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Breast cancer diagnosis from mammographic images using optimized feature selection and neural network architecture
International Journal of Imaging Systems and Technology ( IF 3.0 ) Pub Date : 2020-08-09 , DOI: 10.1002/ima.22467
Ekta Shivhare 1 , Vineeta (Nigam) Saxena 1
Affiliation  

Breast cancer is one of the deadly diseases in women that have raised the mortality rate of women. An accurate and early detection of breast cancer using mammogram images is still a complex task. Hence, this article proposes a novel breast cancer detection model, which included five major phases: (a) preprocessing, (b) segmentation, (c) feature extraction, (d) feature selection, and (e) classification. The input mammogram image is initially preprocessed using contrast limited adaptive histogram equalization (CLAHE) and median filtering. The preprocessed image is then subjected to segmentation via the region growing algorithm. Subsequently, geometric features, texture features and gradient features are extracted from the segmented image. Since the length of the feature vector is large, it is essential to select the optimal features. Here, the selection of optimal features is done by a hybrid optimization algorithm. Once the optimal features are selected, they are subjected to the classification process involving the neural network (NN) classifier. As a novelty, the weight of NN is selected optimally to enhance the accuracy of diagnosis (benign and malignant). The optimal feature selection as well as the weight optimization of NN is accomplished by merging the Lion algorithm (LA) and particle swarm optimization (PSO), named as velocity updated lion algorithm (VU‐LA). Finally, a performance‐based evaluation is carried out between VU‐LA and the existing models like, whale optimization algorithm (WOA), gray wolf optimization (GWO), firefly (FF), PSO, and LA.

中文翻译:

使用优化的特征选择和神经网络架构从乳腺X线照片诊断乳腺癌

乳腺癌是增加女性死亡率的女性致命疾病之一。使用乳房X线照片准确,早期地检测乳腺癌仍然是一项复杂的任务。因此,本文提出了一种新颖的乳腺癌检测模型,该模型包括五个主要阶段:(a)预处理,(b)分割,(c)特征提取,(d)特征选择和(e)分类。最初使用对比受限的自适应直方图均衡化(CLAHE)和中值滤波对输入的乳房X线照片进行预处理。然后,经过预处理的图像通过区域增长算法进行分割。随后,从分割的图像中提取几何特征,纹理特征和梯度特征。由于特征向量的长度较大,因此选择最佳特征至关重要。这里,最佳特征的选择是通过混合优化算法完成的。选择最佳特征后,将对其进行涉及神经网络(NN)分类器的分类过程。作为一种新颖性,可以最佳选择神经网络的权重以提高诊断的准确性(良性和恶性)。最优的特征选择以及NN的权重优化是通过将Lion算法(LA)和粒子群优化(PSO)合并而成的,该算法被称为速度更新狮子算法(VU‐LA)。最后,在VU‐LA与现有模型(如鲸鱼优化算法(WOA),灰太狼优化(GWO),萤火虫(FF),PSO和LA)之间进行了基于性能的评估。它们要经过涉及神经网络(NN)分类器的分类过程。作为一种新颖性,可以最佳选择神经网络的权重以提高诊断的准确性(良性和恶性)。最优的特征选择以及NN的权重优化是通过将Lion算法(LA)和粒子群优化(PSO)合并而成的,该算法被称为速度更新狮子算法(VU‐LA)。最后,在VU‐LA与现有模型(如鲸鱼优化算法(WOA),灰太狼优化(GWO),萤火虫(FF),PSO和LA)之间进行了基于性能的评估。它们要经过涉及神经网络(NN)分类器的分类过程。作为一种新颖性,可以最佳选择神经网络的权重以提高诊断的准确性(良性和恶性)。最优的特征选择以及NN的权重优化是通过将Lion算法(LA)和粒子群优化(PSO)合并而成的,该算法被称为速度更新狮子算法(VU‐LA)。最后,在VU‐LA与现有模型(如鲸鱼优化算法(WOA),灰太狼优化(GWO),萤火虫(FF),PSO和LA)之间进行了基于性能的评估。最优的特征选择以及NN的权重优化是通过将Lion算法(LA)和粒子群优化(PSO)合并而成的,该算法被称为速度更新狮子算法(VU‐LA)。最后,在VU‐LA与现有模型(如鲸鱼优化算法(WOA),灰太狼优化(GWO),萤火虫(FF),PSO和LA)之间进行了基于性能的评估。最优的特征选择以及NN的权重优化是通过将Lion算法(LA)和粒子群优化(PSO)合并而成的,该算法被称为速度更新狮子算法(VU‐LA)。最后,在VU‐LA与现有模型(如鲸鱼优化算法(WOA),灰太狼优化(GWO),萤火虫(FF),PSO和LA)之间进行了基于性能的评估。
更新日期:2020-08-09
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