Automated Detection of Benign and Malignant in Breast Histopathology Images

Baker, Qanita Bani; Zaitoun, Toqa' Abu; Banat, Sajda; Eaydat, Eman; Alsmirat, Mohammad A.

doi:10.1109/aiccsa.2018.8612799

Cited by 9 publications

(4 citation statements)

References 23 publications

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“…The architect proposed in Reference 31 adopts a new combination of K‐means clustering and watershed algorithm to carry out segmentation, feature extraction, and subsequent classification. Xia et al 32 used the traditional CNN network for training.…”

Section: Experiments and Resultsmentioning

confidence: 99%

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Improved heterogeneous data fusion and multi‐scale feature selection method for lung cancer subtype classification

Zhang

Zhao

Qiang

et al. 2021

Concurrency and Computation

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Summary The diagnosis of the disease requires a variety of data and indicators. In order to make the computer perform intelligent computation and diagnosis from a doctor's perspective, complementary information between different modal data needs to be taken into account. Meanwhile, the redundancy features with key information should be selected in order to reduce the complexity of calculation. In this study, an adaptive dynamic loss function is proposed to weight different scales in the multi‐scale expansion network of pathological images according to the doctor's diagnosis process. And an ant colony algorithm based on maximum information coefficient correlation was designed for unsupervised feature selection of fusion features combined image feature and patient differential genes. Experimental results show that the addition of pathological image information and genetic information plays an important role in the classification of lung cancer subtypes. Compared with other feature selection methods, the proposed algorithm can quickly converge. Combining pathological image and gene expression matrix for cancer diagnosis can improve the diagnostic accuracy of specific patients, with an accuracy of 95.62 and AUC achieves 0.897. The proposed method has high effectiveness and superior performance in the classification of lung cancer.

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Section: Experiments and Resultsmentioning

confidence: 99%

“…Then the image features can be used to correlate with the genetic characteristics of the tumor 24–27 . There have been many studies on the diagnosis of cancer using pathological imaging and computer technology 28–32 . Liu et al 33 used Inception architecture to classify tumor and segment cancer regions in pathological images.…”

Section: Introductionmentioning

confidence: 99%

Improved heterogeneous data fusion and multi‐scale feature selection method for lung cancer subtype classification

Zhang

Zhao

Qiang

et al. 2021

Concurrency and Computation

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“…Subsequently, KNN algorithm was used for the classification of the images. A few studies such as [3,25] and [10] relied upon clustering algorithms such as K-Means for the detection of potential regions of interest.…”

Section: Traditional Methodsmentioning

confidence: 99%

Region-based feature enhancement using channel-wise attention for classification of breast histopathological images

Rashmi

Prasad

Udupa

2022

Neural Comput & Applic

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Breast histopathological image analysis at 400x magnification is essential for the determination of malignant breast tumours. But manual analysis of these images is tedious, subjective, error-prone and requires domain knowledge. To this end, computer-aided tools are gaining much attention in the recent past as it aids pathologists and save time. Furthermore, advances in computational power have leveraged the usage of computer tools. Yet, usage of computer-aided tools to analyse these images is challenging due to various reasons such as heterogeneity of malignant tumours, colour variations and presence of artefacts. Moreover, these images are captured at high resolutions which pose a major challenge to designing deep learning models as it demands high computational requirements. In this context, the present work proposes a new approach to efficiently and effectively extract features from these high-resolution images. In addition, at 400x magnification, the characteristics and structure of nuclei play a prominent role in the decision of malignancy. In this regard, the study introduces a novel CNN architecture called as CWA-Net that uses a colour channel attention module to enhance the features of the potential regions of interest such as nuclei. The developed model is qualitatively and quantitatively evaluated on private and public datasets and achieved an accuracy of 0.95% and 0.96%, respectively. The experimental evaluation demonstrates that the proposed method outperforms state-of-the-art methods on both datasets.

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“…Contudo, na maioria dos estudos, a classificaçãoé realizada sem a etapa de feature selection. A classificaçãoé realizada utilizando técnicas de machine learning, como k-Nearest Neighbors (kNN) [Khan et al 2015], Support Vector Machines [Tashk et al 2015], ou Decision Trees [Baker et al 2018].…”

Section: Trabalhos Relacionadosunclassified

Análise comparativa da influência dos espaços de cores na segmentação multi-classe de Whole Slide Imaging do câncer de mama utilizando deep learning

Zeiser¹,

Costa²,

Ramos³

et al. 2020

Anais Principais Do Simpósio Brasileiro De Computação Aplicada À Saúde (SBCAS 2020)

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O diagnóstico em estágios iniciais normalmente resulta em um prognóstico melhor nos casos de câncer. Atualmente, a análise histopatológica e o padrão ouro para o diagnóstico, estadiamento e definição do tratamento de neoplasias de mama. Contudo, a técnica possui algumas restrições que dificultam o processo de análise pelo patologista, como diferentes protocolos de coloração, variações de coloração nas lâminas, e sobreposição de tecidos. Assim, a representação computacional das cores pode exercer uma influência significativa no comportamento dos modelos convolucionais. Desta forma, este estudo propõe uma análise comparativa da influência de quatro espaços de cores (RGB, HSV, YCrCb e LAB) para a segmentação multi-classe de Whole Slide Imaging (WSI) do câncer de mama. A metodologia proposta e composta por uma etapa de pré-processamento das WSI, data augmentation e multi- segmentação utilizando a arquitetura convolucional U-Net com uma ResNet-50 pré-treinada como codificador. Os resultados obtidos demonstram que o espaço de cores HSV apresentou de maneira geral índices melhores para a segmentação das WSI utilizando a metodologia proposta neste estudo.

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Automated Detection of Benign and Malignant in Breast Histopathology Images

Cited by 9 publications

References 23 publications

Improved heterogeneous data fusion and multi‐scale feature selection method for lung cancer subtype classification

Improved heterogeneous data fusion and multi‐scale feature selection method for lung cancer subtype classification

Region-based feature enhancement using channel-wise attention for classification of breast histopathological images

Análise comparativa da influência dos espaços de cores na segmentação multi-classe de Whole Slide Imaging do câncer de mama utilizando deep learning

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