Effective Breast Cancer Recognition Based on Fine-Grained Feature Selection

Li, Guangli; Yuan, Tian; Li, Chuanxiu; Zhuo, Jianwu; Jiang, Ziliang; Wu, Jinpeng; Ji, Donghong; Zhang, Hongbin

doi:10.1109/access.2020.3046309

Cited by 10 publications

(3 citation statements)

References 46 publications

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“…Color normalization followed by enhancement was carried out as preprocessing step. Refinement (R), correlation (C) and adaptive (A) algorithms were used for fine-tuning the features and classification by AdaBoost-oriented tree model [11].…”

Section: Literature Surveymentioning

confidence: 99%

Design of ensemble recurrent model with stacked fuzzy ARTMAP for breast cancer detection

Das

Mohanty

2022

ACI

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PurposeIn time and accurate detection of cancer can save the life of the person affected. According to the World Health Organization (WHO), breast cancer occupies the most frequent incidence among all the cancers whereas breast cancer takes fifth place in the case of mortality numbers. Out of many image processing techniques, certain works have focused on convolutional neural networks (CNNs) for processing these images. However, deep learning models are to be explored well.Design/methodology/approachIn this work, multivariate statistics-based kernel principal component analysis (KPCA) is used for essential features. KPCA is simultaneously helpful for denoising the data. These features are processed through a heterogeneous ensemble model that consists of three base models. The base models comprise recurrent neural network (RNN), long short-term memory (LSTM) and gated recurrent unit (GRU). The outcomes of these base learners are fed to fuzzy adaptive resonance theory mapping (ARTMAP) model for decision making as the nodes are added to the F_2ˆa layer if the winning criteria are fulfilled that makes the ARTMAP model more robust.FindingsThe proposed model is verified using breast histopathology image dataset publicly available at Kaggle. The model provides 99.36% training accuracy and 98.72% validation accuracy. The proposed model utilizes data processing in all aspects, i.e. image denoising to reduce the data redundancy, training by ensemble learning to provide higher results than that of single models. The final classification by a fuzzy ARTMAP model that controls the number of nodes depending upon the performance makes robust accurate classification.Research limitations/implicationsResearch in the field of medical applications is an ongoing method. More advanced algorithms are being developed for better classification. Still, the scope is there to design the models in terms of better performance, practicability and cost efficiency in the future. Also, the ensemble models may be chosen with different combinations and characteristics. Only signal instead of images may be verified for this proposed model. Experimental analysis shows the improved performance of the proposed model. This method needs to be verified using practical models. Also, the practical implementation will be carried out for its real-time performance and cost efficiency.Originality/valueThe proposed model is utilized for denoising and to reduce the data redundancy so that the feature selection is done using KPCA. Training and classification are performed using heterogeneous ensemble model designed using RNN, LSTM and GRU as base classifiers to provide higher results than that of single models. Use of adaptive fuzzy mapping model makes the final classification accurate. The effectiveness of combining these methods to a single model is analyzed in this work.

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Section: Literature Surveymentioning

confidence: 99%

Design of ensemble recurrent model with stacked fuzzy ARTMAP for breast cancer detection

Das

Mohanty

2022

ACI

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“…One of the deadliest types of cancer is breast cancer. Breast cancer affects both men and women, but is more common in women and very rare in men [3], [4]. The level of heterogeneity in breast cancer is high.…”

Section: Introductionmentioning

confidence: 99%

Ensemble learning technique to improve breast cancer classification model

Dullah,

Apsari,

Jumanto

2023

J. Soft Comput. Explor.

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Cancer is a disease characterized by abnormal cell growth and is not contagious, such as breast cancer which can affect both men and women. breast cancer is one of the cancer diseases that is classified as dangerous and takes many victims. However, the biggest problem in this study is that the classification method is low and the resulting accuracy is less than optimal. the purpose of this study is to improve the accuracy of breast cancer classification. Therefore, a new method is proposed, namely ensemble learning which combines logistic regression, decision tree, and random forest methods, with a voting system. This system is useful for finding the best results on each parameter that will produce the best prediction accuracy. The prediction results from this method reached an accuracy of 98.24%. The resulting accuracy rate is more optimal by using the proposed method.

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“…0.902, 0.495, and 0.644, respectively need to be improved. Feature extraction using deep learning-based models like ResNet, DenseNet, VGG-16, and also using SIFT, GIST, HOG, and local binary pattern(LBP) is found in [13], where the features are further processed to obtain fine-tuned features. Though the feature selection is focused in a complex manner, the final classification accuracy is also needed to be improved.…”

Section: Introductionmentioning

confidence: 99%