Breast cancer detection using active contour and classification by deep belief network

Malathi, M.; Sinthia, P.; Farzana, F.M. Aiysha; Mary, G. Aloy Anuja

doi:10.1016/j.matpr.2020.11.551

Cited by 18 publications

(16 citation statements)

References 9 publications

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“…In the technique of restoration of lowdosage medical images, the suggested algorithm is applied for removing the attributes of the function of the point spread and restoring the rebuilt image quality. (Malathi et al, 2021).…”

Section: Deep Belief Network (Dbns)mentioning

confidence: 99%

Comprehensive Study for Breast Cancer Using Deep Learning and Traditional Machine Learning

2022

ZJPAS

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Breast cancer is one of the most dangerous diseases and the second largest cause of women cancer death. Techniques and methods have been adopted for early indications of the disease signs as it's the only effective way of managing breast cancer in women. This review explores the techniques used for breast cancer in Computer-Aided Diagnosis (CAD) using image analysis, deep learning and traditional machine learning. It primarily gives an introduction to the various strategies of machine learning, followed by an explanation of the various deep learning techniques and particular architectures for breast cancer detection and their classification. After the review, the researcher recommended the need for the inclusion of deep learning in machine learning because it performs multi-functions in enabling medical diagnosis. Also, it is important to involve the integration of more than learning methods in medical learning to improve the process of medical diagnostic imaging and their benefits and limitations, recent advancements and development are discussed by reviewing the existing secondary sources. This study reviews papers published from 2015 (early publications on breast cancer) to 2021. This paper is a review of the latest works and techniques have done in the field with the future trends and problems in breast cancer categorization and diagnosis.

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Section: Deep Belief Network (Dbns)mentioning

confidence: 99%

Comprehensive Study for Breast Cancer Using Deep Learning and Traditional Machine Learning

2022

ZJPAS

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“…If the positions change, even a small amount for any reason, the feature maps will be different. To overcome this problem, the downsampling process must be done at the output of every convolutional layer [18]. With convolutional layers, downsampling can be done by changing the convolution's phase across the image.…”

Section: Pooling Layermentioning

confidence: 99%

“…ey show that performance assessment in diagnosis is carried out on two datasets of mammographic mass such as DDSM-400 and CBIS-DDSM, with variations in the accuracy of the corresponding segmentation maps of ground truth. A computer-aided diagnosis (CAD) system was applied by Malathi et al [18] for mammograms to allow initial identification, examination, and treatment of breast cancer. ey discussed exploring a breast CAD architecture focused on characteristic fusion through deep learning of the CNN.…”

Section: Introductionmentioning

confidence: 99%

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Boosting Breast Cancer Detection Using Convolutional Neural Network

Alanazi

Kamruzzaman

Sarker

et al. 2021

Journal of Healthcare Engineering

143

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Breast cancer forms in breast cells and is considered as a very common type of cancer in women. Breast cancer is also a very life-threatening disease of women after lung cancer. A convolutional neural network (CNN) method is proposed in this study to boost the automatic identification of breast cancer by analyzing hostile ductal carcinoma tissue zones in whole-slide images (WSIs). The paper investigates the proposed system that uses various convolutional neural network (CNN) architectures to automatically detect breast cancer, comparing the results with those from machine learning (ML) algorithms. All architectures were guided by a big dataset of about 275,000, 50 × 50-pixel RGB image patches. Validation tests were done for quantitative results using the performance measures for every methodology. The proposed system is found to be successful, achieving results with 87% accuracy, which could reduce human mistakes in the diagnosis process. Moreover, our proposed system achieves accuracy higher than the 78% accuracy of machine learning (ML) algorithms. The proposed system therefore improves accuracy by 9% above results from machine learning (ML) algorithms.

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“…The suggested procedure was tested using 1000 ROIs derived from the DDSM Dataset for segmented images, the quantitative evaluation, based on the area overlap measure AOM, yielded a mean of 0.52 ± 0.20 [6]. F. Liu, et al in [13].…”

Section: Introductionmentioning

confidence: 99%

Breast Tumor Detection Via Active Contour Technique

Radhi¹,

Kamil²

2021

IJIES

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The task of segmenting breast tumours in mammograms is very difficult, as its difficulty lies in the lack of contrast between the tumour and the surrounding breast tissue, especially when dealing with small tumours that are not clear boundaries and hidden under the tissues. Segmentation algorithms often lose the path of tumor boundaries in an attempt to determine the position of them. Active contours are used widely for segmentation as a high-level technology for boundary recognition. The main aim to create a clear contrast between the tumour and the normal breast region. In this study, two approaches to active contour are applied: snakes and level sets. The proposed methods were applied to all abnormal mammogram images taken from mini-MIAS database. The first approach showed a weakness in the segmentation of this type of image, while the other approach was able to segment all the mammogram's tumours. The Chan-Vese method was the most superior of all the active contour segmentation methods. The proposed models were tested in two ways, the first is statistical the best result was for the Chan-Vese method and it came as follows (90%,95% 98%, 97%, 97%) for Jaccard, Dice, PF-Score, Precision, and Sensitivity respectively. And the other is based on the segmented region's characteristics, Chan-Vese was able to accurately determine the location and shape of the tumor. The proposed Chan-Vese approach is appropriate in adopting computer assisted detection systems to predict tumor boundaries and locations in mammography for its reliability and superior performance over other algorithms.

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Breast cancer detection using active contour and classification by deep belief network

Cited by 18 publications

References 9 publications

Comprehensive Study for Breast Cancer Using Deep Learning and Traditional Machine Learning

Comprehensive Study for Breast Cancer Using Deep Learning and Traditional Machine Learning

Boosting Breast Cancer Detection Using Convolutional Neural Network

Breast Tumor Detection Via Active Contour Technique

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