Investigation of wave atom transform by using the classification of mammograms

Gedik, Nebi; Atasoy, Ayten; Sevim, Yusuf

doi:10.1016/j.asoc.2016.03.003

Cited by 4 publications

(1 citation statement)

References 13 publications

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“…This section includes experiments that led to the results of applying simple voting method on the three classifiers shown in the The dataset used in this study is Mammographic Image Analysis Society (MIAS) [10]. It has a total of 119 instances of breast cancer patients with each, either having malignant or benign type of cancer, this data set used by many latest related studies [11][12][13].…”

Section: Multi Classifier Voting Algorithmmentioning

confidence: 99%

An Enhancement of Multi Classifiers Voting Method for Mammogram Image based on Image Histogram Equalization

Ibrahim

Ahmed

Azizah³

et al. 2018

IJIE

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Breast cancer is one the most curable cancer types if it can be diagnosed early. Research efforts have reported with increasing confirmation that the computation methods have greater accurate diagnosis ability. An enhancement of multi classifiers voting technique based on histogram equalization as a preprocessing stage proposed in this paper. The methodology is based on five phases starting by mammogram images collection, preprocessing (histogram equalization and image cropping based region of interest (ROI)), features extracting, classification and last evaluating the classification results. An experimental conducted on different training-testing partitions of the dataset. The numerical results demonstrate that the proposed scheme achieves an accuracy rate of 81.25% and outperformed the accuracy of voting method without using histogram equalization.

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Section: Multi Classifier Voting Algorithmmentioning

confidence: 99%