Automated breast cancer detection in digital mammograms: A moth flame optimization based ELM approach

Muduli, Debendra; Dash, Ratnakar; Majhi, Banshidhar

doi:10.1016/j.bspc.2020.101912

Cited by 99 publications

(42 citation statements)

References 58 publications

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“…In recent decades, breast cancer is the most common cause of death among women worldwide. The scientific reports show that the early treatment of breast cancer increases the survival rate of the patients [ 1 , 2 ]. Several imaging modalities are applied for breast cancer detection like X-ray, ultrasound, magnetic resonance imaging (MRI), histology, positron emission tomography (PET), and computerized tomography (CT).…”

Section: Introductionmentioning

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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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: Introductionmentioning

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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“…We discovered that the proposed model yields better results compared to the traditional FD and LBP features. The previous study [46] obtained 100% sensitivity, and the previous study [58] gives 100% accuracy in the Mini-MIAS dataset. Table 9 depicts that our proposed model yields better sensitivity and accuracy compared to previous studies [57,58] in the DDSM dataset.…”

Section: Comparision With Other Techniquesmentioning

confidence: 79%

“…The previous study [46] obtained 100% sensitivity, and the previous study [58] gives 100% accuracy in the Mini-MIAS dataset. Table 9 depicts that our proposed model yields better sensitivity and accuracy compared to previous studies [57,58] in the DDSM dataset. In the same trend, the previous study [63] gives 100% sensitivity and our proposed model gives 99.79% in the DDSM dataset due to using a small set of mammogram images.…”

Section: Comparision With Other Techniquesmentioning

confidence: 79%

Breast Cancer Detection Using Mammogram Images with Improved Multi-Fractal Dimension Approach and Feature Fusion

Zebari

Ibrahim²,

Zeebaree

et al. 2021

Applied Sciences

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Breast cancer detection using mammogram images at an early stage is an important step in disease diagnostics. We propose a new method for the classification of benign or malignant breast cancer from mammogram images. Hybrid thresholding and the machine learning method are used to derive the region of interest (ROI). The derived ROI is then separated into five different blocks. The wavelet transform is applied to suppress noise from each produced block based on BayesShrink soft thresholding by capturing high and low frequencies within different sub-bands. An improved fractal dimension (FD) approach, called multi-FD (M-FD), is proposed to extract multiple features from each denoised block. The number of features extracted is then reduced by a genetic algorithm. Five classifiers are trained and used with the artificial neural network (ANN) to classify the extracted features from each block. Lastly, the fusion process is performed on the results of five blocks to obtain the final decision. The proposed approach is tested and evaluated on four benchmark mammogram image datasets (MIAS, DDSM, INbreast, and BCDR). We present the results of single- and double-dataset evaluations. Only one dataset is used for training and testing in the single-dataset evaluation, whereas two datasets (one for training, and one for testing) are used in the double-dataset evaluation. The experiment results show that the proposed method yields better results on the INbreast dataset in the single-dataset evaluation, whilst better results are obtained on the remaining datasets in the double-dataset evaluation. The proposed approach outperforms other state-of-the-art models on the Mini-MIAS dataset.

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“…DBN can only be pre-trained until the last hidden layer, but the weights are selected randomly among the output layer and the previous hidden layer. Another study by Muduli et al [ 91 ] performed feature reduction and classification by fusing the extreme learning machine and the moth flame optimization technique (MFO-ELM) for BrC classification. The feature reduction may lead to data loss.…”

Section: Breast-cancer-diagnosis Methods Based On Deep Learningmentioning

confidence: 99%

A Comprehensive Survey on Deep-Learning-Based Breast Cancer Diagnosis

Mridha

Hamid

Monowar

et al. 2021

Cancers

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Breast cancer is now the most frequently diagnosed cancer in women, and its percentage is gradually increasing. Optimistically, there is a good chance of recovery from breast cancer if identified and treated at an early stage. Therefore, several researchers have established deep-learning-based automated methods for their efficiency and accuracy in predicting the growth of cancer cells utilizing medical imaging modalities. As of yet, few review studies on breast cancer diagnosis are available that summarize some existing studies. However, these studies were unable to address emerging architectures and modalities in breast cancer diagnosis. This review focuses on the evolving architectures of deep learning for breast cancer detection. In what follows, this survey presents existing deep-learning-based architectures, analyzes the strengths and limitations of the existing studies, examines the used datasets, and reviews image pre-processing techniques. Furthermore, a concrete review of diverse imaging modalities, performance metrics and results, challenges, and research directions for future researchers is presented.

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Automated breast cancer detection in digital mammograms: A moth flame optimization based ELM approach

Cited by 99 publications

References 58 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

Breast Cancer Detection Using Mammogram Images with Improved Multi-Fractal Dimension Approach and Feature Fusion

A Comprehensive Survey on Deep-Learning-Based Breast Cancer Diagnosis

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