Breast sentinel lymph node cancer detection from mammographic images based on quantum wavelet transform and an atrous pyramid convolutional neural network

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dc.authorid0000-0002-2023-7326en_US
dc.authorid0000-0001-5988-8882en_US
dc.contributor.authorQasim, Mohammed N.
dc.contributor.authorMohammed, Tareq Abed
dc.contributor.authorBayat, Oğuz
dc.date.accessioned2022-10-21T13:59:39Z
dc.date.available2022-10-21T13:59:39Z
dc.date.issued2022en_US
dc.departmentEnstitüler, Lisansüstü Eğitim Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalıen_US
dc.description.abstractThis study proposes an optimal approach to reduce noise in mammographic images and to identify salt-and-pepper, Gaussian, Poisson, and impact noises to determine the exact mass detection operation after these noise reductions. It therefore offers a method for noise reduction operations called quantum wavelet transform filtering and a method for precision mass segmentation called the image morphological operations in mammographic images based on the classification with an atrous pyramid convolutional neural network (APCNN) as a deep learning model. The hybrid approach called a QWT-APCNN is evaluated in terms of criteria compared with previous methods such as peak signal-to-noise ratio (PSNR) and mean-squared error (MSE) in noise reduction and accuracy of detection for mass area recognition. The proposed method presents more performance of noise reduction and segmentation in comparison with state-of-the-art methods. In this paper, we used the APCNN based on the convolutional neural network (CNN) as a new deep learning method, which is able to extract features and perform classification simultaneously, but it is intended as far as possible, empirically for the purpose of this research to be able to determine breast cancer and then identify the exact area of the masses and then classify them according to benign, malignant, and suspicious classes. The obtained results presented that the proposed approach has better performance than others based on some evaluation criteria such as accuracy with 98.57%, sensitivity with 90%, specificity with 85%, and also ROC and AUC with a rate of 86.77.en_US
dc.identifier.citationQasim, M. N., Mohammed, T. A., Bayat, O. (2022). Breast sentinel lymph node cancer detection from mammographic images based on quantum wavelet transform and an atrous pyramid convolutional neural network. Scientific Programming, 2022.en_US
dc.identifier.issn1058-9244
dc.identifier.issn1875-919X
dc.identifier.scopus2-s2.0-85139563348
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://hdl.handle.net/20.500.12939/2982
dc.identifier.volume2022en_US
dc.identifier.wosWOS:000868595000004
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorQasim, Mohammed N.
dc.institutionauthorBayat, Oğuz
dc.language.isoen
dc.publisherHindawi Limiteden_US
dc.relation.ispartofScientific Programming
dc.relation.isversionof10.1155/2022/1887613en_US
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - İdari Personel ve Öğrencien_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectConvolutionen_US
dc.subjectConvolutional Neural Networksen_US
dc.subjectDeep Learningen_US
dc.subjectDiseasesen_US
dc.subjectImage Qualityen_US
dc.subjectImage Segmentationen_US
dc.subjectLearning Systemsen_US
dc.subjectMammographyen_US
dc.subjectMathematical Morphologyen_US
dc.subjectMean Square Erroren_US
dc.subjectSalt and Pepper Noiseen_US
dc.subjectSignal to Noise Ratioen_US
dc.subjectX Ray Screensen_US
dc.subjectWavelet Transformsen_US
dc.titleBreast sentinel lymph node cancer detection from mammographic images based on quantum wavelet transform and an atrous pyramid convolutional neural network
dc.typeArticle

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