Analysis of Machine Learning Algorithms for Breast Cancer Detection

Aswathy, M; Mani, Mohan Prasath

doi:10.4018/978-1-5225-9902-9.ch001

Cited by 5 publications

(3 citation statements)

References 18 publications

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“…Breast cancer is a form of cancer that grows in the cells of the breast 1 . It is the second most diagnosed cancer in women after skin, lung, and cervical cancer.…”

Section: Introductionmentioning

confidence: 99%

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Hybrid optimization algorithm based feature selection for mammogram images and detecting the breast mass using multilayer perceptron classifier

Rajendran¹,

Balasubramaniam²,

Ravi

et al. 2022

Computational Intelligence

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Breast cancer is the second most frequent malignant tumor in the world. Early findings of breast cancer can significantly improve treatment effectiveness. Manual methods of breast cancer diagnosis are prone to human fault and inaccuracy, and they take time. A computer-aided diagnosis can assist radiologists in making better choices by overcoming the disadvantages of manual methods. One of the significant steps in the breast cancer diagnosis process is feature selection. In recent decades, many studies have proposed numerous hybrid optimization methods to select the optimal features in the breast cancer detection system. However, many hybrid optimization algorithms are trapped in local optima and have slow convergence speed. Thus, it reduces the classification accuracy. For resolving these issues, this work proposes a hybrid optimization algorithm that combines the grasshopper optimization algorithm and the crow search algorithm for feature selection and classification of the breast mass with multilayer perceptron. The simulation is experimented with using MATLAB 2019a. The efficacy of the proposed hybrid grasshopper optimization-crow search algorithm with multilayer perceptron system is compared to multilayer perceptron based algorithms of enhanced and

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“…Breast cancer is a form of cancer that grows in the cells of the breast 1 . It is the second most diagnosed cancer in women after skin, lung, and cervical cancer.…”

Section: Introductionmentioning

confidence: 99%

“…Breast cancer is a form of cancer that grows in the cells of the breast. 1 It is the second most diagnosed cancer in women after skin, lung, and cervical cancer. In 2020, there will be 2.3 million new cases of breast cancer, 6,85,000 deaths worldwide, and it is nearly doubled by the year 2025.…”

Section: Introductionmentioning

confidence: 99%

Hybrid optimization algorithm based feature selection for mammogram images and detecting the breast mass using multilayer perceptron classifier

Rajendran¹,

Balasubramaniam²,

Ravi

et al. 2022

Computational Intelligence

View full text Add to dashboard Cite

show abstract

“…Many researchers have their efforts to diagnose and detect breast cancer, and every algorithm has different accuracy which rates on different situations, datasets, and tools. Some researchers focus on comparative analysis of additional machine learning and deep learning approaches to find out the most suitable and appropriate method or techniques to support their dataset with excellent accuracy in predicting breast cancer [21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection System to Identify Invasive Ductal Carcinoma by Predicting Bloom Richardson Grading from Histopathological Images

Talpur¹,

Rashid²,

Khan³

et al. 2022

JISR-C

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After skin cancer, the most common type of cancer is breast cancer among the world population. Breast cancer is the leading cause of cancer-induced mortality among women. Breast cancer is frequently diagnosed by using biopsies in which tissue is removed from the breast and studied under a microscope. The results of these biopsies are based on the qualifications and experience of the pathologist who diagnoses the abnormal cell under the microscope. With the emergence of advancements in the fields of image processing and artificial intelligence, there is an area of interest in developing a deep learning model to improve and enhance the quality and accuracy of breast cancer diagnosis. This study proposed a deep learning model that automatically analyses the multiclass classification of hematoxylin and eosin-stained histological images of invasive ductal carcinoma (IDC) by discriminating the IDC into grades such as G-1, G-2, and G-3. The methodology is focused on a deep learning model to detect grades of invasive ductal carcinoma by adopting the Sequential Convolutional Neural Network Two-Dimensional (CNN2D). We used DataBiox, a public dataset taken from an internet source consisting of 922 images. We evaluate the result of multiclass classification by dividing 80% and 20% of the dataset into training and testing data, respectively. As a result of the training and testing of the pre-trained CNN model, sequential CNN yields the accuracy of the model of 92.81%. Our model accurately classifies a multi-class classification of histological images of grades of breast cancer, specifically IDC. It is ready to be tested with a more diverse and massive database in the future.

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Early detection of digital mammogram using kernel extreme learning machine

Bacha

Abdellafou

Aljuhani

et al. 2022

Concurrency and Computation

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An automated computer-assisted medical diagnosis that combines latest medical approaches and the advanced machine learning algorithms is a very crucial multidisciplinary technology, generating correct and noninvasive diagnoses of multiple diseases like breast cancer. The work proposed in this article focuses on the development of a biomedical computer-assisted diagnosis model that can classify digital mammography as normal (healthy) or abnormal, and further, as malignant or benign.The proposed approach employs the discrete Chebyshev transform to extract the features. Then, the kernel principal component analysis technique is used to extract the discriminating features from the original feature vector. Subsequently, an optimized kernel extreme learning machine is proposed as a classifier to detect the tumors present in the mammographic images. Because the efficiency of the proposed classifier depends on its characteristic kernel variable, the main idea of the present work is to choose the most appropriate features from the downsized feature set and simultaneously obtain the optimized value of the aforementioned parameter. To validate the efficiency of the proposed work, the proposed scheme is performed on two publicly available data sets, namely the Mammographic Image Analysis Society data set and the INbreast data set. From the experimental analysis and its results, it is showed that for both normal-abnormal and malignant-benign classification, the proposed approach results in accuracy of 100% for the first data set. However, in the case of malignant-benign classification, the proposed approach gives an accuracy of 99.93% for the second data set. Further, it is also observed that the proposed approach exhibits highest performance as compared to that of the other approaches.Additionally, the ANOVA test is evaluated to demonstrate that the achievement of the proposed approach is significantly good than that of the other existing approaches.

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Analysis of Machine Learning Algorithms for Breast Cancer Detection

Cited by 5 publications

References 18 publications

Hybrid optimization algorithm based feature selection for mammogram images and detecting the breast mass using multilayer perceptron classifier

Hybrid optimization algorithm based feature selection for mammogram images and detecting the breast mass using multilayer perceptron classifier

Automatic Detection System to Identify Invasive Ductal Carcinoma by Predicting Bloom Richardson Grading from Histopathological Images

Early detection of digital mammogram using kernel extreme learning machine

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