Applying a Random Projection Algorithm to Optimize Machine Learning Model for Breast Lesion Classification

Heidari, Morteza; Lakshmivarahan, S.; Mirniaharikandehei, Seyedehnafiseh; Danala, Gopichandh; Maryada, Sai Kiran R.; Liu, Hong

doi:10.1109/tbme.2021.3054248

Cited by 20 publications

(12 citation statements)

References 36 publications

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“…First, even though we used a wide range of radiomic features (morphology, density heterogeneity, texture patterns, and wavelets-generated features) for Model-I, more radiomics features can be computed from mammograms and analyzed [ 11 ]. In addition, besides PCA, other feature dimensionality reduction methods (i.e., a locality preserving projection algorithm [ 31 ] and a random projection algorithm [ 9 ]) need to be investigated to build optimal feature vectors. Second, although an adaptive multi-layer topographic region growing algorithm is a simple and relatively robust lesion segmentation algorithm, minor manual correction is needed in a small fraction (<5%) of study cases in this large image dataset.…”

Section: Discussionmentioning

confidence: 99%

“…All FFDM images were acquired using Hologic Selenia (Hologic Inc., Bedford, MA, USA) digital mammography machines, which have a fixed pixel size of 70 μm. The detailed patients’ demographic information, breast density distribution, and other image characteristics were reported in our previous studies [ 9 , 18 ]. In this study, we selected 3000 FFDM images from this existing database to assemble a specific dataset for this study.…”

Section: Methodsmentioning

confidence: 99%

“…The examples of ROIs with zero-pad correction will be demonstrated in the sample ROIs in the Results section of the paper. The same sized ROI or patch has been affectively used in our previous CAD studies (i.e., [ 8 , 9 ]).…”

Section: Methodsmentioning

confidence: 99%

“…For CAD of mammograms, the computer-aided detection (CADe) schemes of suspicious lesion detection have been implemented in many medical centers or hospitals to assist radiologists in reading screening mammograms [ 7 ]. However, although great research effort has been made to develop computer-aided diagnosis (CADx) schemes of lesion classification [ 8 , 9 ], no CADx schemes have been approved and accepted in clinical practice. In this study, we focus on developing computer-aided diagnosis schemes of mammograms in order to help improve accuracy of lesion classification.…”

Section: Introductionmentioning

confidence: 99%

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A Comparison of Computer-Aided Diagnosis Schemes Optimized Using Radiomics and Deep Transfer Learning Methods

et al. 2022

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Objective: Radiomics and deep transfer learning are two popular technologies used to develop computer-aided detection and diagnosis (CAD) schemes of medical images. This study aims to investigate and to compare the advantages and the potential limitations of applying these two technologies in developing CAD schemes. Methods: A relatively large and diverse retrospective dataset including 3000 digital mammograms was assembled in which 1496 images depicted malignant lesions and 1504 images depicted benign lesions. Two CAD schemes were developed to classify breast lesions. The first scheme was developed using four steps namely, applying an adaptive multi-layer topographic region growing algorithm to segment lesions, computing initial radiomics features, applying a principal component algorithm to generate an optimal feature vector, and building a support vector machine classifier. The second CAD scheme was built based on a pre-trained residual net architecture (ResNet50) as a transfer learning model to classify breast lesions. Both CAD schemes were trained and tested using a 10-fold cross-validation method. Several score fusion methods were also investigated to classify breast lesions. CAD performances were evaluated and compared by the areas under the ROC curve (AUC). Results: The ResNet50 model-based CAD scheme yielded AUC = 0.85 ± 0.02, which was significantly higher than the radiomics feature-based CAD scheme with AUC = 0.77 ± 0.02 (p < 0.01). Additionally, the fusion of classification scores generated by the two CAD schemes did not further improve classification performance. Conclusion: This study demonstrates that using deep transfer learning is more efficient to develop CAD schemes and it enables a higher lesion classification performance than CAD schemes developed using radiomics-based technology.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Comparison of Computer-Aided Diagnosis Schemes Optimized Using Radiomics and Deep Transfer Learning Methods

et al. 2022

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“…In this study, we assembled a retrospective image dataset from an existing mammography database previously collected in our medical imaging laboratory, which has been used to develop several CAD schemes reported in our previous research papers (i.e., [ 27 , 28 , 29 ]). The dataset used in this study includes 4280 mammograms.…”

Section: Methodsmentioning

confidence: 99%

Improving Performance of Breast Lesion Classification Using a ResNet50 Model Optimized with a Novel Attention Mechanism

Islam

Jones²,

Faiz³

et al. 2022

Tomography

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Background: The accurate classification between malignant and benign breast lesions detected on mammograms is a crucial but difficult challenge for reducing false-positive recall rates and improving the efficacy of breast cancer screening. Objective: This study aims to optimize a new deep transfer learning model by implementing a novel attention mechanism in order to improve the accuracy of breast lesion classification. Methods: ResNet50 is selected as the base model to develop a new deep transfer learning model. To enhance the accuracy of breast lesion classification, we propose adding a convolutional block attention module (CBAM) to the standard ResNet50 model and optimizing a new model for this task. We assembled a large dataset with 4280 mammograms depicting suspicious soft-tissue mass-type lesions. A region of interest (ROI) is extracted from each image based on lesion center. Among them, 2480 and 1800 ROIs depict verified benign and malignant lesions, respectively. The image dataset is randomly split into two subsets with a ratio of 9:1 five times to train and test two ResNet50 models with and without using CBAM. Results: Using the area under ROC curve (AUC) as an evaluation index, the new CBAM-based ResNet50 model yields AUC = 0.866 ± 0.015, which is significantly higher than that obtained by the standard ResNet50 model (AUC = 0.772 ± 0.008) (p < 0.01). Conclusion: This study demonstrates that although deep transfer learning technology attracted broad research interest in medical-imaging informatic fields, adding a new attention mechanism to optimize deep transfer learning models for specific application tasks can play an important role in further improving model performances.

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A Deep Learning Approach to Distinguish Normal Pneumonia and COVID-19 Pneumonia with Segmentation of Affected Areas of Chest X-Ray Images

Chakraborty,

Fahmida,

Habibur Rahman

et al. 2023

Algorithms for Intelligent Systems

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Applying a Random Projection Algorithm to Optimize Machine Learning Model for Breast Lesion Classification

Cited by 20 publications

References 36 publications

A Comparison of Computer-Aided Diagnosis Schemes Optimized Using Radiomics and Deep Transfer Learning Methods

A Comparison of Computer-Aided Diagnosis Schemes Optimized Using Radiomics and Deep Transfer Learning Methods

Improving Performance of Breast Lesion Classification Using a ResNet50 Model Optimized with a Novel Attention Mechanism

A Deep Learning Approach to Distinguish Normal Pneumonia and COVID-19 Pneumonia with Segmentation of Affected Areas of Chest X-Ray Images

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