Comparative study of logistic regression and artificial neural networks on predicting breast cancer cytology

Mohammed, Yosra Abdulaziz; Saleh, Eman Gadban

doi:10.11591/ijeecs.v21.i2.pp1113-1120

Cited by 3 publications

(2 citation statements)

References 14 publications

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“…They have been shown to achieve high accuracy in tasks like breast cancer classification. In a study by Yosra Mohammed and E. Saleh (2021) [107], they used MLP in conjunction with other methods to predict the type of breast tumour, achieving high accuracy rates (94.2%). Authors in another study [108] trained MLPs using the Sine Cosine Algorithm, a metaheuristic optimization method and achieved high accuracy rates of up to 97% on several disease-related datasets, including breast cancer.…”

Section: A Advantagesmentioning

confidence: 99%

Breast Cancer Classification Utilizing Deep Learning Techniques on Medical Images: A Comprehensive Review

Marwah K. Muttair

2024

jes

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Breast cancer is a major public health concern, affecting millions of women worldwide. Good patient outcomes and a successful course of therapy depend on a prompt and accurate diagnosis. The application of deep learning algorithms to the breast cancer classification problem has yielded some encouraging results; this might open the way for more rapid and accurate diagnostics. This article examines the many deep learning (DL) approaches taken to date for BC classification tasks, outlining each one's strengths, weaknesses, and current challenges. This study discusses several DL algorithms like convolutional neural networks, multi-layer neural networks, and autoencoders that analyze histopathological pictures, mammograms, and other forms of medical imaging. Furthermore, we investigate the interpretability & explainability features of DL models as they pertain to BC diagnoses, drawing attention to the need for reliable decision-making tools for medical practitioners. Throughout the review, we identify challenges and potential biases in current research. Finally, we provide an outlook on the future directions of DL in BC classification, focusing on promising research areas. By highlighting the achievements and gaps in the existing literature, this review aims to inspire further advancements in DL-based BC diagnosis, eventually resulting in better healthcare and reliable diagnosis.

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Section: A Advantagesmentioning

confidence: 99%

Breast Cancer Classification Utilizing Deep Learning Techniques on Medical Images: A Comprehensive Review

Marwah K. Muttair

2024

jes

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“…In recent years, automated intelligent breast cancer prediction system is implemented with different supervised learning algorithms such as, k-nearest neighbor (KNN) and artificial neural network (ANN) [15]. However, the performance of the developed model still has scope for improvement for more accurate breast cancer prediction.…”

Section: Literature Surveymentioning

confidence: 99%

Exploring the performance of feature selection method using breast cancer dataset

Assegie¹,

Tulasi²,

Kumar

2022

IJEECS

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Breast cancer is the most common type of cancer occurring mostly in females. In recent years, many researchers have devoted to automate diagnosis of breast cancer by developing different machine learning model. However, the quality and quantity of feature in breast cancer diagnostic dataset have significant effect on the accuracy and efficiency of predictive model. Feature selection is effective method for reducing the dimensionality and improving the accuracy of predictive model. The use of feature selection is to determine feature required for training model and to remove irrelevant and duplicate feature. Duplicate feature is a feature that is highly correlated to another feature. The objective of this study is to conduct experimental research on three different feature selection methods for breast cancer prediction. Sequential, embedded and chi-square feature selection are implemented using breast cancer diagnostic dataset. The study compares the performance of sequential embedded and chi-square feature selection on test set. The experimental result evidently shows that sequential feature selection outperforms as compared to chi-square (X<sup>2</sup>) statistics and embedded feature selection. Overall, sequential feature selection achieves better accuracy of 98.3% as compared to chi-square (X<sup>2</sup>) statistics and embedded feature selection.

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FGF Exhibits an Important Biological Role on Regulating Cell Proliferation of Breast Cancer When it Transports Into The Cell Nuclei

Gao

Wang

et al. 2021

Cell Biochem Biophys

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Comparative study of logistic regression and artificial neural networks on predicting breast cancer cytology

Cited by 3 publications

References 14 publications

Breast Cancer Classification Utilizing Deep Learning Techniques on Medical Images: A Comprehensive Review

Breast Cancer Classification Utilizing Deep Learning Techniques on Medical Images: A Comprehensive Review

Exploring the performance of feature selection method using breast cancer dataset

FGF Exhibits an Important Biological Role on Regulating Cell Proliferation of Breast Cancer When it Transports Into The Cell Nuclei

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