Lesion classification in mammograms using convolutional neural networks and transfer learning

Perre, Ana Catarina; Alexandre, Luı́s A.; Freire, Luís

doi:10.1080/21681163.2018.1498392

Cited by 13 publications

(7 citation statements)

References 11 publications

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“…On the other hand, A. Perre et al [16] proposed a transfer learning approach using three separate networks (VGG-f, VGG-m and caffe). During the fine-tuning process, the output of these pre-trained CNNs was examined twice; one with image normalization and the other without image normalization to identify abnormalities in mammograms.…”

Section: Related Workmentioning

confidence: 99%

Automated Breast Cancer Detection Models Based on Transfer Learning

Alruwaili

Gouda

2022

Sensors

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Breast cancer is among the leading causes of mortality for females across the planet. It is essential for the well-being of women to develop early detection and diagnosis techniques. In mammography, focus has contributed to the use of deep learning (DL) models, which have been utilized by radiologists to enhance the needed processes to overcome the shortcomings of human observers. The transfer learning method is being used to distinguish malignant and benign breast cancer by fine-tuning multiple pre-trained models. In this study, we introduce a framework focused on the principle of transfer learning. In addition, a mixture of augmentation strategies were used to prevent overfitting and produce stable outcomes by increasing the number of mammographic images; including several rotation combinations, scaling, and shifting. On the Mammographic Image Analysis Society (MIAS) dataset, the proposed system was evaluated and achieved an accuracy of 89.5% using (residual network-50) ResNet50, and achieved an accuracy of 70% using the Nasnet-Mobile network. The proposed system demonstrated that pre-trained classification networks are significantly more effective and efficient, making them more acceptable for medical imaging, particularly for small training datasets.

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Section: Related Workmentioning

confidence: 99%

Automated Breast Cancer Detection Models Based on Transfer Learning

Alruwaili

Gouda

2022

Sensors

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“…Further, the proposed model successfully extracted stromal features and classified stroma around invasive cancer and benign biopsies in breast cancer. To improve the accuracy of the CNN classification model's using a small training dataset, Perre et al [38] employed the transfer learning technique on three pre-trained CNN on the Imagenet dataset. This pre-trained CNN's are used to extract specific features.…”

Section: Related Workmentioning

confidence: 99%

Automated Breast Mass Classification System Using Deep Learning and Ensemble Learning in Digital Mammogram

Malebary

Hashmi

2021

IEEE Access

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In recent years, deep learning techniques are employed in the mammography processing field to reduce radiologists' costs. Existing breast mass classification systems are implemented using deep learning technologies such as a Convolutional Neural Network (CNN). CNN based systems have attained higher performance than the machine learning-based systems in the classification task of mammography images, but a few issues still exist. Some of these issues are; ignorance of semantic features, analysis limitation to the current patch of images, lost patches in less contrast mammography images, and ambiguity in segmentation. These issues lead to increased false information about patches of mammography image, computational cost, decisions based on current patches, and not recovering the variance of patches intensity. In turn, breast mass classification systems based on convolutional neural networks produced unsatisfactory classification accuracy. To resolve these issues and improve the accuracy of classification on low contrast images, we propose a novel Breast Mass Classification system named BMC. It has improved architecture based on a combination of k-mean clustering, Long Short-Term Memory network of Recurrent Neural Network (RNN), CNN, random forest, boosting techniques to classify the breast mass into benign, malignant, and normal. Further, the proposed BMC system is compared with existing classification systems using two publicly available datasets of mammographic images. Proposed BMC system achieves the sensitivity, specificity, F-measure, and accuracy for the DDSM dataset is 0.97%, 0.98%,0.97%, 0.96% and for the MIAS dataset is 0.97%, 0.97%,0.98%, and 0.95% respectively. Further Area Under Curve (AUC) rate of the proposed BMC system lies between 0.94% -0.97% for DDSM and 0.94%-0.98% for the MIAS dataset. The BMC method worked comparably better than other mammography classification schemes that have previously been invented. Moreover, the Confidence interval statistical test is also employed to determine the classification accuracy of the BMC system using different configurations and neural network parameters.INDEX TERMS Deep learning, convolutional neural network, random forest, breast mass classification, recurrent neural network, boosting, machine learning, mammograms, medical image processing.

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“…Transfer learning is concerned, and experiment results indicate that CNN models with transfer learning outperform the models without transfer learning. Reference [63] investigates three kinds of implementation of an 8-layered CNN architecture. Parameters, such as the number of convolutional filters in each layer, are fine-tuned with mammographic lesion instances.…”

Section: Cnn-based Mbcdmentioning

confidence: 99%

“…Existing deep architectures are made the most use of, and these models [61, 63, 64, 68] are pretrained on the ImageNet and parameters are initialized. And then, mammographic lesion instances are used to fine-tune the deep models.…”

Section: Cnn-based Mbcdmentioning

confidence: 99%

A Technical Review of Convolutional Neural Network-Based Mammographic Breast Cancer Diagnosis

Zou

Meng

et al. 2019

Computational and Mathematical Methods in Medicine

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This study reviews the technique of convolutional neural network (CNN) applied in a specific field of mammographic breast cancer diagnosis (MBCD). It aims to provide several clues on how to use CNN for related tasks. MBCD is a long-standing problem, and massive computer-aided diagnosis models have been proposed. The models of CNN-based MBCD can be broadly categorized into three groups. One is to design shallow or to modify existing models to decrease the time cost as well as the number of instances for training; another is to make the best use of a pretrained CNN by transfer learning and fine-tuning; the third is to take advantage of CNN models for feature extraction, and the differentiation of malignant lesions from benign ones is fulfilled by using machine learning classifiers. This study enrolls peer-reviewed journal publications and presents technical details and pros and cons of each model. Furthermore, the findings, challenges and limitations are summarized and some clues on the future work are also given. Conclusively, CNN-based MBCD is at its early stage, and there is still a long way ahead in achieving the ultimate goal of using deep learning tools to facilitate clinical practice. This review benefits scientific researchers, industrial engineers, and those who are devoted to intelligent cancer diagnosis.

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Lesion classification in mammograms using convolutional neural networks and transfer learning

Cited by 13 publications

References 11 publications

Automated Breast Cancer Detection Models Based on Transfer Learning

Automated Breast Cancer Detection Models Based on Transfer Learning

Automated Breast Mass Classification System Using Deep Learning and Ensemble Learning in Digital Mammogram

A Technical Review of Convolutional Neural Network-Based Mammographic Breast Cancer Diagnosis

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