Breast Cancer Diagnosis Using an Unsupervised Feature Extraction Algorithm Based on Deep Learning

Xiao, Yawen; Wu, Jun; Lin, Zongli; Zhao, Xiaodong

doi:10.23919/chicc.2018.8483140

Cited by 19 publications

(5 citation statements)

References 10 publications

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“…The author used the CBISDDSM dataset. The research based on the unsupervised feature extraction approach that was based on deep learning was proposed by Xiao et al (2018). This method was used solely for feature extraction.…”

Section: Literature Reviewmentioning

confidence: 99%

Various Deep Learning Techniques Involved In Breast Cancer Mammogram Classification – A Survey

Dronamraju

2022

IJIE

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The most common and rapidly spreading disease in the world is breast cancer. Most cases of breast cancer are observed in females. Breast cancer can be controlled with early detection. Early discovery helps to manage a lot of cases and lower the death rate. On breast cancer, numerous studies have been conducted. Machine learning is the method that is utilized in research the most frequently. There have been a lot of earlier machine learning-based studies. Decision trees, KNN, SVM, naive bays, and other machine learning algorithms perform better in their respective fields. However, a newly created method is now being utilized to categorize breast cancer. Deep learning is a recently developed method. The limitations of machine learning are solved through deep learning. Convolution neural networks, recurrent neural networks, deep belief networks, and other deep learning techniques are frequently utilized in data science. Deep learning algorithms perform better than machine learning algorithms. The best aspects of the images are extracted. CNN is employed in our study to categorize the photos. Basically, CNN is the most widely used technique to categorize images, on which our research is based.

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Section: Literature Reviewmentioning

confidence: 99%

Various Deep Learning Techniques Involved In Breast Cancer Mammogram Classification – A Survey

Dronamraju

2022

IJIE

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“…Feature Extraction (FE) is the process of transforming the input image into a set of features. The main aim of FE is to decrease the number of features in a dataset by producing new features from the existing features and then removing the original features [33]. In fact, FE process is performed before using the classification model.…”

Section: Feature Extraction Stagementioning

confidence: 99%

“…These techniques are; Gabor filter, co-occurrence matrix, wavelet transform based-features, etc. [33]. Actually, Gray Level Co-occurrence Matrix (GLCM) is the most popular, robust method used to convert the input image into a set of features [34,35].…”

Section: Feature Extraction Stagementioning

confidence: 99%

Classification of Breast Cancer Using Neutrosophic Techniques and Deep Neural Network

Shaban¹

2021

Preprint

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Breast cancer is one of the most common types of cancer that affects women globally and it is the primary cause of death. Early detection of breast cancer is a vital process that can facilitate appropriate treatment, stop the progression of cancer cells, and reduce morbidity and mortality. Artificial Intelligence (AI) and Machine Learning (ML) are the most popular methods that can be used to detect and classify breast cancer accurately. In this paper, a new strategy for classifying breast cancer using Neutrosophic Techniques (NTs) and machine learning techniques is introduced, which is called Breast Cancer Classification Strategy (BC2S). The proposed BC2S consists of two phases, which are; Data Preprocessing Phase (DP2) and Breast Cancer Classification Phase (BC2P). The main aim of the data preprocessing phase is to; (i) extract features from mammogram images and then remove the outlier items, (ii) select the most effective and informative features from those extracted features using new feature selection method called Efficient Ant Colony Optimization (EACO), and (iii) convert the selected features from classical domain into neutrosophic domain using NTs to give accurate classification through the next classification phase BC2P. The proposed classification model uses Deep Neural Network (DNN) to determine whether the patient is normal or infected with benign or malignant cancer. According to experimental results, the proposed strategy outperforms other competitors in terms of accuracy, precision, recall, and F-measure.

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“…Method Accuracy (%) [38] PCA-AE-Ada 85 [39] ACO-SVM 95.98 [35] GA-SVM 97.19 [35] PSO-SVM 97.37 [26] GA-MOO-NN 98.85 [14] PCA-SVM 96.84 [40] Breast cancer diagnosis techniques using SVM, PNN, and MLP 97.80 [11] Classification system using fuzzy-GA method 97.36 [20] Classification system using mixture ensemble of convolutional neural network 96.39 [41] SAE-SVM 98.25 [42] Prediction of breast cancer using SVM and K-NN 98.57 [43] Breast…”

Section: Referencementioning

confidence: 99%

Breast Cancer Detection in the IOT Health Environment Using Modified Recursive Feature Selection

Memon

Haq

et al. 2019

Wireless Communications and Mobile Computing

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The accurate and efficient diagnosis of breast cancer is extremely necessary for recovery and treatment in early stages in IoT healthcare environment. Internet of Things has witnessed the transition in life for the last few years which provides a way to analyze both the real-time data and past data by the emerging role of artificial intelligence and data mining techniques. The current state-of-the-art method does not effectively diagnose the breast cancer in the early stages, and most of the ladies suffered from this dangerous disease. Thus, the early detection of breast cancer significantly poses a great challenge for medical experts and researchers. To solve the problem of early-stage detection of breast cancer, we proposed machine learning-based diagnostic system which effectively classifies the malignant and benign people in the environment of IoT. In the development of our proposed system, a machine learning classifier support vector machine is used to classify the malignant and benign people. To improve the classification performance of the classification system, we used a recursive feature selection algorithm to select more suitable features from the breast cancer dataset. The training/testing splits method is applied for training and testing of the classifier for the best predictive model. Additionally, the classifier performance has been checked on by using performance evaluation metrics such as classification, specificity, sensitivity, Matthews’s correlation coefficient, F1-score, and execution time. To test the proposed method, the dataset “Wisconsin Diagnostic Breast Cancer” has been used in this research study. The experimental results demonstrate that the recursive feature selection algorithm selects the best subset of features, and the classifier SVM achieved optimal classification performance on this best subset of features. The SVM kernel linear achieved high classification accuracy (99%), specificity (99%), and sensitivity (98%), and the Matthews’s correlation coefficient is 99%. From these experimental results, we concluded that the proposed system performance is excellent due to the selection of more appropriate features that are selected by the recursive feature selection algorithm. Furthermore, we suggest this proposed system for effective and efficient early stages diagnosis of breast cancer. Thus, through this system, the recovery and treatment will be more effective for breast cancer. Lastly, the implementation of the proposed system is very reliable in all aspects of IoT healthcare for breast cancer.

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Breast Cancer Diagnosis Using an Unsupervised Feature Extraction Algorithm Based on Deep Learning

Cited by 19 publications

References 10 publications

Various Deep Learning Techniques Involved In Breast Cancer Mammogram Classification – A Survey

Various Deep Learning Techniques Involved In Breast Cancer Mammogram Classification – A Survey

Classification of Breast Cancer Using Neutrosophic Techniques and Deep Neural Network

Breast Cancer Detection in the IOT Health Environment Using Modified Recursive Feature Selection

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