Machine Learning Algorithms for Binary Classification of Breast Cancer

Katiyar, Preeti; Singh, Krishna Pratap

doi:10.1007/978-981-99-1312-1_3

Cited by 3 publications

(2 citation statements)

References 18 publications

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“…Recently, machine learning has been used to create computer-aided detection (CAD) systems for identifying benign and malignant lesions via image processing, artificial intelligence, and neural network approaches. These technologies can improve image quality, automate image readability, and reduce experimental errors for more accurate diagnosis [5]- [7]. Machine learning (ML) and artificial intelligence (AI) approaches are being used in many research publications that examine the detection, segmentation, and classification of breast cancer.…”

Section: Introductionmentioning

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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Section: Introductionmentioning

confidence: 99%

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

katiyar

2023

Preprint

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“…experimental errors for more accurate diagnosis [5]- [7]. Machine learning (ML) and arti cial intelligence (AI) approaches are being used in many research publications that examine the detection, segmentation, and classi cation of breast cancer.…”

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confidence: 99%

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

katiyar

2024

Preprint

Self Cite

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Breast cancer is a severe health issue that affects women worldwide, 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. Several recent studies have shown that deep convolutional neural networks (DCNNs) can be used to diagnose breast cancer in mammograms with performances comparable to or even superior to those of human experts. To extract attributes from the Mammographic Image Analysis Society (MIAS) dataset, the proposed model uses pretrained convolutional neural network (CNN) architectures such as 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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A Transfer Learning-Based Deep Learning Model for Automated Breast Cancer Identification in Mammograms

katiyar

2024

Preprint

Self Cite

View full text Add to dashboard Cite

Breast cancer poses a significant global health threat to women, underscoring the crucial need for reliable and effective screening approaches. The utilization of computer-aided diagnostic (CAD) systems, leveraging mammograms, enables early detection, diagnosis, and treatment of breast cancer, thereby offering vital support in combating this disease. This study introduces a unique deep-learning model that uses transfer learning to identify and categorize breast cancer automatically. Several recent studies have shown that deep convolutional neural networks (DCNNs) can be used to diagnose breast cancer in mammograms with performances comparable to or even superior to those of human experts. The proposed model extracts features from the Mammographic Image Analysis Society (MIAS) dataset using pre-trained convolutional neural network (CNN) architectures such as ResNet50 and VGG-16. This revolutionary deep-learning model has the potential to improve the efficiency and accuracy of breast cancer detection and categorization.

show abstract

Machine Learning Algorithms for Binary Classification of Breast Cancer

Cited by 3 publications

References 18 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 Transfer Learning-Based Deep Learning Model for Automated Breast Cancer Identification in Mammograms

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