Applying cuckoo search based algorithm and hybrid based neural classifier for breast cancer detection using ultrasound images

Autor: Stafford Michahial, Bindu A. Thomas
Rok vydání: 2019
Předmět:
Zdroj: Evolutionary Intelligence. 15:989-1006
ISSN: 1864-5917
1864-5909
DOI: 10.1007/s12065-019-00268-9
Popis: Ultrasound examination is one of the most convenient and appropriate processes used for the diagnosis of tumors that make use of ultrasound images. Ultrasound imaging is a noninvasive modality utilized commonly for the detection of breast cancer, which is a common and dangerous cancer found in women. This paper proposes an approach for the detection of breast cancer using ultrasound images using MKF-cuckoo search (MKF-CS) algorithm and hybrid based neural (H-BN) classifier. In pre-processing, the input images to be diagnosed are pre-processed by ROI extraction using a novel algorithm, four way search. The pre-processed image is allowed to perform segmentation using MKF-CS algorithm. The key features, such as mean, variance, standard deviation, and so on, are extracted in feature extraction and are fed to the proposed H-BN classifier. Based on the training data, H-BN classifier classifies the data into benign or malignant tumor classes, for the detection of breast cancer. To evaluate the performance of the proposed MKFCS-HBN approach, three metrics, such as accuracy, sensitivity, and specificity, are utilized. The experimental results show that MKFCS-HBN could attain the maximum performance with an accuracy of 0.8889, the sensitivity of 1, and specificity of 0.85 and thus, prove its effectiveness.
Databáze: OpenAIRE
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