Convolutional Neural Network for Classification of Histopathology Images for Breast Cancer Detection

Narayanan, Barath Narayanan; Krishnaraja, Vignesh; Ali, Redha

doi:10.1109/naecon46414.2019.9058279

Cited by 19 publications

(18 citation statements)

References 16 publications

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“…With the aging of the population, the number of prostate cancer patients is increasing year by year, and the number of people who need biopsy is also increasing; the observation range of CAD tools is all areas of the slice, and the advantage of CAD tools is to avoid missed inspections caused by manual observation; and CAD tools are only compatible with internal algorithms [ 15 ]. It has nothing to do with labor intensity and time and can reuse computer resources to provide reproducible results, which can greatly improve the efficiency of diagnosis and treatment and ease the tension between doctors and patients [ 16 , 17 ].…”

Section: Introductionmentioning

confidence: 99%

Identification and Classification of Prostate Cancer Identification and Classification Based on Improved Convolution Neural Network

Tyagi

Tyagi²,

Choudhury³

et al. 2022

BioMed Research International

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Prostate cancer is one of the most common cancers in men worldwide, second only to lung cancer. The most common method used in diagnosing prostate cancer is the microscopic observation of stained biopsies by a pathologist and the Gleason score of the tissue microarray images. However, scoring prostate cancer tissue microarrays by pathologists using Gleason mode under many tissue microarray images is time-consuming, susceptible to subjective factors between different observers, and has low reproducibility. We have used the two most common technologies, deep learning, and computer vision, in this research, as the development of deep learning and computer vision has made pathology computer-aided diagnosis systems more objective and repeatable. Furthermore, the U-Net network, which is used in our study, is the most extensively used network in medical image segmentation. Unlike the classifiers used in previous studies, a region segmentation model based on an improved U-Net network is proposed in our research, which fuses deep and shallow layers through densely connected blocks. At the same time, the features of each scale are supervised. As an outcome of the research, the network parameters can be reduced, the computational efficiency can be improved, and the method’s effectiveness is verified on a fully annotated dataset.

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Section: Introductionmentioning

confidence: 99%

Identification and Classification of Prostate Cancer Identification and Classification Based on Improved Convolution Neural Network

Tyagi

Tyagi²,

Choudhury³

et al. 2022

BioMed Research International

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show abstract

“…They achieved an accuracy of about 85% in their work. Similar to this work, Ali et al [ 18 ] proposed a neural architecture with a color constancy technique and achieved an accuracy of 93.5%. Their work also involved histogram equalization but its accuracy was not that good.…”

Section: Related Workmentioning

confidence: 62%

ABCanDroid: A Cloud Integrated Android App for Noninvasive Early Breast Cancer Detection Using Transfer Learning

Chowdhury

Das²,

Dey

et al. 2022

Sensors

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Many patients affected by breast cancer die every year because of improper diagnosis and treatment. In recent years, applications of deep learning algorithms in the field of breast cancer detection have proved to be quite efficient. However, the application of such techniques has a lot of scope for improvement. Major works have been done in this field, however it can be made more efficient by the use of transfer learning to get impressive results. In the proposed approach, Convolutional Neural Network (CNN) is complemented with Transfer Learning for increasing the efficiency and accuracy of early detection of breast cancer for better diagnosis. The thought process involved using a pre-trained model, which already had some weights assigned rather than building the complete model from scratch. This paper mainly focuses on ResNet101 based Transfer Learning Model paired with the ImageNet dataset. The proposed framework provided us with an accuracy of 99.58%. Extensive experiments and tuning of hyperparameters have been performed to acquire the best possible results in terms of classification. The proposed frameworks aims to be an efficient tool for all doctors and society as a whole and help the user in early detection of breast cancer.

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“…Besides BreaKHis, other images were also applied. All of these breast cancer databases have similar characteristics, for example, using the Wisconsin Original Dataset [ 10 ], the University of Michigan and University of British Columbia Virtual Slidebox [ 19 ] or other individual databases [ 20 , 21 , 22 ]. The different magnifications of images and the multi-resolution databases are essential to make the models more robust and generalizable [ 23 ].…”

Section: Discussionmentioning

confidence: 99%

Breast Tumor Tissue Segmentation with Area-Based Annotation Using Convolutional Neural Network

et al. 2022

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In this paper, we propose a novel approach to segment tumor and normal regions in human breast tissues. Cancer is the second most common cause of death in our society; every eighth woman will be diagnosed with breast cancer in her life. Histological diagnosis is key in the process where oncotherapy is administered. Due to the time-consuming analysis and the lack of specialists alike, obtaining a timely diagnosis is often a difficult process in healthcare institutions, so there is an urgent need for improvement in diagnostics. To reduce costs and speed up the process, an automated algorithm could aid routine diagnostics. We propose an area-based annotation approach generalized by a new rule template to accurately solve high-resolution biological segmentation tasks in a time-efficient way. These algorithm and implementation rules provide an alternative solution for pathologists to make decisions as accurate as manually. This research is based on an individual database from Semmelweis University, containing 291 high-resolution, bright field microscopy breast tumor tissue images. A total of 70% of the 128 × 128-pixel resolution images (206,174 patches) were used for training a convolutional neural network to learn the features of normal and tumor tissue samples. The evaluation of the small regions results in high-resolution histopathological image segmentation; the optimal parameters were calculated on the validation dataset (29 images, 10%), considering the accuracy and time factor as well. The algorithm was tested on the test dataset (61 images, 20%), reaching a 99.10% f1 score on pixel level evaluation within 3 min on average. Besides the quantitative analyses, the system’s accuracy was measured qualitatively by a histopathologist, who confirmed that the algorithm was also accurate in regions not annotated before.

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Convolutional Neural Network for Classification of Histopathology Images for Breast Cancer Detection

Cited by 19 publications

References 16 publications

Identification and Classification of Prostate Cancer Identification and Classification Based on Improved Convolution Neural Network

Identification and Classification of Prostate Cancer Identification and Classification Based on Improved Convolution Neural Network

ABCanDroid: A Cloud Integrated Android App for Noninvasive Early Breast Cancer Detection Using Transfer Learning

Breast Tumor Tissue Segmentation with Area-Based Annotation Using Convolutional Neural Network

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