Transfer learning in deep convolutional neural networks for detection of architectural distortion in digital mammography

Costa, Arthur C.; Oliveira, Helder Cesar Rodigues de; Borges, Lucas R.; Vieira, Marcelo A. C.

doi:10.1117/12.2564348

Cited by 7 publications

(3 citation statements)

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“…156 To this end, Costa et al transferred the VGG-16 network for detection on clinical mammogram data sets with 280 images. 157 Positive patches for training are cropped out from original images where the center of AD is shown in the patches while negative patches are randomly sampled within the breast area. By doing so, a patch data set containing 44,224 ROIs with the same number for positive and negative patches is formed.…”

Section: Other Detection Scenarios In Breast Cancermentioning

confidence: 99%

A systematic survey of deep learning in breast cancer

Zhou

Wang

et al. 2021

Int J Intell Syst

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In recent years, we witnessed a speeding development of deep learning in computer vision fields like categorization, detection, and semantic segmentation. Within several years after the emergence of AlexNet, the performance of deep neural networks has already surpassed human being experts in certain areas and showed great potential in applications such as medical image analysis. The development of automated breast cancer detection systems that integrate deep learning has received wide attention from the community. Breast cancer, a major killer of females that results in millions of deaths, can be controlled even be cured given that it is detected at an early stage with sophisticated systems. In this paper, we reviewed breast cancer diagnosis, detection, and segmentation computer-aided (CAD) systems based on state-of-the-art deep convolutional neural networks. The available data sets also indirectly determine CAD systems' performance, so we introduced and discussed the details of public data sets. The challenges remaining in CAD systems for breast cancer are discussed at the end of this paper. The highlights of this survey mainly come from three following aspects. First, we covered a wide range of the basics of breast cancer from imaging modalities to popular databases in the community; Second, we presented the key elements in deep learning to form the compactness for methods mentioned in reviewed papers; Third and lastly, the summative details in each reviewed paper are provided so that interested readers can have a refined version of these works without referring to original papers.Therefore, this systematic survey suits readers with varied backgrounds and will be beneficial to them.

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Section: Other Detection Scenarios In Breast Cancermentioning

confidence: 99%

A systematic survey of deep learning in breast cancer

Zhou

Wang

et al. 2021

Int J Intell Syst

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“…Experimenting with BCDR dataset, the approach yielded AUC was 0.984, and accuracy was 0.982. Using a deep learning approach, authors in [170] adapted a pre-trained VGG-16 network on ImageNet images in combination with transfer learning technique to obtain AUC = 0.89. Similarly, in [171], the study developed a deep learning model for the detection of AD through detection (Gabor filters) and aggregation (Faster-RCNN) in 2D and 3D, respectively.…”

Section: ) Architectural Distortionmentioning

confidence: 99%

A State-of-the-Art Survey on Deep Learning Methods for Detection of Architectural Distortion From Digital Mammography

Oyelade

Ezugwu

2020

IEEE Access

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Breast cancer is a type of cancer that has risen to be the second cause of death among women. Classification of breast tissues into normal, benign, or malignant depends on the presence of abnormalities like microcalcifications, masses, architectural distortions, and asymmetries. Architectural distortion (AD) is subtle in detection with no association with masses but shows the abnormal arrangement of tissue strands, often in a radial, spiculation, or random pattern. It is widely rated as the third symptom of breast cancer which is the most commonly missed abnormality. Most computational approaches characterizing abnormalities in breast images often concentrate on the detection of microcalcification and masses with architectural distortions appearing as a secondary finding. The subtle nature and a minimal occurrence of architectural distortions may seem to complicate computational approaches for its detection. As a result, little research interest has been recorded in this area. It is widely reported that some cases of recent breast cancer are wrongly diagnosed due to the omission in detecting the presence of architectural distortion at the early stage of the disease. However, we discovered that most computational solutions to early detection of breast cancer are focused mainly on detecting other abnormalities such as masses and microcalcification, which are some evidence of the advanced stage of the disease. To emphasise the little efforts channeled towards detection of AD compared to other abnormalities, this article aims to detail the review of such studies in the last decade. To the best of our knowledge, this study presents the first review which focuses on the detection of architectural distortion (AD) from mammographic images. Furthermore, this article presents a comprehensive review of approaches, advances, and challenges on the computational methods for detecting AD, with the sole aim of advancing the use of deep learning models in detecting AD. Moreover, a comparative study of performance analyses of articles surveyed in this article is investigated. Our findings revealed that about 70% of the existing literature adopted Gabor Filters, while just less than 10% leveraged on the state-ofthe-art performances recorded in computer vision and deep learning, in building outstanding computational models for the detection of AD. The current study also discovered that using a deep learning approach, such as the convolution neural network (CNN) method, can yield a significant increase in performance for the task of detection of architectural distortions. This assertion is based on literature results obtained using the CNN, which generates an accuracy of 99.4% compared to the use of Gabor filters method, which accounts for 95% accuracy.

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“…10,11 Deep learning models have demonstrated outstanding results by supporting state-of-the-art CADs techniques in identifying abnormalities such as mass, microcalcifications, asymmetry, and even subtle abnormalities like architectural distortion in digital breast images (Wx). [11][12][13][14][15][16][17][18][19] Convolutional neural network (CNN) is a type of deep learning model and often applied to the problems of detecting abnormalities in digital images, localization of regions of interest (ROIs), classification of findings, image retrieval, and risk assessment. CNN has advanced rapidly over the years but still appears difficult to understand due to its complexity in obtaining a consistent and outstanding result from a given architecture.…”

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confidence: 99%

A comparative performance study of random‐grid model for hyperparameters selection in detection of abnormalities in digital breast images

Oyelade

Ezugwu

2022

Concurrency and Computation

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Deep learning models have been widely reported to have achieved significant performance in image processing and classification tasks. They have mainly been harnessed and applied to the problem of detecting abnormalities in digital breast images. However, the significant number and high dimensional space requirement of hyperparameters in deep learning models often make it challenging to find the best configuration for such parameters when tuning for best performance. In appropriately handling this performance tuning often lead to difficulty in striking balance between underfitting and overfitting. This article proposes an optimized convolutional neural network (CNN) architecture through application of hybrid selection model in obtaining best hyperparameter configuration which outperforms similar existing models. We approached this non-trivial challenge by defining a hybrid of the grid-based and random-based model for the selection of hyperparameters and then investigate the performance of the configurations. To further improve the performance of the CNN model, data augmentation technique was applied. Furthermore, the study undertook a comparative study of the performance of the best configuration on some benchmarked datasets. The resulting model was applied to publicly available benchmark datasets, namely, the DDSM and MIAS datasets. Findings from the experimentations revealed that hyperparameters with Adam optimization algorithm showed superiority by yielding an accuracy of 1.0 using DDSM dataset, while SGD, RMSprop, Adam, and Adagrad output an accuracy of 0.9375 with MIAS dataset. The outcome of this study further strengthens the appropriateness of Adam optimizer and has also produced a state-of-the-art CNN model suitable for solving the problem of detection and classification of breast cancer from digital mammography.

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Transfer learning in deep convolutional neural networks for detection of architectural distortion in digital mammography

Cited by 7 publications

References 0 publications

A systematic survey of deep learning in breast cancer

A systematic survey of deep learning in breast cancer

A State-of-the-Art Survey on Deep Learning Methods for Detection of Architectural Distortion From Digital Mammography

A comparative performance study of random‐grid model for hyperparameters selection in detection of abnormalities in digital breast images

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