2021
DOI: 10.1007/978-3-030-73909-6_85
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Breast Lesions Detection Using FADHECAL and Multilevel Otsu Thresholding Segmentation in Digital Mammograms

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Cited by 3 publications
(3 citation statements)
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“…VGG net requires much more parameters to thoroughly evaluate its performance. In ( 30 , 31 ), the use of VGG16 was modified to classify microcalcification images into benign or malignant cases from the DDSM database and obtained accuracy of 94.3 and 87.0%, respectively. Study of ( 33 ) utilized AlexNet and managed to achieve an accuracy of 79.1% upon utilizing 10-fold cross validation technique with 300 epochs and learning rate of 0.01 based on 900 images from SYUCC and NAHSMU database.…”
Section: Resultsmentioning
confidence: 99%
See 1 more Smart Citation
“…VGG net requires much more parameters to thoroughly evaluate its performance. In ( 30 , 31 ), the use of VGG16 was modified to classify microcalcification images into benign or malignant cases from the DDSM database and obtained accuracy of 94.3 and 87.0%, respectively. Study of ( 33 ) utilized AlexNet and managed to achieve an accuracy of 79.1% upon utilizing 10-fold cross validation technique with 300 epochs and learning rate of 0.01 based on 900 images from SYUCC and NAHSMU database.…”
Section: Resultsmentioning
confidence: 99%
“…Otsu Segmentation Method and MorphologicalEx Method presented by ( 29 ) were utilized to remove the artifacts that may be present at the image. Otsu Segmentation Method works on grayscale images and involves global thresholding or local thresholding to classify pixels values ( 30 , 31 ). For instance, we denote mammogram image as function of G ( x, y ) and intensity value of I { I = 0, 1, 2, … I −1}.…”
Section: Methodsmentioning
confidence: 99%
“…Thus, it will lead to double reading and increased time for detecting the location of breast cancer. A study by Makandar A, Halali B (2016) showed the CAD system for detection of breast cancer using median filter dan Contrast Limited Adaptive Histogram Equalization (CLAHE) of enhancement process and Otsu thresholding segmentation [12]. This system still has limitations to detect breast cancer due to poor intensity images of enhanced digitised mammograms.…”
Section: Introductionmentioning
confidence: 99%