A review of mammographic region of interest classification

Tasdemir, Sena Busra Yengec; Taşdemir, Kasım; Aydın, Zafer

doi:10.1002/widm.1357

Cited by 13 publications

(9 citation statements)

References 51 publications

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“…Data augmentation helps the CNN model generalize better and reduces over tting by providing more diverse examples for training [34]. Large datasets are preferable for use with DL models.…”

Section: Data Augmentationmentioning

confidence: 99%

A Transfer Learning-Based Deep Learning Model for Automated Breast Cancer Identification in Mammograms

katiyar

2024

Preprint

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Breast cancer is a severe health issue that affects women all over the world, underscoring the need for reliable and effective screening techniques. The early detection, diagnosis, and treatment of breast cancer are made possible by computer-aided diagnostic (CAD) systems that rely on mammograms. This study introduces a unique deep-learning model that uses transfer learning to identify and categorize breast cancer automatically. Deep convolutional neural networks have been shown in several recent studies to diagnose breast cancer in mammograms with performance comparable to or even outperforming that of human experts. To extract attributes from the Mammographic Image Analysis Society (MIAS) dataset, the proposed model uses pre-trained convolutional neural network (CNN) architectures like ResNet50 and Visual Geometry Group networks (VGG)-16. This novel deep-learning model holds significant potential for enhancing the efficiency and accuracy of breast cancer detection and classification. A preprint has previously been published [1]

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“…Data augmentation helps the CNN model generalize better and reduces over tting by providing more diverse examples for training [34]. Large datasets are preferable for use with DL models.…”

Section: Data Augmentationmentioning

confidence: 99%

A Transfer Learning-Based Deep Learning Model for Automated Breast Cancer Identification in Mammograms

katiyar

2024

Preprint

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“…There are 753 calcification instances, 891 mass cases, and 3071 images in the CBIS-DDMS. The abovementioned work used a mammogram database provided by MIAS [33]. The database consists of 322 images in the portable gray map (PGM) format, each with a size of 1024 × 1024 pixels.…”

Section: Dataset Descriptionmentioning

confidence: 99%

A Transfer Learning-Based Deep Learning Model for Automated Breast Cancer Identification in Mammograms

katiyar

2023

Preprint

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Breast cancer is a severe health issue that affects women all over the world, underscoring the need for reliable and effective screening techniques. The early detection, diagnosis, and treatment of breast cancer are made possible by computer-aided diagnostic (CAD) systems that rely on mammograms. This study introduces a unique deep-learning model that uses transfer learning to identify and categorize breast cancer automatically. Deep convolutional neural networks have been shown in several recent studies to diagnose breast cancer in mammograms with performance comparable to or even outperforming that of human experts. In order to extract features from the dataset from the Mammographic Image Analysis Society (MIAS), the proposed model uses pre-trained convolutional neural network (CNN) architectures like ResNet50 and Visual Geometry Group networks (VGG)-16. This novel deep-learning model holds significant potential for enhancing the efficiency and accuracy of breast cancer detection and classification.

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“…As shown in Fig. 7, the digital database for screening mammography (DDSM), MIAS, and private datasets are the most popular databases used for BC-classification models based on the statistics discussed on [103]. In this work, the applied mammogram database was provided by MIAS.…”

Section: A Dataset Descriptionmentioning

confidence: 99%

A Novel Deep-Learning Model for Automatic Detection and Classification of Breast Cancer Using the Transfer-Learning Technique

et al. 2021

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Breast cancer (BC) is one of the primary causes of cancer death among women. Early detection of BC allows patients to receive appropriate treatment, thus increasing the possibility of survival. In this work, a new deep-learning (DL) model based on the transfer-learning (TL) technique is developed to efficiently assist in the automatic detection and diagnosis of the BC suspected area based on two techniques namely 80-20 and cross-validation. DL architectures are modeled to be problem-specific. TL uses the knowledge gained during solving one problem in another relevant problem. In the proposed model, the features are extracted from the mammographic image analysis-society (MIAS) dataset using a pretrained convolutional neural network (CNN) architecture such as Inception V3, ResNet50, Visual Geometry Group networks (VGG)-19, VGG-16, and Inception-V2 ResNet. Six evaluation metrics for evaluating the performance of the proposed model in terms of accuracy, sensitivity, specificity, precision, F-score, and area under the ROC curve (AUC) has been chosen. Experimental results show that the TL of the VGG16 model is powerful for BC diagnosis by classifying the mammogram breast images with overall

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A review of mammographic region of interest classification

Cited by 13 publications

References 51 publications

A Transfer Learning-Based Deep Learning Model for Automated Breast Cancer Identification in Mammograms

A Transfer Learning-Based Deep Learning Model for Automated Breast Cancer Identification in Mammograms

A Transfer Learning-Based Deep Learning Model for Automated Breast Cancer Identification in Mammograms

A Novel Deep-Learning Model for Automatic Detection and Classification of Breast Cancer Using the Transfer-Learning Technique

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