Transfer Learning in Breast Mammogram Abnormalities Classification With Mobilenet and Nasnet

Falconi, Lenin G.; Pérez, María Jesús Verdú; Aguilar, Wilbert G.

doi:10.1109/iwssip.2019.8787295

Cited by 74 publications

(38 citation statements)

References 19 publications

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“…This work is an extension of our work originally presented in IWS-SIP 2019 [1] about mammogram abnormalities classification using Transfer Learning (TL) with Mobilenet [2] and Nasnet [3]. In this paper, we also address the classification problem of mammogram abnormalities using the CBIS-DDSM [4] dataset, but we extend the experimentation in transfer learning to other ImageNet pre-trained convolutional neural network (ConvNet) models like: Resnet, Resnext, Xception; to name a few.…”

Section: Introductionmentioning

confidence: 85%

“…In our previous work [1], we used the methodology by [31], in order to find relevant works for study. Table 1 shows the search string designed to retrieve information from: Springer Link, Science Direct (Elsevier), IEEE Xplore, Scopus, Web of Science, ACM digital library, and PubMed.…”

Section: Search Processmentioning

confidence: 99%

“…Only the added layers are trained while the rest of the Con-vNet's weights remain frozen. 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%

“…In this work, we aim to classify region of interest images from mass tumors of the CBIS-DDSM [4] dataset. We extend our previous experimentation presented in [1] by using different ConvNets models and also the Fine Tuning technique in order to increase the performance of the classifier of breast mammogram abnormalities in benign and malignant. Our research results indicate that Fine Tuning is able to train an accurate classifier and overcome overfitting.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Introductionmentioning

confidence: 85%

Section: Search Processmentioning

confidence: 99%

Section: Transfer Learning As a New Convnet Classifiermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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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“…Researchers apply deep learning in object recognition in various domains. In the Health domain, researchers have applied deep learning based on medical images [17][18][19]. In the security domain, deep learning has been applied to palmprint classification [20] and eye feature point detection [21].…”

Section: Introductionmentioning

confidence: 99%

Neural Style Transfer and Geometric Transformations for Data Augmentation on Balinese Carving Recognition using MobileNet

Darma¹,

Suciati²,

Siahaan³

2020

IJIES

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The preservation of Balinese carving data is a challenge in recognition of Balinese carving. Balinese carvings are a cultural heritage found in traditional buildings in Bali. The collection of Balinese carving images from public images can be a solution for preserving cultural heritage. However, the lousy quality of taking photographs, e.g., skewed shots, can affect the recognition results. Research on the Balinese carving recognition has existed but only recognizes a predetermined image. We proposed a Neural Style Geometric Transformation (NSGT) as a data augmentation technique for Balinese carvings recognition. NSGT is combining Neural Style Transfers and Geometric Transformations for a small dataset solution. This method provides variations in color, lighting, rotation, rescale, zoom, and the size of the training dataset, to improve recognition performance. We use MobileNet as a feature extractor because it has a small number of parameters, which makes it suitable to be applied on mobile devices. Eight scenarios were tested based on image styles and geometric transformations to get the best results. Based on the results, the proposed method can improve accuracy by up to 16.2%.

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Augmentations: An Insight into Their Effectiveness on Convolution Neural Networks

Ethiraj

Bolla²

2022

Communications in Computer and Information Science

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Transfer Learning in Breast Mammogram Abnormalities Classification With Mobilenet and Nasnet

Cited by 74 publications

References 19 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

Neural Style Transfer and Geometric Transformations for Data Augmentation on Balinese Carving Recognition using MobileNet

Augmentations: An Insight into Their Effectiveness on Convolution Neural Networks

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