Determining and classifying the region of interest in ultrasonic images of the breast using neural networks

Buller, Danuta; Buller, Andrzej; Innocent, Peter R.; Pawlak, Waldemar

doi:10.1016/0933-3657(95)00020-8

Cited by 24 publications

(4 citation statements)

References 17 publications

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“…ANNs have been used in a great number of diagnostic decision support systems for medical applications, and they have demonstrated good predictive power (Buller, Buller, Innocent, & Pawlak, 1996;Verma & Zakos, 2001;Zarkogianni, Vazeou, Mougiakakou, Prountzou, & Nikita, 2011;Zarkogianni et al, 2015b). Ensembles of ANNs (EANN) can improve both the generalization abilities and the performance of an individual ANN, by compensating with each other the errors produced by each ANN (Sharkey, 1996).…”

Section: Introductionmentioning

confidence: 99%

Comparative assessment of statistical and machine learning techniques towards estimating the risk of developing type 2 diabetes and cardiovascular complications

et al. 2017

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The aim of the present study is to comparatively assess the performance of different machine learning and statistical techniques with regard to their ability to estimate the risk of developing type 2 diabetes mellitus (Case 1) and cardiovascular disease complications (Case 2). This is the first work investigating the application of ensembles of artificial neural networks (EANN) towards producing the 5‐year risk of developing type 2 diabetes mellitus and cardiovascular disease as a long‐term diabetes complication. The performance of the proposed models has been comparatively assessed with the performance obtained by applying logistic regression, Bayesian‐based approaches, and decision trees. The models' discrimination and calibration have been evaluated using the classification accuracy (ACC), the area under the curve (AUC) criterion, and the Hosmer–Lemeshow goodness of fit test. The obtained results demonstrate the superiority of the proposed models (EANN) over the other models. In Case 1, EANN with different topologies has achieved high discrimination and good calibration performance (ACC = 80.20%, AUC = 0.849, p value = .886). In Case 2, EANN based on bagging has resulted in good discrimination and calibration performance (ACC = 92.86%, AUC = 0.739, p value = .755).

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Section: Introductionmentioning

confidence: 99%

Comparative assessment of statistical and machine learning techniques towards estimating the risk of developing type 2 diabetes and cardiovascular complications

et al. 2017

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“…We will first look into the capability of NN in determining and recognizing a region where malignant and benign lesions can be found. Buller [21] was one of the first who used neural network in breast cancer detection for ultrasound images. In his work, he separated the training process for benign and malignant cases by feeding the first system only with images containing benign lesion and the other with images containing only malignant lesion.…”

Section: Applications Of Annsmentioning

confidence: 99%

Artificial Neural Networks in Image Processing for Early Detection of Breast Cancer

Mehdy

Shair

et al. 2017

Computational and Mathematical Methods in Medicine

128

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Medical imaging techniques have widely been in use in the diagnosis and detection of breast cancer. The drawback of applying these techniques is the large time consumption in the manual diagnosis of each image pattern by a professional radiologist. Automated classifiers could substantially upgrade the diagnosis process, in terms of both accuracy and time requirement by distinguishing benign and malignant patterns automatically. Neural network (NN) plays an important role in this respect, especially in the application of breast cancer detection. Despite the large number of publications that describe the utilization of NN in various medical techniques, only a few reviews are available that guide the development of these algorithms to enhance the detection techniques with respect to specificity and sensitivity. The purpose of this review is to analyze the contents of recently published literature with special attention to techniques and states of the art of NN in medical imaging. We discuss the usage of NN in four different medical imaging applications to show that NN is not restricted to few areas of medicine. Types of NN used, along with the various types of feeding data, have been reviewed. We also address hybrid NN adaptation in breast cancer detection.

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“…The method of Buller et al(1996) is quite different from common methods, which try to automatically classify and locate malignant and benign breast lesions in US images. The original breast US images were first compressed by taking the average of a 3×3 pixel window.…”

Section: Ultrasound Image Recognitionmentioning

confidence: 99%

“…Furthermore, there is no clear boundary between the genital organs and other adjacent structures of the fetus; thus it is impractical to obtain the closed contour of the genital organ and recognize the genital organ as an independent object. Using the characteristics of the genital organs and inspired by the research of Buller et al(1996), in this paper we construct a recognition and location algorithm for fetal genital organs based on the classification of the pixel, in the situation where no ROI can be predefined manually. Every pixel in the US images will be divided into two classes, in the fetal genital organ or out of the fetal genital organ.…”

Section: Overall Design Of the Algorithmmentioning

confidence: 99%

A fast automatic recognition and location algorithm for fetal genital organs in ultrasound images

Tang

Chen

2009

J. Zhejiang Univ. Sci. B

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Abstract:Severe sex ratio imbalance at birth is now becoming an important issue in several Asian countries. Its leading immediate cause is prenatal sex-selective abortion following illegal sex identification by ultrasound scanning. In this paper, a fast automatic recognition and location algorithm for fetal genital organs is proposed as an effective method to help prevent ultrasound technicians from unethically and illegally identifying the sex of the fetus. This automatic recognition algorithm can be divided into two stages. In the 'rough' stage, a few pixels in the image, which are likely to represent the genital organs, are automatically chosen as points of interest (POIs) according to certain salient characteristics of fetal genital organs. In the 'fine' stage, a specifically supervised learning framework, which fuses an effective feature data preprocessing mechanism into the multiple classifier architecture, is applied to every POI. The basic classifiers in the framework are selected from three widely used classifiers: radial basis function network, backpropagation network, and support vector machine. The classification results of all the POIs are then synthesized to determine whether the fetal genital organ is present in the image, and to locate the genital organ within the positive image. Experiments were designed and carried out based on an image dataset comprising 658 positive images (images with fetal genital organs) and 500 negative images (images without fetal genital organs). The experimental results showed true positive (TP) and true negative (TN) results from 80.5% (265 from 329) and 83.0% (415 from 500) of samples, respectively. The average computation time was 453 ms per image.

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Determining and classifying the region of interest in ultrasonic images of the breast using neural networks

Cited by 24 publications

References 17 publications

Comparative assessment of statistical and machine learning techniques towards estimating the risk of developing type 2 diabetes and cardiovascular complications

Comparative assessment of statistical and machine learning techniques towards estimating the risk of developing type 2 diabetes and cardiovascular complications

Artificial Neural Networks in Image Processing for Early Detection of Breast Cancer

A fast automatic recognition and location algorithm for fetal genital organs in ultrasound images

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