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DeepBreastNet: A novel and robust approach for automated breast cancer detection from histopathological images

Demir Fatih

Biocybernetics and Biomedical Engineering

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2021

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Vol. 41, no. 3

1123--1139

EN

The analysis of histopathological images is the core way for detecting breast cancer, the most insidious type of cancer for women. Artificial intelligence-based applications are used as an effective and supportive tool for automated breast cancer detection. Especially, deep learning models are among the most popular approaches due to their high performances in classification problems of medical images. In this study, a novel and robust approach, based on the convolutional-LSTM (CLSTM) learning model, the pre-processing technique using marker-controlled watershed segmentation algorithm (MWSA), and the optimized SVM classifier, was proposed for detecting breast cancer automatically from histopathological images (HPIs). The CLSTM model trained on the BreakHis dataset, which is popular in the research community, composes of binary and eight-class classification tasks. The classification performance of the CLSTM model was significantly increased by using the processed HPIs with MWSA. For binary and eight-class classification tasks, the best scores were obtained by using the optimized SVM classifier with Bayesian optimization instead of the softmax classifier of the CLSTM model. The proposed approach, which provided very high performance for both classification tasks, was compared to the existing approaches using the BreakHis dataset.

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Breast cancer detection from histopathology images using modified residual neural networks

Gupta Varun, Vasudev Megha, Doegar Amit, Sambyal Nitigya

Biocybernetics and Biomedical Engineering

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2021

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Vol. 41, no. 4

1272--1287

EN

Breast cancer is one of the major causes of death among women worldwide. Efficient diagnosis of breast cancer in the early phases can reduce the associated morbidity and mortality and can provide a higher probability of full recovery. Computer-aided detection systems use computer technologies to detect abnormalities in clinical images which can assist medical professionals in a faster and more accurate diagnosis. In this paper, we propose a modified residual neural network-based method for breast cancer detection using histopathology images. The proposed approach provides good performance over varying magnification factors of 40X, 100X, 200X and 400X. The network obtains an average classification accuracy of 99.75%, precision of 99.18% and recall of 99.37% on BreakHis dataset with 40X magnification factor. The proposed work outperforms the existing methods and delivers state-of-the-art results on the benchmark breast cancer dataset.

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Variational Bayesian inversion for microwave breast imaging

Gharsalli L., Ayasso H., Duchêne B., Mohammad-Djafari A.

Computer Assisted Methods in Engineering and Science

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2014

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Vol. 21, no. 3-4

199--210

EN

Microwave imaging is considered as a nonlinear inverse scattering problem and tackled in a Bayesian estimation framework. The object under test (a breast affected by a tumor) is assumed to be composed of compact regions made of a restricted number of different homogeneous materials. This a priori knowledge is defined by a Gauss-Markov-Potts distribution. First, we express the joint posterior of all the unknowns; then, we present in detail the variational Bayesian approximation used to compute the estimators and reconstruct both permittivity and conductivity maps. This approximation consists of the best separable probability law that approximates the true posterior distribution in the Kullback-Leibler sense. This leads to an implicit parametric optimization scheme which is solved iteratively. Some preliminary results, obtained by applying the proposed method to synthetic data, are presented and compared with those obtained by means of the classical contrast source inversion method.