Dataset of breast ultrasound images

Al-Dhabyani, Walid; Gomaa, Mohammed; Khaled, Hussein M.; Fahmy, Aly A.

doi:10.1016/j.dib.2019.104863

Cited by 993 publications

(400 citation statements)

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“…Each lesion of the OASBUD dataset consists of two scans, one is longitudinal and other is transverse. A second dataset of BUS images collected at BHE [27] included 210 malignant and 437 benign lesions is also employed. The segmented part in the breast lesion exposes cancer affected portion.…”

Section: Resultsmentioning

confidence: 99%

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Identification of Breast Malignancy by Marker-Controlled Watershed Transformation and Hybrid Feature Set for Healthcare

et al. 2020

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Breast cancer is a highly prevalent disease in females that may lead to mortality in severe cases. The mortality can be subsided if breast cancer is diagnosed at an early stage. The focus of this study is to detect breast malignancy through computer-aided diagnosis (CADx). In the first phase of this work, Hilbert transform is employed to reconstruct B-mode images from the raw data followed by the marker-controlled watershed transformation to segment the lesion. The methods based only on texture analysis are quite sensitive to speckle noise and other artifacts. Therefore, a hybrid feature set is developed after the extraction of shape-based and texture features from the breast lesion. Decision tree, k-nearest neighbor (KNN), and ensemble decision tree model via random under-sampling with Boost (RUSBoost) are utilized to segregate the cancerous lesions from the benign ones. The proposed technique is tested on OASBUD (Open Access Series of Breast Ultrasonic Data) and breast ultrasound (BUS) images collected at Baheya Hospital Egypt (BHE). The OASBUD dataset contains raw ultrasound data obtained from 100 patients containing 52 malignant and 48 benign lesions. The dataset collected at BHE contains 210 malignant and 437 benign images. The proposed system achieved promising accuracy of 97% with confidence interval (CI) of 91.48% to 99.38% for OASBUD and 96.6% accuracy with CI of 94.90% to 97.86% for the BHE dataset using ensemble method.

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Section: Resultsmentioning

confidence: 99%

“…In this research work, a database called OASBUD [26] and BUS images collected at BHE [27] are used. The OASBUD dataset includes raw ultrasound data obtained from 100 patients with 52 malignant breast lesions and 48 benign lesions.…”

Section: Image Databasementioning

confidence: 99%

Identification of Breast Malignancy by Marker-Controlled Watershed Transformation and Hybrid Feature Set for Healthcare

et al. 2020

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“…In this research, we consider two publicly available breast ultrasound image datasets [28,29]. The two datasets are considered mainly for two reasons: (1) to increase the size of the dataset for the training purpose in order to avoid overfitting and biasness and (2) to consider three classes (benign, malignant and normal).…”

Section: Methodsmentioning

confidence: 99%

“…Therefore, all the images are transformed into gray color to fit into the model. The dataset in [ 29 ] contains 780 images, in which there are three categories: malignant, benign, and normal cases. The average image size of the images is 500 × 500 pixels.…”

Section: Methodsmentioning

confidence: 99%

Convolutional neural network-based models for diagnosis of breast cancer

Masud

Rashed

Hossain

2020

Neural Comput & Applic

102

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Breast cancer is the most prevailing cancer in the world and each year affecting millions of women. It is also the cause of largest number of deaths in women dying in cancers. During the last few years, researchers are proposing different convolutional neural network models in order to facilitate diagnostic process of breast cancer. Convolutional neural networks are showing promising results to classify cancers using image datasets. There is still a lack of standard models which can claim the best model because of unavailability of large datasets that can be used for models' training and validation. Hence, researchers are now focusing on leveraging the transfer learning approach using pre-trained models as feature extractors that are trained over millions of different images. With this motivation, this paper considers eight different fine-tuned pre-trained models to observe how these models classify breast cancers applying on ultrasound images. We also propose a shallow custom convolutional neural network that outperforms the pre-trained models with respect to different performance metrics. The proposed model shows 100% accuracy and achieves 1.0 AUC score, whereas the best pre-trained model shows 92% accuracy and 0.972 AUC score. In order to avoid biasness, the model is trained using the fivefold cross validation technique. Moreover, the model is faster in training than the pre-trained models and requires a small number of trainable parameters. The Grad-CAM heat map visualization technique also shows how perfectly the proposed model extracts important features to classify breast cancers.

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“…To further assess the developed model, breast ultrasound is used in the following experiment. Breast cancer is one of the most common causes of death among women worldwide [46]. The quality of the segmentation results of breast ultrasound images affects subsequent automated processing, e.g., computer-aided medical image analysis.…”

Section: B Comparisons With Popular Acmsmentioning

confidence: 99%