King Abdulaziz University Breast Cancer Mammogram Dataset (KAU-BCMD)

Alsolami, Asmaa S.; Shalash, Wafaa M.; Alsaggaf, Wafaa; Ashoor, Sawsan; Refaat, Haneen; Elmogy, Mohammed

doi:10.3390/data6110111

Cited by 24 publications

(3 citation statements)

References 35 publications

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“…This dataset includes images with numerous breast abnormalities, such as masses, calcifications, and asymmetries, along with detailed annotations and ground truth labels provided by expert radiologists. Over and above, the King Abdulaziz University Breast Cancer Mammographic Dataset (KAU-BCMD ) [92] (https://www.kaggle.com/datasets/asmaasaad/king-abdulaziz-university-mammogram-dataset)is a mammographic image dataset designed to support research in breast cancer detection and diagnosis. This dataset consists of a collection of digital mammograms accompanied by detailed annotations and ground truth labels provided by three radiologists.…”

Section: Datasets For Mammographymentioning

confidence: 99%

Breast Cancer Detection using Mammography: Image Processing to Deep Learning

Qureshi,

Rehman,

Hussain

et al. 2024

Preprint

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Breast cancer stands as a predominant health concern for women globally. As mammography is the primary screening tool for breast cancer detection, improving the detection of breast cancer at screening could save more lives. This mammography re-view paper provides a systematic review of computer-aided techniques during a specific time frame for the segmentation and classification of microcalcification, evaluating image processing, machine learning, and deep learning techniques. The systematic review is meticulously carried out, adhering closely to the preferred reporting items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. This article focuses on mammographic breast cancer detection approaches based on automated systems, discussed chronologically from 1970 through 2023. This article encompasses the breadth of artificial intelligence-based methods from the most primitive to the most sophisticated models. Image processing and machine learning-based methods are compre-hensively reviewed. The evaluation of a deep learning architecture based on self-extracted features for classification tasks demonstrated outclass performance. Large-scale datasets required for a broader and in-depth analysis of novel methods for breast cancer detection are also discussed in this article. This research work is aligned with the United Nations' sustainability de-velopment goals.

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Section: Datasets For Mammographymentioning

confidence: 99%

Breast Cancer Detection using Mammography: Image Processing to Deep Learning

Qureshi,

Rehman,

Hussain

et al. 2024

Preprint

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“…Both classes are extracted, and all the histogram features are selected. The KAUBCMD (22) clinical dataset was released by the clinical experts for breast cancer classification purposes in six categories. The improvised Firefly model and the Deep CNN (23,24)…”

Section: Introductionmentioning

confidence: 99%

Enhancing Breast Cancer Data Classification Performance in Medical Data by Employing GRU-LMA Deep Learning Classifier for Accurate Diagnosis

Chandar,

Krishnapriya

2024

IJST

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Objectives: To suggest a new data classifier to enhance breast cancer data classification in mammography digital images (MDI) in order to reduce the error rate and training time and improve accuracy in breast cancer prediction. A deep learning approach is utilized to identify the significant deep spots in MDI for accurate diagnosis. Methods: The Gated Recurrent Unit (GRU)-based DL approach is employed to speed up the learning phase in the DRNN classifier, and the Levenberg-Marquardt algorithm (LMA) is used to identify and reduce errors during classification. For feature extraction, noise removal, & filtering, Adaptive Median Filtering (AMF) and Tetrolet transform (TTM) algorithms are utilized, and the features are separated into two categories (morphology & texture). The King Abdulaziz University Breast Cancer Mammogram Dataset (KAUBCMD) dataset is used for this research work. KAUBCMD has 1416 case reports, with 2 views for both the right and left breasts, and 5662 MDI with clinical results of 3 target classes (negative, benign, and malignant) and 2 subclasses (incomplete and suspected malignant). MATLAB tool is used to evaluate the DL-based GRU-LMA approach based on performance metrics. The detailed comparative analysis is done with prevailing classifier models such as linear SVM, KNN-HP, and K-Means GRI. Findings: The suggested DL-based GRU-LMA classifier model shows proven results with 95.19% accuracy, 96.03% TPR and 95.09% TNR, 95.07% sensitivity, 97.81% specificity, 96% precision, 97.03% recall, 17 minutes of training time, 3% error rate, 3% FPR, and 4% FNR. GRU-LMA outperforms the current classifier models L-SVM, KNN-HP, and K-Means GRI. Novelty: The proven results show breast cancer prediction and classification accuracy with minimal time and error rate, which helps the radiologist and other BC clinical experts make diagnoses easily. The GRU-LMA addresses the limitations of the prevailing classifier models, L-SVM, KNN-HP, and K-Means GRI.https://www.indjst.org/

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“…The study also discusses the significant variation in breast anatomy and tumour growth probability based on race. Alsolami et al (2021) (7) examined the KAU-BCMD dataset, re-examining 1416 cases of images 5662 and 405 ultrasound images from 205 patients, with three radiologists annotating the data set. Zhu et al (2020) (8) analyzed deep learning methodologies in cancer diagnosis and prediction, focusing on accuracy, cost reduction, and application issues in cancer diagnosis systems.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Breast Cancer Detection: Deep Transfer Learning Empowered by SVM Classifiers

Rao,

Ramakrishna,

Prasad

et al. 2024

IJST

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Objectives: The research aims to enhance breast cancer detection accuracy and effectiveness using deep transfer learning and pre-trained neural networks. It analyses breast ultrasound images and identifies important characteristics using pre-trained networks. The goal is to create a more efficient and accurate automated system for breast cancer detection. Methods: The study uses breast ultrasound cancer image data from the Kaggle Data Repository to extract informative features, identify cancer-related characteristics, and classify them into benign, malignant, and normal tissue. Pre-trained Deep Neural Networks (DNNs) extract these features and feed them into a 10-fold cross-validation SVM classifier. The SVM is evaluated using various kernel functions to identify the best kernel for separating data points. This methodology aims to achieve accurate classification of breast cancer in ultrasound images. Findings: The study confirms the effectiveness of deep transfer learning for breast cancer detection in ultrasound images, with Inception V3 outperforming VGG-16 and VGG-19 in extracting relevant features. The combination of Inception V3 and the SVM classifier with a polynomial kernel achieved the highest classification accuracy, indicating its ability to model complex relationships. The study demonstrated an AUC of 0.944 and a classification accuracy of 87.44% using the Inception V3 + SVM polynomial. Novelty: This research demonstrates the potential of deep transfer learning and SVM classifiers for accurate breast cancer detection in ultrasound images. It integrates Inception V3, VGG-16, and VGG-19 for breast cancer detection, demonstrating improved classification accuracy. The combination of Inception V3 and SVM (polynomial) achieved a significant AUC (0.944) and classification accuracy (87.44%), outperforming other models tested. This research underscores the potential of these technologies for accurate breast cancer detection in ultrasound images. Keywords: Breast Cancer, Deep Learning, Feature Extraction, Inception-v3, SVM, Transfer Learning

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King Abdulaziz University Breast Cancer Mammogram Dataset (KAU-BCMD)

Cited by 24 publications

References 35 publications

Breast Cancer Detection using Mammography: Image Processing to Deep Learning

Breast Cancer Detection using Mammography: Image Processing to Deep Learning

Enhancing Breast Cancer Data Classification Performance in Medical Data by Employing GRU-LMA Deep Learning Classifier for Accurate Diagnosis

Optimizing Breast Cancer Detection: Deep Transfer Learning Empowered by SVM Classifiers

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