Feature selection and classifier performance in computer-aided diagnosis for breast ultrasound

Gómez, W.; Rodríguez, A García; Pereira, Wagner Coelho de Albuquerque; Infantosi, Antonio Fernando Catelli

doi:10.1109/cewit.2013.6713755

Cited by 10 publications

(9 citation statements)

References 18 publications

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“…In our work, as will be presented in results section, through the use of some regularization techniques, we improve the CNN generalization, achieving and AUC >0.95 in the test set. The results of the proposed CNN model outperform the results obtained with some traditional algorithms obtained in [20], in which the authors achieved very satisfactory results using the same database, and the performance of some pre-trained networks (containing much more complexity) using transfer learning.…”

Section: Literature Reviewmentioning

confidence: 64%

“…Although we focused on papers using automatic feature selection, it is of particular interest to note the results yielded by [20], which utilized the same data set used in our work. The authors proposed a feature selection technique based on mutual information technique and a statistical test for breast tumor classification in ultrasound images.…”

Section: Literature Reviewmentioning

confidence: 99%

“…One could argue that in lack of enough data, it is more convenient to use a more traditional machine learning algorithm such as SVM [22], as they are much faster to train and, in some cases, generalize better, such as the work in [20].…”

Section: Literature Reviewmentioning

confidence: 99%

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Breast Lesion Classification in Ultrasound Images Using Deep Convolutional Neural Network

et al. 2020

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In recent years, convolutional neural networks (CNNs) have found many applications in medical image analysis. Having enough labeled data, CNNs could be trained to learn image features and used for object localization, classification, and segmentation. Although there are many interests in building and improving automated systems for medical image analysis, lack of reliable and publicly available biomedical datasets makes such a task difficult. In this work, the effectiveness of CNNs for the classification of breast lesions in ultrasound (US) images will be studied. First, due to a limited number of training data, we use a custom-built CNN with a few hidden layers and apply regularization techniques to improve the performance. Second, we use transfer learning and adapt some pre-trained models for our dataset. The dataset used in this work consists of a limited number of cases, 641 in total, histopathologically categorized (413 benign and 228 malignant lesions). To assess how the results of our classifier generalize on our data set, a 5-fold crossvalidation were employed, where in each fold 80% of data were used for training and the 20% for testing. Accuracy and the area under the ROC curve (AUC) were used as the main performance metrics. Before applying any regularizations techniques, we achieved an overall accuracy of 85.98% for tumor classification, and the AUC equal to 0.94. After applying image augmentation and regularization, the accuracy and the AUC increased to 92.05% and 0.97, respectively. Using a pre-trained model, we achieved an overall accuracy of 87.07% and an AUC equal to 0.96. The obtained results demonstrated the effectiveness of our custom architecture for classification of tumors in this small US imaging dataset, surpassing some traditional learning algorithm based on manual feature selection.

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Section: Literature Reviewmentioning

confidence: 64%

Section: Literature Reviewmentioning

confidence: 99%

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Breast Lesion Classification in Ultrasound Images Using Deep Convolutional Neural Network

et al. 2020

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“…The traditional segmentation task in DM can be divided into four main classes: (i) threshold-based segmentation, (ii) region-based segmentation, (iii) pixel-based segmentation, and (iv) model-based segmentation [3,78]. Furthermore, US image segmentation includes several techniques: threshold-based, region-based, edge-based, water-based, active-contour-based, and neural-network-learning-based techniques [141,142].…”

Section: Image Segmentationmentioning

confidence: 99%

Deep-Learning-Based Computer-Aided Systems for Breast Cancer Imaging: A Critical Review

2020

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This paper provides a critical review of the literature on deep learning applications in breast tumor diagnosis using ultrasound and mammography images. It also summarizes recent advances in computer-aided diagnosis/detection (CAD) systems, which make use of new deep learning methods to automatically recognize breast images and improve the accuracy of diagnoses made by radiologists. This review is based upon published literature in the past decade (January 2010–January 2020), where we obtained around 250 research articles, and after an eligibility process, 59 articles were presented in more detail. The main findings in the classification process revealed that new DL-CAD methods are useful and effective screening tools for breast cancer, thus reducing the need for manual feature extraction. The breast tumor research community can utilize this survey as a basis for their current and future studies.

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“…In the last decade, a large number of segmentation methods have been developed for US images, for example, thresholding-based methods [ 16 – 18 ], clustering-based methods [ 19 – 23 ], watershed-based methods [ 24 – 27 ], graph-based methods [ 28 – 35 ], and active contour models [ 36 – 42 ]. Thresholding is one of the frequently used segmentation techniques for the monochrome image.…”

Section: Introductionmentioning

confidence: 99%

A Novel Segmentation Approach Combining Region- and Edge-Based Information for Ultrasound Images

Luo

Liu

Huang

et al. 2017

BioMed Research International

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Ultrasound imaging has become one of the most popular medical imaging modalities with numerous diagnostic applications. However, ultrasound (US) image segmentation, which is the essential process for further analysis, is a challenging task due to the poor image quality. In this paper, we propose a new segmentation scheme to combine both region- and edge-based information into the robust graph-based (RGB) segmentation method. The only interaction required is to select two diagonal points to determine a region of interest (ROI) on the original image. The ROI image is smoothed by a bilateral filter and then contrast-enhanced by histogram equalization. Then, the enhanced image is filtered by pyramid mean shift to improve homogeneity. With the optimization of particle swarm optimization (PSO) algorithm, the RGB segmentation method is performed to segment the filtered image. The segmentation results of our method have been compared with the corresponding results obtained by three existing approaches, and four metrics have been used to measure the segmentation performance. The experimental results show that the method achieves the best overall performance and gets the lowest ARE (10.77%), the second highest TPVF (85.34%), and the second lowest FPVF (4.48%).

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Feature selection and classifier performance in computer-aided diagnosis for breast ultrasound

Cited by 10 publications

References 18 publications

Breast Lesion Classification in Ultrasound Images Using Deep Convolutional Neural Network

Breast Lesion Classification in Ultrasound Images Using Deep Convolutional Neural Network

Deep-Learning-Based Computer-Aided Systems for Breast Cancer Imaging: A Critical Review

A Novel Segmentation Approach Combining Region- and Edge-Based Information for Ultrasound Images

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