Detection of Breast Cancer from Mammogram Images Using Deep Transfer Learning

Das, Akalpita; Das, Himanish Shekhar; Barman, Utpal; Choudhury, Arijeet; Mazumdar, Sourav; Neog, Anupal

doi:10.1007/978-981-16-0425-6_2

Cited by 4 publications

(1 citation statement)

References 14 publications

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“…Deep learning has made significant strides in research over the past 10 years , and the subject of healthcare is no exception (Das et al, 2020). In the research on breast cancer, which produced encouraging findings as well, several deep architectures (Hamidinekoo et al, 2018) are investigated and effectively applied.…”

Section: Introductionmentioning

confidence: 99%

Breast cancer detection: Shallow convolutional neural network against deep convolutional neural networks based approach

Das

Das²,

Neog³

et al. 2023

Front. Genet.

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Introduction: Of all the cancers that afflict women, breast cancer (BC) has the second-highest mortality rate, and it is also believed to be the primary cause of the high death rate. Breast cancer is the most common cancer that affects women globally. There are two types of breast tumors: benign (less harmful and unlikely to become breast cancer) and malignant (which are very dangerous and might result in aberrant cells that could result in cancer).Methods: To find breast abnormalities like masses and micro-calcifications, competent and educated radiologists often examine mammographic images. This study focuses on computer-aided diagnosis to help radiologists make more precise diagnoses of breast cancer. This study aims to compare and examine the performance of the proposed shallow convolutional neural network architecture having different specifications against pre-trained deep convolutional neural network architectures trained on mammography images. Mammogram images are pre-processed in this study's initial attempt to carry out the automatic identification of BC. Thereafter, three different types of shallow convolutional neural networks with representational differences are then fed with the resulting data. In the second method, transfer learning via fine-tuning is used to feed the same collection of images into pre-trained convolutional neural networks VGG19, ResNet50, MobileNet-v2, Inception-v3, Xception, and Inception-ResNet-v2.Results: In our experiment with two datasets, the accuracy for the CBIS-DDSM and INbreast datasets are 80.4%, 89.2%, and 87.8%, 95.1% respectively.Discussion: It can be concluded from the experimental findings that the deep network-based approach with precise tuning outperforms all other state-of-the-art techniques in experiments on both datasets.

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Section: Introductionmentioning

confidence: 99%

Breast cancer detection: Shallow convolutional neural network against deep convolutional neural networks based approach

Das

Das²,

Neog³

et al. 2023

Front. Genet.

Self Cite

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Time efficient real time facial expression recognition with CNN and transfer learning

2022

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Predicting early breast cancer recurrence from histopathological images in the Carolina Breast Cancer Study

Shi,

Olsson,

Hoadley

et al. 2023

npj Breast Cancer

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Approaches for rapidly identifying patients at high risk of early breast cancer recurrence are needed. Image-based methods for prescreening hematoxylin and eosin (H&E) stained tumor slides could offer temporal and financial efficiency. We evaluated a data set of 704 1-mm tumor core H&E images (2–4 cores per case), corresponding to 202 participants (101 who recurred; 101 non-recurrent matched on age and follow-up time) from breast cancers diagnosed between 2008–2012 in the Carolina Breast Cancer Study. We leveraged deep learning to extract image information and trained a model to identify recurrence. Cross-validation accuracy for predicting recurrence was 62.4% [95% CI: 55.7, 69.1], similar to grade (65.8% [95% CI: 59.3, 72.3]) and ER status (66.3% [95% CI: 59.8, 72.8]). Interestingly, 70% (19/27) of early-recurrent low-intermediate grade tumors were identified by our image model. Relative to existing markers, image-based analyses provide complementary information for predicting early recurrence.

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Detection of Breast Cancer from Mammogram Images Using Deep Transfer Learning

Cited by 4 publications

References 14 publications

Breast cancer detection: Shallow convolutional neural network against deep convolutional neural networks based approach

Breast cancer detection: Shallow convolutional neural network against deep convolutional neural networks based approach

Time efficient real time facial expression recognition with CNN and transfer learning

Predicting early breast cancer recurrence from histopathological images in the Carolina Breast Cancer Study

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