Deep Learning Techniques for Breast Cancer Detection Using Medical Image Analysis

Selvathi, D.; Poornila, A. Aarthy

doi:10.1007/978-3-319-61316-1_8

Cited by 40 publications

(16 citation statements)

References 18 publications

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“…Deep convolutional neural networks (DCNNs) have significantly outperformed traditional methods in recent years, owing to the strong feature expression ability that can further improve the detection accuracy. With the development of deep learning, DCNN has been widely used in medical image detection [21][22][23]. ese CAD systems can be used to migrate between breast cancer and lung cancer.…”

Section: Introductionmentioning

confidence: 99%

Deep Convolutional Neural Networks-Based Automatic Breast Segmentation and Mass Detection in DCE-MRI

Han

Jiang

Pang

et al. 2020

Computational and Mathematical Methods in Medicine

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Breast segmentation and mass detection in medical images are important for diagnosis and treatment follow-up. Automation of these challenging tasks can assist radiologists by reducing the high manual workload of breast cancer analysis. In this paper, deep convolutional neural networks (DCNN) were employed for breast segmentation and mass detection in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). First, the region of the breasts was segmented from the remaining body parts by building a fully convolutional neural network based on U-Net++. Using the method of deep learning to extract the target area can help to reduce the interference external to the breast. Second, a faster region with convolutional neural network (Faster RCNN) was used for mass detection on segmented breast images. The dataset of DCE-MRI used in this study was obtained from 75 patients, and a 5-fold cross validation method was adopted. The statistical analysis of breast region segmentation was carried out by computing the Dice similarity coefficient (DSC), Jaccard coefficient, and segmentation sensitivity. For validation of breast mass detection, the sensitivity with the number of false positives per case was computed and analyzed. The Dice and Jaccard coefficients and the segmentation sensitivity value for breast region segmentation were 0.951, 0.908, and 0.948, respectively, which were better than those of the original U-Net algorithm, and the average sensitivity for mass detection achieved 0.874 with 3.4 false positives per case.

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Section: Introductionmentioning

confidence: 99%

Deep Convolutional Neural Networks-Based Automatic Breast Segmentation and Mass Detection in DCE-MRI

Han

Jiang

Pang

et al. 2020

Computational and Mathematical Methods in Medicine

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“…Finally, Support Vector Machine Classification (SVM) is used to classify benign and malignant cancers. Selvathi and Poornila [22] propose a general thresholding method for extracting chest bounds in images in which images are converted to binary using a fixed threshold value of 18. Each component with a significant number of pixels connected is considered as the chest area.…”

Section: Literature Reviewmentioning

confidence: 99%

Presentation of Novel Hybrid Algorithm for Detection and Classification of Breast Cancer Using Growth Region Method and Probabilistic Neural Network

Isfahani

Jannat-Dastjerdi

Eskandari

et al. 2021

Computational Intelligence and Neuroscience

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Mammography is a significant screening test for early detection of breast cancer, which increases the patient’s chances of complete recovery. In this paper, a clustering method is presented for the detection of breast cancer tumor locations and areas. To implement the clustering method, we used the growth region approach. This method detects similar pixels nearby. To find the best initial point for detection, it is essential to remove human interaction in clustering. Therefore, in this paper, the FCM-GA algorithm is used to find the best point for starting growth. Their results are compared with the manual selection method and Gaussian Mixture Model method for verification. The classification is performed to diagnose breast cancer type in two primary datasets of MIAS and BI-RADS using features of GLCM and probabilistic neural network (PNN). Results of clustering show that the presented FCM-GA method outperforms other methods. Moreover, the accuracy of the clustering method for FCM-GA is 94%, as the best approach used in this paper. Furthermore, the result shows that the PNN methods have high accuracy and sensitivity with the MIAS dataset.

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“…Auto encoder basically consists of encoder that followed the decoder, encoder usually transfer the input in the form of variables like x, y and decoder takes that input and try to get back all the original input. The main purpose of auto encoder is to learn from a big dataset, by training its network that ignore the irrelevant signals such as noise [58], [60], [61].…”

Section: A Auto Encodermentioning

confidence: 99%

“…A sparse auto encoder can handle the sparsity regularizer. Sparsity regularizer provides the sparsity of output from the hidden layer of neural network [58], [60], [61].…”

Section: B Sparse Auto Encodersmentioning

confidence: 99%

Prediction of Breast Cancer, Comparative Review of Machine Learning Techniques, and Their Analysis

et al. 2020

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Breast cancer is type of tumor that occurs in the tissues of the breast. It is most common type of cancer found in women around the world and it is among the leading causes of deaths in women. This paper presents the comparative analysis of machine learning, deep learning and data mining techniques being used for the prediction of breast cancer. Many researchers have put their efforts on breast cancer diagnoses and prognoses, every technique has different accuracy rate and it varies for different situations, tools and datasets being used. Our main focus is to comparatively analyze different existing Machine Learning and Data Mining techniques in order to find out the most appropriate method that will support the large dataset with good accuracy of prediction. The main purpose of this review is to highlight all the previous studies of machine learning algorithms that are being used for breast cancer prediction and this paper provides the all necessary information to the beginners who want to analyze the machine learning algorithms to gain the base of deep learning.

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Deep Learning Techniques for Breast Cancer Detection Using Medical Image Analysis

Cited by 40 publications

References 18 publications

Deep Convolutional Neural Networks-Based Automatic Breast Segmentation and Mass Detection in DCE-MRI

Deep Convolutional Neural Networks-Based Automatic Breast Segmentation and Mass Detection in DCE-MRI

Presentation of Novel Hybrid Algorithm for Detection and Classification of Breast Cancer Using Growth Region Method and Probabilistic Neural Network

Prediction of Breast Cancer, Comparative Review of Machine Learning Techniques, and Their Analysis

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