Breast mass detection in digital mammography based on anchor-free architecture

Cao, Haichao; Pu, Shiliang; Tan, Wenming; Tong, Junyan

doi:10.1016/j.cmpb.2021.106033

Cited by 34 publications

(10 citation statements)

References 34 publications

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“…The process was examined on the Mini-MIAS dataset with the VGG-16 and VGG-19 pre-trained models. The “natural deformation data augmentation approach” is proposed by Cao et al [ 67 ] as a new data augmentation method based on local elastic deformation. The essential notion is that only the BMass is elastically deformed in a picture containing BMass to replicate the natural changing of BMass, while the local background region in contact with BMass changes accordingly.…”

Section: Advanced Augmentation Techniquesmentioning

confidence: 99%

Image Augmentation Techniques for Mammogram Analysis

et al. 2022

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Research in the medical imaging field using deep learning approaches has become progressively contingent. Scientific findings reveal that supervised deep learning methods’ performance heavily depends on training set size, which expert radiologists must manually annotate. The latter is quite a tiring and time-consuming task. Therefore, most of the freely accessible biomedical image datasets are small-sized. Furthermore, it is challenging to have big-sized medical image datasets due to privacy and legal issues. Consequently, not a small number of supervised deep learning models are prone to overfitting and cannot produce generalized output. One of the most popular methods to mitigate the issue above goes under the name of data augmentation. This technique helps increase training set size by utilizing various transformations and has been publicized to improve the model performance when tested on new data. This article surveyed different data augmentation techniques employed on mammogram images. The article aims to provide insights into basic and deep learning-based augmentation techniques.

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Section: Advanced Augmentation Techniquesmentioning

confidence: 99%

Image Augmentation Techniques for Mammogram Analysis

et al. 2022

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“…CNN, ResNet-50, and InceptionResNet-V2 were used for classification and achieved an average overall accuracy of 88.74%, 92.56%, and 95.32%, respectively. Cao et al (2021) [16] proposed a novel model for detecting breast masses in mammograms, furthermore, they proposed a new data augmentation technique to overcome the overfitting problem due to the small dataset. Their augmentation technique is based on local elastic deformation, this technique enhanced the performance of their model; however, its calculation speed is slower compared to the traditional augmentation techniques.…”

Section: Deep Learning-based-object Detection (Single Shot and Two Sh...mentioning

confidence: 99%

Mammogram breast cancer CAD systems for mass detection and classification: a review

Hassan

Hamad

Mahar

2022

Multimed Tools Appl

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Although there is an improvement in breast cancer detection and classification (CAD) tools, there are still some challenges and limitations that need more investigation. The significant development in machine learning and image processing techniques in the last ten years affected hugely the development of breast cancer CAD systems especially with the existence of deep learning models. This survey presents in a structured way, the current deep learning-based CAD system to detect and classify masses in mammography, in addition to the conventional machine learning-based techniques. The survey presents the current publicly mammographic datasets, also provides a dataset-based quantitative comparison of the most recent techniques and the most used evaluation metrics for the breast cancer CAD systems. The survey provides a discussion of the current literature and emphasizes its pros and limitations. Furthermore, the survey highlights the challenges and limitations in the current breast cancer detection and classification techniques.

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“…Data augmentation methods were added to the workflow of these experiments to enlarge the data set and avoid overfitting issues [51]- [53]. This technique aims to increase the number of deep neural model training datasets, balance the size of the datasets, and boost their efficiency, but it is still the subject of research [54].…”

Section: Data Augmentationmentioning

confidence: 99%

Lung cancer medical images classification using hybrid CNN-SVM

Saleh

Chin

Penshie

et al. 2021

Int. J. Adv. Intell. Informatics

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Lung cancer is one of the leading causes of death worldwide. Early detection of this disease increases the chances of survival. Computer-Aided Detection (CAD) has been used to process CT images of the lung to determine whether an image has traces of cancer. This paper presents an image classification method based on the hybrid Convolutional Neural Network (CNN) algorithm and Support Vector Machine (SVM). This algorithm is capable of automatically classifying and analyzing each lung image to check if there is any presence of cancer cells or not. CNN is easier to train and has fewer parameters compared to a fully connected network with the same number of hidden units. Moreover, SVM has been utilized to eliminate useless information that affects accuracy negatively. In recent years, Convolutional Neural Networks (CNNs) have achieved excellent performance in many computer visions tasks. In this study, the performance of this algorithm is evaluated, and the results indicated that our proposed CNN-SVM algorithm has been succeed in classifying lung images with 97.91% accuracy. This has shown the method's merit and its ability to classify lung cancer in CT images accurately.

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Breast mass detection in digital mammography based on anchor-free architecture

Cited by 34 publications

References 34 publications

Image Augmentation Techniques for Mammogram Analysis

Image Augmentation Techniques for Mammogram Analysis

Mammogram breast cancer CAD systems for mass detection and classification: a review

Lung cancer medical images classification using hybrid CNN-SVM

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