Low Dose Mammography via Deep Learning

Zhu, Guogang; Fu, Jian; Dong, Jianbing

doi:10.1088/1742-6596/1626/1/012110

Cited by 5 publications

(2 citation statements)

References 12 publications

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“…Zhu et al [ 17 ] employed DL to develop a method for improving the quality of low-dose mammography images. The fundamental goal of the CNN model used for low-dose mammography is noise reduction.…”

Section: Literature Studymentioning

confidence: 99%

Breast Cancer Diagnosis Based on IoT and Deep Transfer Learning Enabled by Fog Computing

et al. 2023

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Across all countries, both developing and developed, women face the greatest risk of breast cancer. Patients who have their breast cancer diagnosed and staged early have a better chance of receiving treatment before the disease spreads. The automatic analysis and classification of medical images are made possible by today’s technology, allowing for quicker and more accurate data processing. The Internet of Things (IoT) is now crucial for the early and remote diagnosis of chronic diseases. In this study, mammography images from the publicly available online repository The Cancer Imaging Archive (TCIA) were used to train a deep transfer learning (DTL) model for an autonomous breast cancer diagnostic system. The data were pre-processed before being fed into the model. A popular deep learning (DL) technique, i.e., convolutional neural networks (CNNs), was combined with transfer learning (TL) techniques such as ResNet50, InceptionV3, AlexNet, VGG16, and VGG19 to boost prediction accuracy along with a support vector machine (SVM) classifier. Extensive simulations were analyzed by employing a variety of performances and network metrics to demonstrate the viability of the proposed paradigm. Outperforming some current works based on mammogram images, the experimental accuracy, precision, sensitivity, specificity, and f1-scores reached 97.99%, 99.51%, 98.43%, 80.08%, and 98.97%, respectively, on the huge dataset of mammography images categorized as benign and malignant, respectively. Incorporating Fog computing technologies, this model safeguards the privacy and security of patient data, reduces the load on centralized servers, and increases the output.

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Section: Literature Studymentioning

confidence: 99%

Breast Cancer Diagnosis Based on IoT and Deep Transfer Learning Enabled by Fog Computing

et al. 2023

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“…These kinds of deep neural networks have also been employed in medicine, where they have shown to be effective at predicting and classifying patient diagnoses. The U-Net model, for example, has shown good performance in image segmentations, a critical technique in medical imaging [1] and X-ray mammography [2]. Deep neural networks, on the other hand, are subject to adversarial models.…”

Section: Introductionmentioning

confidence: 99%

Improvement of noisy images filtered by bilateral process using a multi-scale context aggregation network

Hussein

2022

EEJET

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Deep learning has recently received a lot of attention as a feasible solution to a variety of artificial intelligence difficulties. Convolutional neural networks (CNNs) outperform other deep learning architectures in the application of object identification and recognition when compared to other machine learning methods. Speech recognition, pattern analysis, and image identification, all benefit from deep neural networks. When performing image operations on noisy images, such as fog removal or low light enhancement, image processing methods such as filtering or image enhancement are required. The study shows the effect of using Multi-scale deep learning Context Aggregation Network CAN on Bilateral Filtering Approximation (BFA) for de-noising noisy CCTV images. Data-store is used tomanage our dataset, which is an object or collection of data that are huge to enter in memory, it allows to read, manage, and process data located in multiple files as a single entity. The CAN architecture provides integral deep learning layers such as input, convolution, back normalization, and Leaky ReLu layers to construct multi-scale. It is also possible to add custom layers like adaptor normalization (µ) and adaptive normalization (Lambda) to the network. The performance of the developed CAN approximation operator on the bilateral filtering noisy image is proven when improving both the noisy reference image and a CCTV foggy image. The three image evaluation metrics (SSIM, NIQE, and PSNR) evaluate the developed CAN approximation visually and quantitatively when comparing the created de-noised image over the reference image.Compared with the input noisy image, these evaluation metrics for the developed CAN de-noised image were (0.92673/0.76253, 6.18105/12.1865, and 26.786/20.3254) respectively

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Enhancing mammography: a comprehensive review of computer methods for improving image quality

Santos,

Abreu

2024

Prog. Biomed. Eng.

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Mammography imaging remains the gold standard for breast cancer detection and diagnosis, but challenges in image quality can lead to misdiagnosis, increased radiation exposure, and higher healthcare costs. This comprehensive review evaluates traditional and machine learning-based techniques for improving mammography image quality, aiming to benefit clinicians and enhance diagnostic accuracy. Our literature search, spanning 2015 – 2024, identified 115 articles focusing on contrast enhancement and noise reduction methods, including histogram equalization, filtering, unsharp masking, fuzzy logic, transform-based techniques, and advanced machine learning approaches. Machine learning, particularly architectures integrating denoising autoencoders with convolutional neural networks, emerged as highly effective in enhancing image quality without compromising detail. The discussion highlights the success of these techniques in improving mammography images’ visual quality. However, challenges such as high noise ratios, inconsistent evaluation metrics, and limited open-source datasets persist. Addressing these issues offers opportunities for future research to further advance mammography image enhancement methodologies.

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Low Dose Mammography via Deep Learning

Cited by 5 publications

References 12 publications

Breast Cancer Diagnosis Based on IoT and Deep Transfer Learning Enabled by Fog Computing

Breast Cancer Diagnosis Based on IoT and Deep Transfer Learning Enabled by Fog Computing

Improvement of noisy images filtered by bilateral process using a multi-scale context aggregation network

Enhancing mammography: a comprehensive review of computer methods for improving image quality

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