Grey Wolf optimization based breast cancer detection using 1D Convolution LSTM classifier

• Autorzy: Jebarani Esther P. , Umadevi N.

Identyfikatory

DOI

10.15199/48.2023.01.01

Warianty tytułu

PL

Wykrywanie raka piersi w oparciu o optymalizację Gray Wolf przy użyciu klasyfikatora 1D Convolution LSTM

• Języki publikacji: EN

Abstrakty

EN

Women are particularly vulnerable to breast cancer. Breast cancer diagnosis has benefited greatly from the utilization of ultrasound imaging. Breast UltraSound (BUS) image segmentation remains a difficult challenge due to low image quality. Furthermore, BUS image segmentation, as well as classification, is an important stage in the analysis process. Initially, the image associated with breast cancer is gathered from MIAS database. The gathered image undergoes pre-processing operation using the adaptive median filtering technique. Subsequently, the segmentation is performed in the pre-processed images using the hybrid method consisting of GMM and K-Means. These segmented images undergo the feature extraction steps further where the features are extracted by utilizing the Gray Level Co-occurrence Matrix (GLCM). Grey Wolf Optimization (GWO) selects the optimal features for further classification using a novel 1D Convolution LSTM. Here, the pooling layer of 1D CNN is replaced by the LSTM. The objective function behind the optimal feature selection and classification is the accuracy maximization. Finally, the novel One Dimensional Convolution Long Short Term Memory (1 DCLSTM) classifies the outcome into normal, benign, and malignant, respectively. The proposed method is compared with the other state of art methods related to this research.

PL

Kobiety są szczególnie narażone na raka piersi. Diagnostyka raka piersi bardzo skorzystała na wykorzystaniu obrazowania ultrasonograficznego. Segmentacja obrazu UltraSound (BUS) piersi pozostaje trudnym wyzwaniem ze względu na niską jakość obrazu. Ponadto segmentacja obrazu BUS, a także klasyfikacja, jest ważnym etapem procesu analizy. Początkowo obraz związany z rakiem piersi pozyskiwany jest z bazy MIAS. Zgromadzony obraz jest poddawany wstępnemu przetwarzaniu przy użyciu techniki adaptacyjnego filtrowania medianowego. Następnie na wstępnie przetworzonych obrazach przeprowadzana jest segmentacja metodą hybrydową składającą się z GMM i K-Means. Te podzielone na segmenty obrazy przechodzą kolejne etapy ekstrakcji cech, w których cechy są wyodrębniane przy użyciu macierzy współwystępowania poziomu szarości (GLCM). Optymalizacja Gray Wolf (GWO) wybiera optymalne funkcje do dalszej klasyfikacji przy użyciu nowatorskiego rozwiązania 1D Convolution LSTM. W tym przypadku warstwa łączenia 1D CNN zostaje zastąpiona przez LSTM. Funkcją celu stojącą za optymalnym doborem i klasyfikacją cech jest maksymalizacja dokładności. Wreszcie, powieść jednowymiarowa pamięć krótkoterminowa z konwolucją jednowymiarową (1 DCLSTM) klasyfikuje wynik odpowiednio na normalny, łagodny i złośliwy. Proponowana metoda jest porównywana z innymi nowoczesnymi metodami związanymi z tymi badaniami.

Słowa kluczowe

EN

breast cancer detection; classification; grey wolf optimization; optimal feature selection; long short term memory; one-dimensional convolution

PL

rak piersi; algorytm szarego wilka; diagnostyka

• Wydawca: Wydawnictwo SIGMA-NOT
• Czasopismo: Przegląd Elektrotechniczny
• Rocznik: 2023
• Tom: R. 99, nr 1
• Strony: 1--9
• Opis fizyczny: Bibliogr. 49 poz., rys., tab.

Twórcy

autor

Jebarani Esther P.

• Department of Computer Science & IT, Sri Jayendra Saraswathy Maha Vidyalaya College of Arts and Science, Coimbatore, India

• https://orcid.org/0000-0003-3685-2670

autor

Umadevi N.

• Department of Computer Science & IT, Sri Jayendra Saraswathy Maha Vidyalaya College of Arts and Science, Coimbatore, India

• https://orcid.org/0000-0001-9021-2637

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).