An Automatic Breast Cancer Diagnostic System Based on Mammographic Images Using Convolutional Neural Network Classifier

Jubeen, Maria; Rahman, Ata Ur; Wahid, Samiya Abdul; Imran, Azhar; Yasin, Amanullah; Ihsan, Imran

doi:10.56979/401/2022/93

Cited by 2 publications

(2 citation statements)

References 16 publications

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“…For a wide range of computer vision applications [97], convolutional neural networks (CNNs) [98][99][100] have become a dominant deep learning model. Given their efficiency in learning hierarchical features from CT images, CNNs play a crucial role in computed tomography reconstruction.…”

Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

A Systematic Literature Review of 3D Deep Learning Techniques in Computed Tomography Reconstruction

Rahman,

Khan,

Sadiq

et al. 2023

Tomography

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Computed tomography (CT) is used in a wide range of medical imaging diagnoses. However, the reconstruction of CT images from raw projection data is inherently complex and is subject to artifacts and noise, which compromises image quality and accuracy. In order to address these challenges, deep learning developments have the potential to improve the reconstruction of computed tomography images. In this regard, our research aim is to determine the techniques that are used for 3D deep learning in CT reconstruction and to identify the training and validation datasets that are accessible. This research was performed on five databases. After a careful assessment of each record based on the objective and scope of the study, we selected 60 research articles for this review. This systematic literature review revealed that convolutional neural networks (CNNs), 3D convolutional neural networks (3D CNNs), and deep learning reconstruction (DLR) were the most suitable deep learning algorithms for CT reconstruction. Additionally, two major datasets appropriate for training and developing deep learning systems were identified: 2016 NIH-AAPM-Mayo and MSCT. These datasets are important resources for the creation and assessment of CT reconstruction models. According to the results, 3D deep learning may increase the effectiveness of CT image reconstruction, boost image quality, and lower radiation exposure. By using these deep learning approaches, CT image reconstruction may be made more precise and effective, improving patient outcomes, diagnostic accuracy, and healthcare system productivity.

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Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

A Systematic Literature Review of 3D Deep Learning Techniques in Computed Tomography Reconstruction

Rahman,

Khan,

Sadiq

et al. 2023

Tomography

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“…Early detection of breast cancer is crucial for accurate diagnosis and analysis, and many researchers are turning to biomedical imaging to aid specialist radiologists. Various methods such as MRI, mammography, and ultrasound are utilized to identify breast carcinoma [25,26]. However, the large volume of images challenges radiologists in identifying potential cancerous areas.…”

Section: Introductionmentioning

confidence: 99%

Efficient Breast Cancer Diagnosis from Complex Mammographic Images Using Deep Convolutional Neural Network

Bukht

Ahmad²,

Almadhor

et al. 2023

Computational Intelligence and Neuroscience

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Medical image analysis places a significant focus on breast cancer, which poses a significant threat to women’s health and contributes to many fatalities. An early and precise diagnosis of breast cancer through digital mammograms can significantly improve the accuracy of disease detection. Computer-aided diagnosis (CAD) systems must analyze the medical imagery and perform detection, segmentation, and classification processes to assist radiologists with accurately detecting breast lesions. However, early-stage mammography cancer detection is certainly difficult. The deep convolutional neural network has demonstrated exceptional results and is considered a highly effective tool in the field. This study proposes a computational framework for diagnosing breast cancer using a ResNet-50 convolutional neural network to classify mammogram images. To train and classify the INbreast dataset into benign or malignant categories, the framework utilizes transfer learning from the pretrained ResNet-50 CNN on ImageNet. The results revealed that the proposed framework achieved an outstanding classification accuracy of 93%, surpassing other models trained on the same dataset. This novel approach facilitates early diagnosis and classification of malignant and benign breast cancer, potentially saving lives and resources. These outcomes highlight that deep convolutional neural network algorithms can be trained to achieve highly accurate results in various mammograms, along with the capacity to enhance medical tools by reducing the error rate in screening mammograms.

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An Automatic Breast Cancer Diagnostic System Based on Mammographic Images Using Convolutional Neural Network Classifier

Cited by 2 publications

References 16 publications

A Systematic Literature Review of 3D Deep Learning Techniques in Computed Tomography Reconstruction

A Systematic Literature Review of 3D Deep Learning Techniques in Computed Tomography Reconstruction

Efficient Breast Cancer Diagnosis from Complex Mammographic Images Using Deep Convolutional Neural Network

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