Classification of breast tumors as benign and malignant using textural feature descriptor

Sharma, Mukta; Singh, Rahul; Bhattacharya, Mahua

doi:10.1109/bibm.2017.8217811

Cited by 16 publications

(13 citation statements)

References 10 publications

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“…Table 9 provides a comparison of the proposed CNN paired with UED parameter optimization and the alternative methods, such as the single-layer CNN [ 9 ], RF classifier + PFTAS [ 10 ], LeNet-5(Sgdm) [ 11 ], LeNet-5(Adam) [ 12 ], LeNet-5(RMSprop) [ 13 ], and CNN with Taguchi method [ 7 ]. This table illustrates that the accuracy of the optimized network architecture is 84.41%, and it has an accuracy superior to other methods.…”

Section: Resultsmentioning

confidence: 99%

“…Single-layer CNN [9] 77.50 RF classifier + PFTAS [10] 81.28 LeNet-5(Sgdm) [11] 80.69 LeNet-5(Adam) [12] 82.22 LeNet-5(RMSprop) [13] 82.58 CNN with Taguchi method [7] 83.19 Our method 84.41…”

Section: Methods Accuracy (%)mentioning

confidence: 99%

“…The confusion matrix shows 1997 correct classifications (521 of benign and 1476 of malignant) among 2373 validation images, and the AUC is 0.842 in the ROC curve. Table 9 provides a comparison of the proposed CNN paired with UED parameter optimization and the alternative methods, such as the single-layer CNN [9], RF classifier + PFTAS [10], LeNet-5(Sgdm) [11], LeNet-5(Adam) [12], LeNet-5(RMSprop) [13], and CNN with Taguchi method [7]. This Table 9 provides a comparison of the proposed CNN paired with UED parameter optimization and the alternative methods, such as the single-layer CNN [9], RF classifier + PFTAS [10], LeNet-5(Sgdm) [11], LeNet-5(Adam) [12], LeNet-5(RMSprop) [13], and CNN with Taguchi method [7].…”

Section: Conv_1 Conv_2mentioning

confidence: 99%

“…The LeNet-5 system represents an effective network for CNN application, with a high recognition rate. Other deep learning networks include single-layer CNN [11], RF classifier + PFTAS [12], LeNet-5(Sgdm) [13], LeNet-5(Adam) [14], and LeNet-5(RMSprop) [15]. The training of a CNN requires excessive computations with large sample and parameter settings to solve practical problems [16].…”

Section: Introductionmentioning

confidence: 99%

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Optimization of Deep Learning Network Parameters Using Uniform Experimental Design for Breast Cancer Histopathological Image Classification

Lin

Jeng

2020

Diagnostics

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Breast cancer, a common cancer type, is a major health concern in women. Recently, researchers used convolutional neural networks (CNNs) for medical image analysis and demonstrated classification performance for breast cancer diagnosis from within histopathological image datasets. However, the parameter settings of a CNN model are complicated, and using Breast Cancer Histopathological Database data for the classification is time-consuming. To overcome these problems, this study used a uniform experimental design (UED) and optimized the CNN parameters of breast cancer histopathological image classification. In UED, regression analysis was used to optimize the parameters. The experimental results indicated that the proposed method with UED parameter optimization provided 84.41% classification accuracy rate. In conclusion, the proposed method can improve the classification accuracy effectively, with results superior to those of other similar methods.

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Section: Resultsmentioning

confidence: 99%

Section: Methods Accuracy (%)mentioning

confidence: 99%

Section: Conv_1 Conv_2mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimization of Deep Learning Network Parameters Using Uniform Experimental Design for Breast Cancer Histopathological Image Classification

Lin

Jeng

2020

Diagnostics

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“…(15) In addition, a successful application of the CNN is the LeNet-5 system, which consists of two convolutional layers, two pooling layers, and a fully connected layer. (16) The following methods are used for the defect classification of benign tumor and malignant tumor features: singlelayer CNN, (17) rotation forest classifier and a parameter-free version of threshold adjacency statistics, (18) LeNet-5 stochastic gradient descent with momentum, (19) LeNet-5 adaptive moment estimation, (20) and LeNet-5 root mean square propagation. (21) Although traditional methods are capable of segmenting and detecting breast tumors using the Breast Cancer Histopathological Database (BreakHis), classification accuracy is limited.…”

Section: Introductionmentioning

confidence: 99%

Hyperparameter Optimization of Deep Learning Networks for Classification of Breast Histopathology Images

Lin¹,

Jeng²,

Lee³

2021

Sensors and Materials

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After tumor detection in their breasts, women typically fear mastectomy; this affects curative care outcomes. Most tumors are benign. After resection and pathological examination, because of advances in medicine and treatment, the success rate of early breast cancer treatment can reach 60 to 90%. An accurate assessment of tumor extent is essential. In this study, a novel method of hyperparameter optimization of deep learning networks was proposed to classify tumors as malignant or benign. When setting hyperparameters in deep learning networks, most users use trial and error to determine them. In our experiments, the Taguchi method was used to select the impact factors. The orthogonal table design was used to conduct experiments. Then, the best combination of parameters was determined and significant impact factors were analyzed. The Breast Cancer Histopathological Database was used for analysis. This database was built in collaboration with the P&D Laboratory and contains 2480 benign and 5429 malignant samples. The experimental results showed that the proposed method obtained a high accuracy of 83.19%.

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A novel DeepML framework for multi‐classification of breast cancer based on transfer learning

Sharma

Mandloi

Bhattacharya

2022

Int J Imaging Syst Tech

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In the automated diagnosis of breast cancer (BC), microscopic images based on multi-classification play a prominent role. Multi-classification of BC means to differentiate among the sub-categories of BC (papillary carcinoma, ductal carcinoma, fibroadenoma, etc.). However, unpretentious contrasts in various sub-categories of BC occur due to the wide fluctuation of 1) excessive coherency of malignant cells, 2) high definition image appearance, and 3) excessive heterogeneity in color distribution, which makes the task more crucial. Therefore, the automated sub-category discrimination using microscopic images has great medical diagnostic significance yet has not much explored. Thus, the present paper proposes a framework based on machine learning (ML) and deep learning (DL) to multi-classify BC cells into 8 sub-categories. These 8 subcategories comprise four kinds that delineate benigncy, and the other four portray malignancy. More appropriately, both the ML and DL models with the concept of transfer learning have been proposed as DeepML framework to achieve multi-classification of BC using histopathological images. The DeepML framework has achieved distinguished performance (approx. 98% & 89% average accuracy for 90-10% and 80-20% train-test split, respectively) on a wide scale dataset, which intimate the quality of the proposed framework among existing approaches.

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Classification of breast tumors as benign and malignant using textural feature descriptor

Cited by 16 publications

References 10 publications

Optimization of Deep Learning Network Parameters Using Uniform Experimental Design for Breast Cancer Histopathological Image Classification

Optimization of Deep Learning Network Parameters Using Uniform Experimental Design for Breast Cancer Histopathological Image Classification

Hyperparameter Optimization of Deep Learning Networks for Classification of Breast Histopathology Images

A novel DeepML framework for multi‐classification of breast cancer based on transfer learning

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