contrast limited adaptive histogram equalization (CLAHE) and de-correlation stretch. Detection of breast masses is accomplished by gray difference weight and maximally stable external regions (MSER) detector. Experimentation on Mammographic Image Analysis Society (MIAS) and curated breast imaging subset of digital database for screening mammography (CBIS-DDSM) show that the method proposed performs breast boundary segmentation and mass detection with best accuracies. Mass detection achieved high accuracies of about 97.64% and 94.66% for MIAS and CBIS-DDSM dataset, respectively. The method is simple, robust, less affected to noise, density, shape and size which could provide reasonable support for mammographic analysis.

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Detection of breast abnormalities in digital mammograms using the electromagnetism-like algorithm

Cited by 17 publications

References 23 publications

[Retracted] Segmentation of Breast Masses in Mammogram Image Using Multilevel Multiobjective Electromagnetism‐Like Optimization Algorithm

[Retracted] Segmentation of Breast Masses in Mammogram Image Using Multilevel Multiobjective Electromagnetism‐Like Optimization Algorithm

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Breast Cancer Mass Detection in Mammograms Using Gray Difference Weight and MSER Detector

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