Mammography image quality evaluation in breast cancer screening: The Saudi experience

Albeshan, Salman M.; Alashban, Yazeed; Tahan, Fatina M. Al; Al-enezi, Salma; Alnaimy, Noor; Shubayr, Nasser; Eliraqi, Fatma

doi:10.1016/j.jrras.2022.100467

Cited by 3 publications

(2 citation statements)

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“…In the past works, computer processors that combine large memory and rapid buses have covered a sensible and inculcating path to the medical image. Machine learning algorithms are utilized to remove the above‐mentioned characteristics of images 20 …”

Section: Introductionmentioning

confidence: 99%

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Krill herd optimization algorithm with deep convolutional neural network fostered breast cancer classification using mammogram images

Bai

Krish

2023

Concurrency and Computation

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In this paper proposes a Krill Herd Optimization algorithm with Deep Convolutional neural network fostered Breast Cancer Classification using Mammogram Images (BC-APPDRC-DCNN-KHO). Here, the input images are taken from Real time and MAMMOSET datasets. These images are pre-processed using Altered Phase Preserving Dynamic Range Compression (APPDRC) technique. This APPDRC is applied for preserving local features, compressing dynamic range of images, and enhancing the speckle noise filtering, these are all necessary for better boundary detection. Then, the Pre-processed images are classified using Deep Convolutional neural network (DCNN). The DCNN weight parameters are optimized based on Krill Herd Optimization algorithm. The Proposed BCC-DCNN-KHO-MI method classifies the input breast cancer imageries into 3 categories: benign, malignant, and normal. The pro-

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Section: Introductionmentioning

confidence: 99%

“…Machine learning algorithms are utilized to remove the above-mentioned characteristics of images. 20…”

mentioning

confidence: 99%