Comparing the performance of various deep networks for binary classification of breast tumours

Hamidinekoo, Azam; Suhail, Zobia; Denton, Erika R. E.; Zwiggelaar, Reyer

doi:10.1117/12.2318084

Cited by 7 publications

(3 citation statements)

References 7 publications

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“…This approach is tested in both our current and previous work [1]. In a similar fashion, VGG16 [39], GoogLeNet [40] and AlexNet [13] are trained in TL in [41].…”

Section: Transfer Learning As a New Convnet Classifiermentioning

confidence: 99%

Transfer Learning and Fine Tuning in Breast Mammogram Abnormalities Classification on CBIS-DDSM Database

Falconi¹,

Pérez²,

Aguila³

et al. 2020

Adv. sci. technol. eng. syst. j.

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Breast cancer has an important incidence in women mortality worldwide. Currently, mammography is considered the gold standard for breast abnormalities screening examinations, since it aids in the early detection and diagnosis of the illness. However, both identification of mass lesions and its malignancy classification is a challenging problem for artificial intelligence. In this work, we extend our previous research in mammogram classification, where we studied NasNet and MobileNet in transfer learning to train a breast abnormality malignancy classifier, and include models like: VGG, Resnet, Xception and Resnext. However, training deep learning models tends to overfit. This problem is also carried out in this work. Our results show that Fine Tuning achieves the best classifier performance in VGG16 with AUC value of 0.844 in the CBIS-DDSM dataset.

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“…This approach is tested in both our current and previous work [1]. In a similar fashion, VGG16 [39], GoogLeNet [40] and AlexNet [13] are trained in TL in [41].…”

Section: Transfer Learning As a New Convnet Classifiermentioning

confidence: 99%

Transfer Learning and Fine Tuning in Breast Mammogram Abnormalities Classification on CBIS-DDSM Database

Falconi¹,

Pérez²,

Aguila³

et al. 2020

Adv. sci. technol. eng. syst. j.

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show abstract

“…The information about these datasets are summarised in Table 1. In the preprocessing step, all images were segmented into background and tissue and the intensity values of the segmented regions were normalised (Hamidinekoo et al, 2018b).…”

Section: Datasetsmentioning

confidence: 99%

“…When several regions were detected, connected component analysis was used in the post-processing section and the three largest detected regions were extracted as RoIs. CADi development Based on a comparative study (Hamidinekoo et al, 2018b), among the well-known deep CNNs, the DenseNet was found as an appropriate model for mass classification due to its key characteristic to bypass signals from the preceding layers to the subsequent layers. In our implementations, the DenseNet's growth rate was set to 4 to construct 4 dense-blocks and 3 transition layers in the architecture.…”

Section: Model Architecturementioning

confidence: 99%

Automated Mammogram Analysis with a Deep Learning Pipeline

Hamidinekoo¹,

Denton²,

Zwiggelaar³

2019

Preprint

Self Cite

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Current deep learning based detection models tackle detection and segmentation tasks by casting them to pixel or patch-wise classification. To automate the initial mass lesion detection and segmentation on the whole mammographic images and avoid the computational redundancy of patch-based and sliding window approaches, the conditional generative adversarial network (cGAN) was used in this study. Subsequently, feeding the detected regions to the trained densely connected network (DenseNet), the binary classification of benign versus malignant was predicted. We used a combination of publicly available mammographic data repositories to train the pipeline, while evaluating the model's robustness toward our clinically collected repository, which was unseen to the pipeline.

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Breast Cancer Detection and Localization Using MobileNet Based Transfer Learning for Mammograms

Ansar

Raza

Dar

2020

Intelligent Computing Systems

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Comparing the performance of various deep networks for binary classification of breast tumours

Cited by 7 publications

References 7 publications

Transfer Learning and Fine Tuning in Breast Mammogram Abnormalities Classification on CBIS-DDSM Database

Transfer Learning and Fine Tuning in Breast Mammogram Abnormalities Classification on CBIS-DDSM Database

Automated Mammogram Analysis with a Deep Learning Pipeline

Breast Cancer Detection and Localization Using MobileNet Based Transfer Learning for Mammograms

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