Breast mass characterization using sparse approximations of patch-sampled deep features

Harris, C; Makrogiannis, Sokratis

doi:10.1117/12.2653926

Cited by 2 publications

(1 citation statement)

References 15 publications

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“…PatchSample decomposition is a revolutionary technique Harris et al. ( 31 ) devised for learning sparse approximations and making classification judgments. In contrast to BlockBoost, the prior method, PatchSample, builds larger dictionaries that encompass a wider variety of visual data from every point inside the region of interest (ROI) and spatially specific information.…”

Section: Literature Reviewmentioning

confidence: 99%

A novel fusion framework of deep bottleneck residual convolutional neural network for breast cancer classification from mammogram images

Jabeen,

Khan,

Hameed

et al. 2024

Front. Oncol.

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With over 2.1 million new cases of breast cancer diagnosed annually, the incidence and mortality rate of this disease pose severe global health issues for women. Identifying the disease’s influence is the only practical way to lessen it immediately. Numerous research works have developed automated methods using different medical imaging to identify BC. Still, the precision of each strategy differs based on the available resources, the issue’s nature, and the dataset being used. We proposed a novel deep bottleneck convolutional neural network with a quantum optimization algorithm for breast cancer classification and diagnosis from mammogram images. Two novel deep architectures named three-residual blocks bottleneck and four-residual blocks bottle have been proposed with parallel and single paths. Bayesian Optimization (BO) has been employed to initialize hyperparameter values and train the architectures on the selected dataset. Deep features are extracted from the global average pool layer of both models. After that, a kernel-based canonical correlation analysis and entropy technique is proposed for the extracted deep features fusion. The fused feature set is further refined using an optimization technique named quantum generalized normal distribution optimization. The selected features are finally classified using several neural network classifiers, such as bi-layered and wide-neural networks. The experimental process was conducted on a publicly available mammogram imaging dataset named INbreast, and a maximum accuracy of 96.5% was obtained. Moreover, for the proposed method, the sensitivity rate is 96.45, the precision rate is 96.5, the F1 score value is 96.64, the MCC value is 92.97%, and the Kappa value is 92.97%, respectively. The proposed architectures are further utilized for the diagnosis process of infected regions. In addition, a detailed comparison has been conducted with a few recent techniques showing the proposed framework’s higher accuracy and precision rate.

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Section: Literature Reviewmentioning

confidence: 99%

A novel fusion framework of deep bottleneck residual convolutional neural network for breast cancer classification from mammogram images

Jabeen,

Khan,

Hameed

et al. 2024

Front. Oncol.

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Spatially localized sparse approximations of deep features for breast mass characterization

Harris,

Okorie,

Makrogiannis

2023

MBE

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<abstract><p>We propose a deep feature-based sparse approximation classification technique for classification of breast masses into benign and malignant categories in film screen mammographs. This is a significant application as breast cancer is a leading cause of death in the modern world and improvements in diagnosis may help to decrease rates of mortality for large populations. While deep learning techniques have produced remarkable results in the field of computer-aided diagnosis of breast cancer, there are several aspects of this field that remain under-studied. In this work, we investigate the applicability of deep-feature-generated dictionaries to sparse approximation-based classification. To this end we construct dictionaries from deep features and compute sparse approximations of Regions Of Interest (ROIs) of breast masses for classification. Furthermore, we propose block and patch decomposition methods to construct overcomplete dictionaries suitable for sparse coding. The effectiveness of our deep feature spatially localized ensemble sparse analysis (DF-SLESA) technique is evaluated on a merged dataset of mass ROIs from the CBIS-DDSM and MIAS datasets. Experimental results indicate that dictionaries of deep features yield more discriminative sparse approximations of mass characteristics than dictionaries of imaging patterns and dictionaries learned by unsupervised machine learning techniques such as K-SVD. Of note is that the proposed block and patch decomposition strategies may help to simplify the sparse coding problem and to find tractable solutions. The proposed technique achieves competitive performances with state-of-the-art techniques for benign/malignant breast mass classification, using 10-fold cross-validation in merged datasets of film screen mammograms.</p></abstract>

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Breast mass characterization using sparse approximations of patch-sampled deep features

Cited by 2 publications

References 15 publications

A novel fusion framework of deep bottleneck residual convolutional neural network for breast cancer classification from mammogram images

A novel fusion framework of deep bottleneck residual convolutional neural network for breast cancer classification from mammogram images

Spatially localized sparse approximations of deep features for breast mass characterization

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