Microcalcifications Enhancement in Digital Mammograms using Fractal Modeling

Mohamed, Wael A.; Alolfe, M.A.; Kadah, Yasser M.

doi:10.1109/cibec.2008.4786034

Cited by 6 publications

(9 citation statements)

References 10 publications

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“…These solutions can be classified into classical and deep learning models. Early CAD systems have been developed using the classical machine learning models such as the research conducted in [18][19][20][21][22][23][24][25][26][27][28][29][30]. The basic framework in these studies consists of three stages including Region of Interest (ROI) extraction, feature extraction and selection, and classification stage using well-known classifiers such as K Nearest Neighbor (KNN), Support Vector Machine (SVM), Neural Network (NN), and Ensemble classifiers such as the work conducted by Kadah et al [18,21,24,25,31].…”

Section: Related Workmentioning

confidence: 99%

“…Early CAD systems have been developed using the classical machine learning models such as the research conducted in [18][19][20][21][22][23][24][25][26][27][28][29][30]. The basic framework in these studies consists of three stages including Region of Interest (ROI) extraction, feature extraction and selection, and classification stage using well-known classifiers such as K Nearest Neighbor (KNN), Support Vector Machine (SVM), Neural Network (NN), and Ensemble classifiers such as the work conducted by Kadah et al [18,21,24,25,31]. In [18], a CAD system that can help radiologists diagnose microcalcification patterns in digitized mammograms earlier and faster than traditional screening systems has been introduced.…”

Section: Related Workmentioning

confidence: 99%

“…Classification of the normal/abnormal, and microcalcification masses has been accomplished on the MIAS benchmarking dataset. In [21], an enhancement in the classifier performance of the normal/microcalcification of breast tissue has been performed by the utilization of voting-based KNN and SVM classification approaches. A hybrid approach of SVM and Linear Discriminant Analysis (LDA) has been utilized in designing a CAD system for breast lesion classification in [22].…”

Section: Related Workmentioning

confidence: 99%

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A Hybrid Deep Transfer Learning of CNN-Based LR-PCA for Breast Lesion Diagnosis via Medical Breast Mammograms

Alhussan

Ghoneim

Atteia

et al. 2022

Sensors

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One of the most promising research areas in the healthcare industry and the scientific community is focusing on the AI-based applications for real medical challenges such as the building of computer-aided diagnosis (CAD) systems for breast cancer. Transfer learning is one of the recent emerging AI-based techniques that allow rapid learning progress and improve medical imaging diagnosis performance. Although deep learning classification for breast cancer has been widely covered, certain obstacles still remain to investigate the independency among the extracted high-level deep features. This work tackles two challenges that still exist when designing effective CAD systems for breast lesion classification from mammograms. The first challenge is to enrich the input information of the deep learning models by generating pseudo-colored images instead of only using the input original grayscale images. To achieve this goal two different image preprocessing techniques are parallel used: contrast-limited adaptive histogram equalization (CLAHE) and Pixel-wise intensity adjustment. The original image is preserved in the first channel, while the other two channels receive the processed images, respectively. The generated three-channel pseudo-colored images are fed directly into the input layer of the backbone CNNs to generate more powerful high-level deep features. The second challenge is to overcome the multicollinearity problem that occurs among the high correlated deep features generated from deep learning models. A new hybrid processing technique based on Logistic Regression (LR) as well as Principal Components Analysis (PCA) is presented and called LR-PCA. Such a process helps to select the significant principal components (PCs) to further use them for the classification purpose. The proposed CAD system has been examined using two different public benchmark datasets which are INbreast and mini-MAIS. The proposed CAD system could achieve the highest performance accuracies of 98.60% and 98.80% using INbreast and mini-MAIS datasets, respectively. Such a CAD system seems to be useful and reliable for breast cancer diagnosis.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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A Hybrid Deep Transfer Learning of CNN-Based LR-PCA for Breast Lesion Diagnosis via Medical Breast Mammograms

Alhussan

Ghoneim

Atteia

et al. 2022

Sensors

Self Cite

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“…Because of the increased computing power of today’s graphics processing units (GPUs), such models may contain thousands or even tens of thousands of features that have been concealed from view. The detection of possibly malignant tumors in medical imagery is one of the most common applications of deep learning in the healthcare industry [ 6 , 39 , 40 , 41 , 42 , 43 , 44 , 45 ]. Radiomics, the detection of clinically significant patterns in imaging data that are invisible to the naked eye, is becoming an increasingly popular use of deep learning [ 46 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Clinical Decision Support Framework for Segmentation and Classification of Brain Tumor MRIs Using a U-Net and DCNN Cascaded Learning Algorithm

et al. 2022

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Brain tumors (BTs) are an uncommon but fatal kind of cancer. Therefore, the development of computer-aided diagnosis (CAD) systems for classifying brain tumors in magnetic resonance imaging (MRI) has been the subject of many research papers so far. However, research in this sector is still in its early stage. The ultimate goal of this research is to develop a lightweight effective implementation of the U-Net deep network for use in performing exact real-time segmentation. Moreover, a simplified deep convolutional neural network (DCNN) architecture for the BT classification is presented for automatic feature extraction and classification of the segmented regions of interest (ROIs). Five convolutional layers, rectified linear unit, normalization, and max-pooling layers make up the DCNN’s proposed simplified architecture. The introduced method was verified on multimodal brain tumor segmentation (BRATS 2015) datasets. Our experimental results on BRATS 2015 acquired Dice similarity coefficient (DSC) scores, sensitivity, and classification accuracy of 88.8%, 89.4%, and 88.6% for high-grade gliomas. When it comes to segmenting BRATS 2015 BT images, the performance of our proposed CAD framework is on par with existing state-of-the-art methods. However, the accuracy achieved in this study for the classification of BT images has improved upon the accuracy reported in prior studies. Image classification accuracy for BRATS 2015 BT has been improved from 88% to 88.6%.

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“…Networks [2], [14], [20], [27], Nearest Neighbor [30], Fuzzy [25], [31] and Support Vector Machine (SVM) [3], [7], [13], [15], [30], [32]. Timp and Karssemeijer [1], developed CAD techniques to study interval changes between two consecutive mammographic screening rounds.…”

Section: Page 4 Of 41mentioning

confidence: 99%

Breast cancer detection and classification in digital mammography based on Non-Subsampled Contourlet Transform (NSCT) and Super Resolution

Pak

Kanan

Alikhassi

2015

Computer Methods and Programs in Biomedicine

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Microcalcifications Enhancement in Digital Mammograms using Fractal Modeling

Cited by 6 publications

References 10 publications

A Hybrid Deep Transfer Learning of CNN-Based LR-PCA for Breast Lesion Diagnosis via Medical Breast Mammograms

A Hybrid Deep Transfer Learning of CNN-Based LR-PCA for Breast Lesion Diagnosis via Medical Breast Mammograms

Clinical Decision Support Framework for Segmentation and Classification of Brain Tumor MRIs Using a U-Net and DCNN Cascaded Learning Algorithm

Breast cancer detection and classification in digital mammography based on Non-Subsampled Contourlet Transform (NSCT) and Super Resolution

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