Wyniki wyszukiwania - Biblioteka Nauki

Ten serwis zostanie wyłączony 2025-02-11.

Nowa wersja platformy, zawierająca wyłącznie zasoby pełnotekstowe, jest już dostępna.
Przejdź na https://bibliotekanauki.pl

Ograniczanie wyników

Znaleziono wyników: 3

Liczba wyników na stronie

Wyniki wyszukiwania

Wyszukiwano:
w słowach kluczowych: breast cancer diagnosis

Sortuj według:

Ogranicz wyniki do:

Deep learning versus classical neural approach to mammogram recognition

100%

Kurek J. , Świderski B. , Osowski S. , Kruk M. , Barhoumi W.

nr 6

Morphological method for extraction of microcalcifications in mammograms for breast cancer diagnosis

86%

Nieniewski M.

tom Vol. 8, No. 3

427-448

The paper presents a new morphological method for extraction of microcalcifications in mammograms for breast cancer diagnosis. The proposed method is based on the use of the morphological detector together with morphological pyramid for detection of local irregularities of brightness in a wide range of size and shapes. The binary maps obtained from the pyramid indicate locations of the condidates for microcalcifications in the mammogram. Independently, the gray level reconstruction of the original mammogram is carried out in order to obtain the axact shape of h-domes, which depic regional maxima (hills) of brithness in the image. By thresholding the image of h-domes, one obtains a binary map of h-domes. Subsequently, a bimary reconstruction is carried out, in which the binary map of h-domes is used as a mask, and the map obtained from the pyramid after some modification is used as the marker. As a result of the reconstruction, the required map of microcalcifications is extracted. A number of tests of the proposed method on various mammograms are presented.

Deep learning versus classical neural approach to mammogram recognition

72%

Kurek J. , Świderski B. , Osowski S. , Kruk M. , Barhoumi W.

Bulletin of the Polish Academy of Sciences. Technical Sciences

2018

tom Vol. 66, nr 6

831--840

Automatic recognition of mammographic images in breast cancer is a complex issue due to the confusing appearance of some perfectly normal tissues which look like masses. The existing computer-aided systems suffer from non-satisfactory accuracy of cancer detection. This paper addresses this problem and proposes two alternative techniques of mammogram recognition: the application of a variety of methods for definition of numerical image descriptors in combination with an efficient SVM classifier (so-called classical approach) and application of deep learning in the form of convolutional neural networks, enhanced with additional transformations of input mammographic images. The key point of the first approach is defining the proper numerical image descriptors and selecting the set which is the most class discriminative. To achieve better performance of the classifier, many image descriptors were defined by means of applying different characterization of the images: Hilbert curve representation, Kolmogorov-Smirnov statistics, the maximum subregion principle, percolation theory, fractal texture descriptors as well as application of wavelet and wavelet packets. Thanks to them, better description of the basic image properties has been obtained. In the case of deep learning, the features are automatically extracted as part of convolutional neural network learning. To get better quality of results, additional representations of mammograms, in the form of nonnegative matrix factorization and the self-similarity principle, have been proposed. The methods applied were evaluated based on a large database composed of 10,168 regions of interest in mammographic images taken from the DDSM database. Experimental results prove the advantage of deep learning over traditional approach to image recognition. Our best average accuracy in recognizing abnormal cases (malignant plus benign versus healthy) was 85.83%, with sensitivity of 82.82%, specificity of 86.59% and AUC = 0.919. These results are among the best for this massive database.