Evaluating Deep and Statistical Machine Learning Models in the Classification of Breast Cancer from Digital Mammograms

Alhussan, Amel Ali; Samee, Nagwan M. Abdel; Ghoneim, Vidan F.; Kadah, Yasser M.

doi:10.14569/ijacsa.2021.0121033

Cited by 9 publications

(18 citation statements)

References 37 publications

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“…To highlight our contribution, this study extends our prior work [16] in constructing an effective CAD system for the categorization of lesions in breast mammograms and contributes the following:…”

Section: Introductionmentioning

confidence: 79%

“…The outcomes of those methods were not appropriated for the accurate classification of breast lesions since they lack high accuracy and sensitivity. However, the developed CAD system based on the classification of ROIs has yielded as high a performance as the work conducted using the classical ML methods [16,75] and the DL based methods [52,63,64,[79][80][81][82][83]. For the MIAS dataset, the proposed LR-PCA-based system outperforms the deep learning systems developed by Ragab et al [29], Alhussan et al [16], and Zhang et al [81].…”

Section: Comparing the Performance And Conclusionmentioning

confidence: 89%

“…A 32 × 32 square region of interest (ROI) was extracted from the lesion. Such an ROI size was selected to directly compare our proposed framework against earlier work that addressed the same classification problem using various techniques [16]. However, the ROI selection method was commonly used for various medical image CAD system pipelines [62,63].…”

Section: Data Preparationmentioning

confidence: 99%

“…This explains why the number of ROIs is larger than the number of images. We tried to avoid overlap to obtain completely independent ROIs and this was followed in previous work [16,62,63]. Regarding the INBreast database, the previous work on this database relied on extracting an ROI of the whole mass and resizing it to the input size of the deep learning model [64].…”

Section: Data Preparationmentioning

confidence: 99%

“…To evaluate the proposed CAD framework, Accuracy (Acc. ), sensitivity (SE), specificity (SP), False Negative Rate (FNR), False Positive Rate (FPR), Area Under the Curve (AUC), Matthews Correlation Coefficient (MCC), and F1-score were used [16,71]. All evaluation metrics were driven using the benefits of confusion matrix.…”

Section: Classical ML Approaches For Roi Classification and Model Eva...mentioning

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: Introductionmentioning

confidence: 79%

Section: Comparing the Performance And Conclusionmentioning

confidence: 89%

Section: Data Preparationmentioning

confidence: 99%

Section: Data Preparationmentioning

confidence: 99%

Section: Classical ML Approaches For Roi Classification and Model Eva...mentioning

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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mCD and Clipped RBM-Based DBN for Optimal Classification of Breast Cancer

Ahlawat

Vinod

2023

Third Congress on Intelligent Systems

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Insight into breast cancer detection: new hybrid feature selection method

Shaban¹

2022

Neural Comput & Applic

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Breast cancer, which is also the leading cause of death among women, is one of the most common forms of the disease that affects females all over the world. The discovery of breast cancer at an early stage is extremely important because it allows selecting appropriate treatment protocol and thus, stops the development of cancer cells. In this paper, a new patients detection strategy has been presented to identify patients with the disease earlier. The proposed strategy composes of two parts which are data preprocessing phase and patient detection phase (PDP). The purpose of this study is to introduce a feature selection methodology for determining the most efficient and significant features for identifying breast cancer patients. This method is known as new hybrid feature selection method (NHFSM). NHFSM is made up of two modules which are quick selection module that uses information gain, and feature selection module that uses hybrid bat algorithm and particle swarm optimization. Consequently, NHFSM is a hybrid method that combines the advantages of bat algorithm and particle swarm optimization based on filter method to eliminate many drawbacks such as being stuck in a local optimal solution and having unbalanced exploitation. The preprocessed data are then used during PDP in order to enable a quick and accurate detection of patients. Based on experimental results, the proposed NHFSM improves the efficiency of patients’ classification in comparison with state-of-the-art feature selection approaches by roughly 0.97, 0.76, 0.75, and 0.716 in terms of accuracy, precision, sensitivity/recall, and F-measure. In contrast, it has the lowest error rate value of 0.03.

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Evaluating Deep and Statistical Machine Learning Models in the Classification of Breast Cancer from Digital Mammograms

Cited by 9 publications

References 37 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

mCD and Clipped RBM-Based DBN for Optimal Classification of Breast Cancer

Insight into breast cancer detection: new hybrid feature selection method

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