Imprint cytology‐based breast malignancy screening: an efficient nuclei segmentation technique

Saha, Monjoy; Arun, Indu; Agarwal, Sanjit; Ahmed, Rosina; Chatterjee, Sanjoy; Chakraborty, Chinmay

doi:10.1111/jmi.12595

Cited by 9 publications

(2 citation statements)

References 48 publications

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“…The proposed system can be extended to other types of diseases such as lung cancer, prostate cancer, and glioblastoma. Additionally, in this study (Saha et al, 2017), an effective nuclei—segmentation method has been developed for screening of breast malignancy on the basis of imprint cytology. This study focus on establishing the efficient and automated algorithm for segmentation by incorporating super pixels and Lagrange's interpolation to outline the overlapped nuclei in accordance with breast cells as malignant or normal.…”

Section: Review On Related Workmentioning

confidence: 99%

Detecting breast cancer using novel mask R‐CNN techniques

Jahangeer

Thambidurai

2022

Expert Systems

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Breast cancer is more common in women and the mortality rate also increases in recent days. Early detection of breast cancer is reduces the severity of the disease to some extent. Various image processing and classification techniques imposed on a particular image to detect and diagnose the breast cancer clearly. For early detection, an efficient methodology is needed. To reduce death rate, accurate discovery of the disease should be efficiently implemented. The mask R-CNN is used for segmentation to identify abnormalities and ensemble CNN is used to classify the benign and malignant tumour from the given mammographic breast cancer image. The input is fed into the enhanced fuzzy based median filter which is used to remove speckle noises which in turn increases the clarity of images. The noise-free images are fed into the proposed mask R-CNN for segmentation. It is an effective framework for segmentation of medical images. The classification process is then given into ensemble based CNN classifier for prediction of mammogram images. The ensemble based CNN is proposed for the classification of mammograms as benign and malignant. The proposed method focus on the optimization which is performed to minimize the memory requirements and running time while maintaining the high performance of the classifier. The performance metrics such as accuracy, precision, f-measure, and recall is evaluated to measure the efficacy of the proposed design. Using MIAS and DDSM dataset the performance metrics has been evaluated and compared with existing approaches. The results shows that the performance of the proposed method shows higher efficiency.

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Section: Review On Related Workmentioning

confidence: 99%

Detecting breast cancer using novel mask R‐CNN techniques

Jahangeer

Thambidurai

2022

Expert Systems

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“…Breast cancer is the most common cancer in women [1,2,3,4] and represents one of the leading causes of death in women [5]. In 2020, breast cancer occupied 12% of all human malignant tumor cases [6], and by 2040, this number is expected to rise to 46%.…”

Section: Introductionmentioning

confidence: 99%

HAHNet: A convolutional neural network for HER2 status classification of breast cancer

Wang

Zhu

Chen

et al. 2023

Preprint

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Background: Breast cancer is a major health problem for women. Human epidermal growth factor receptor-2 (HER2) is a very important diagnostic and prognostic factor for breast cancer, and HER2 status classification is essential for the development of treatment plans for breast cancer. Generally speaking, pathologists will adopt immunohistochemistry (IHC) to assess HER2 status, which requires additional economic costs. Furthermore, the manual assessment of HER2 status is time-consuming and error-prone. In recent years, deep learning has been widely used in medical field and has attained great achievements. However, the existing deep learning methods for HER2 status classification of conventional hematoxylin and eosin (H&E) stained images are not accurate enough. Results: To address these problems, a neural network model named HAHNet is proposed in this paper. HAHNet combines multi-scale features with attention mechanisms, which is able to directly classify HER2 status of H&E stained histological images of breast cancer. Typically, the HAHNet network mainly includes convolution preprocessing, attention mechanism, downsampling, and multi-scale feature extraction. The experimental results show that HAHNet outperforms other existing methods with regard to six metrics of Accuracy, Sensitivity, Precision, F-score, MCC, and AUC. Conclusions: Collectively, the above experiments demonstrate that our proposed HAHNet achieves high performance in classifying the HER2 status of breast cancer using only H&E stained samples, which can be used in case classification and helps to reduce the cost required for diagnosis.

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Superpixel/voxel medical image segmentation algorithm based on the regional interlinked value

Fang

Wang

2021

Pattern Anal Applic

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Imprint cytology‐based breast malignancy screening: an efficient nuclei segmentation technique

Cited by 9 publications

References 48 publications

Detecting breast cancer using novel mask R‐CNN techniques

Detecting breast cancer using novel mask R‐CNN techniques

HAHNet: A convolutional neural network for HER2 status classification of breast cancer

Superpixel/voxel medical image segmentation algorithm based on the regional interlinked value

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