Performance analysis of various classifiers on deep learning network for breast cancer detection

Selvathi, D.; AarthyPoornila, A

doi:10.1109/cspc.2017.8305869

Cited by 22 publications

(9 citation statements)

References 16 publications

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“…The model was tested on the MIAS dataset and achieved an accuracy rate of 98.5%. An integrated system with sparse autoencoder (SAE) and ML classifiers, such as decision tree, KNN, SVM and random forest, was developed by Selvathi and Poornila [139]. The SAE learns representations of features from images and classifiers cascaded with SAE and classification based on extracted features.…”

Section: Autoencodersmentioning

confidence: 99%

“…In greater detail, Kallenberg et al [135] scored 0.59 on AUC; Petersen et al used denoised sparse autoencoders, obtaining 0.62 AUC on a private set of data. The autoencoder-based methods by Selvathi and Poornila [138,139] and Taghanaki et al [140] performed remarkably well in mammogram classification. For example, Selvathi and Poornila [138] achieved 98.50% accuracy on MIAS using stacked autoencoders.…”

Section: Pros and Cons Of Deep Learning Approachesmentioning

confidence: 99%

“…For example, Selvathi and Poornila [138] achieved 98.50% accuracy on MIAS using stacked autoencoders. Furthermore, they improved by 0.39% when they combined sparse autoencoders and random forest [139] and ran it over MIAS. Taghanaki et al [140] reached 98.45% accuracy on two different datasets, INBreast and IRMA, using a multi-objective optimisation of deep auto-encoder networks.…”

Section: Pros and Cons Of Deep Learning Approachesmentioning

confidence: 99%

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A Bottom-Up Review of Image Analysis Methods for Suspicious Region Detection in Mammograms

et al. 2021

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Breast cancer is one of the most common death causes amongst women all over the world. Early detection of breast cancer plays a critical role in increasing the survival rate. Various imaging modalities, such as mammography, breast MRI, ultrasound and thermography, are used to detect breast cancer. Though there is a considerable success with mammography in biomedical imaging, detecting suspicious areas remains a challenge because, due to the manual examination and variations in shape, size, other mass morphological features, mammography accuracy changes with the density of the breast. Furthermore, going through the analysis of many mammograms per day can be a tedious task for radiologists and practitioners. One of the main objectives of biomedical imaging is to provide radiologists and practitioners with tools to help them identify all suspicious regions in a given image. Computer-aided mass detection in mammograms can serve as a second opinion tool to help radiologists avoid running into oversight errors. The scientific community has made much progress in this topic, and several approaches have been proposed along the way. Following a bottom-up narrative, this paper surveys different scientific methodologies and techniques to detect suspicious regions in mammograms spanning from methods based on low-level image features to the most recent novelties in AI-based approaches. Both theoretical and practical grounds are provided across the paper sections to highlight the pros and cons of different methodologies. The paper’s main scope is to let readers embark on a journey through a fully comprehensive description of techniques, strategies and datasets on the topic.

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Section: Autoencodersmentioning

confidence: 99%

Section: Pros and Cons Of Deep Learning Approachesmentioning

confidence: 99%

Section: Pros and Cons Of Deep Learning Approachesmentioning

confidence: 99%

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A Bottom-Up Review of Image Analysis Methods for Suspicious Region Detection in Mammograms

et al. 2021

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“…The total positive cases are P = T P + FN; similarly, the total negative cases: N = T N + FP. From these measures, the true positive rate (8), false positive rate (9), and true negative rate (10) are derived.…”

Section: Performance Metricsmentioning

confidence: 99%

“…However, mammography has some limitations like the variability of its sensitivity, which is inversely proportional to breast density, the false positive and negative rates, and the patient's exposure to radiation [7]. Other screening tests available are: ultrasound, magnetic resonance (MRI), tomosynthesis, and infrared thermography [8]- [9]; in most cases, the aforementioned screening tests are used as adjunct tests.…”

Section: Introductionmentioning

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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Comparative Study on Various Techniques Involved in Designing a Computer Aided Diagnosis (CAD) System for Mammogram Classification

Mrunalini

NareshKumar

Premaladha

2020

New Trends in Computational Vision and Bio-Inspired Computing

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Performance analysis of various classifiers on deep learning network for breast cancer detection

Cited by 22 publications

References 16 publications

A Bottom-Up Review of Image Analysis Methods for Suspicious Region Detection in Mammograms

A Bottom-Up Review of Image Analysis Methods for Suspicious Region Detection in Mammograms

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

Comparative Study on Various Techniques Involved in Designing a Computer Aided Diagnosis (CAD) System for Mammogram Classification

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