Breast Cancer Detection and Classification: A Comparative Analysis Using Machine Learning Algorithms

Sakib, Shadman; Yasmin, Nowrin; Tanzeem, Abyaz Kader; Shorna, Fatema; Hasib, Khan Md.; Alam, Saadia Binte

doi:10.1007/978-981-16-8862-1_46

Cited by 23 publications

(6 citation statements)

References 19 publications

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“…The accuracy achieved with MLP, LR, and RF was 98%, 79%, and 95%, respectively. Sakib et al [94] performed a comparison among DL, SVM, LR, RF, and KNN to predict breast cancer. The RF model outperforms others with 96.66% accuracy.…”

Section: Sathiyanarayanan Et Almentioning

confidence: 99%

Breast Cancer-Risk Factors and Prediction Using Machine-Learning Algorithms and Data Source: A Review of Literature

Alsabry,

Algabri,

Ahsan

2023

jast

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Breast cancer (BC) is a major health concern worldwide. It is a complex and multifactorial disease, and identifying its risk factors is crucial for early detection and effective treatment. This review article provides an overview of the literature on breast cancer risk factors, data sources, and machine learning algorithms for prediction. The paper discusses the various risk factors associated with breast cancer, including age, family history, lifestyle choices, and environmental factors. Additionally, the article explores the different data sources used in breast cancer research, including clinical data, genomic data, and lifestyle data. The paper then reviews the different machine-learning algorithms used for breast cancer prediction, including supervised and unsupervised learning. The performance of ML algorithms in predicting BC risk using different data sources was assessed. This review provides valuable insights into the current state of research on BC risk factors and prediction using ML algorithms and data sources, and the findings will be useful for healthcare professionals and researchers working in the field of BC.

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Section: Sathiyanarayanan Et Almentioning

confidence: 99%

Breast Cancer-Risk Factors and Prediction Using Machine-Learning Algorithms and Data Source: A Review of Literature

Alsabry,

Algabri,

Ahsan

2023

jast

View full text Add to dashboard Cite

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“…The current body of literature encompasses a substantial range of studies about breast cancer that hold important relevance. Sakib et al [24] did a comparative analysis to investigate the application of ML and Deep Learning (DL) approaches in the context of breast cancer detection and diagnosis. The task of classification involved the utilization of five wellestablished supervised ML methodologies, namely, the Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Decision Tree (DT), Logistic Regression (LR), and Random Forest (RF), in conjunction with a DL methodology.…”

Section: Related Workmentioning

confidence: 99%

A Federated Learning Approach to Breast Cancer Prediction in a Collaborative Learning Framework

Almufareh,

Tariq,

Humayun

et al. 2023

Healthcare

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Breast cancer continues to pose a substantial worldwide public health concern, necessitating the use of sophisticated diagnostic methods to enable timely identification and management. The present research utilizes an iterative methodology for collaborative learning, using Deep Neural Networks (DNN) to construct a breast cancer detection model with a high level of accuracy. By leveraging Federated Learning (FL), this collaborative framework effectively utilizes the combined knowledge and data assets of several healthcare organizations while ensuring the protection of patient privacy and data security. The model described in this study showcases significant progress in the field of breast cancer diagnoses, with a maximum accuracy rate of 97.54%, precision of 96.5%, and recall of 98.0%, by using an optimum feature selection technique. Data augmentation approaches play a crucial role in decreasing loss and improving model performance. Significantly, the F1-Score, a comprehensive metric for evaluating performance, turns out to be 97%. This study signifies a notable advancement in the field of breast cancer screening, fostering hope for improved patient outcomes via increased accuracy and reliability. This study highlights the potential impact of collaborative learning, namely, in the field of FL, in transforming breast cancer detection. The incorporation of privacy considerations and the use of diverse data sources contribute to the advancement of early detection and the treatment of breast cancer, hence yielding significant benefits for patients on a global scale.

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“…Nevertheless, with contemporary tools available, this process can now be accomplished more efficiently and with reduced time and effort ( Toğaçar, 2021 ). Artificial intelligence (AI) has become increasingly popular for its ability to rapidly process data and make assessments ( Sakib et al, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

Transfer learning based approach for lung and colon cancer detection using local binary pattern features and explainable artificial intelligence (AI) techniques

Alsubai

2024

PeerJ Computer Science

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Cancer, a life-threatening disorder caused by genetic abnormalities and metabolic irregularities, is a substantial health danger, with lung and colon cancer being major contributors to death. Histopathological identification is critical in directing effective treatment regimens for these cancers. The earlier these disorders are identified, the lesser the risk of death. The use of machine learning and deep learning approaches has the potential to speed up cancer diagnosis processes by allowing researchers to analyse large patient databases quickly and affordably. This study introduces the Inception-ResNetV2 model with strategically incorporated local binary patterns (LBP) features to improve diagnostic accuracy for lung and colon cancer identification. The model is trained on histopathological images, and the integration of deep learning and texture-based features has demonstrated its exceptional performance with 99.98% accuracy. Importantly, the study employs explainable artificial intelligence (AI) through SHapley Additive exPlanations (SHAP) to unravel the complex inner workings of deep learning models, providing transparency in decision-making processes. This study highlights the potential to revolutionize cancer diagnosis in an era of more accurate and reliable medical assessments.

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Breast Cancer Detection and Classification: A Comparative Analysis Using Machine Learning Algorithms

Cited by 23 publications

References 19 publications

Breast Cancer-Risk Factors and Prediction Using Machine-Learning Algorithms and Data Source: A Review of Literature

Breast Cancer-Risk Factors and Prediction Using Machine-Learning Algorithms and Data Source: A Review of Literature

A Federated Learning Approach to Breast Cancer Prediction in a Collaborative Learning Framework

Transfer learning based approach for lung and colon cancer detection using local binary pattern features and explainable artificial intelligence (AI) techniques

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