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Emerging Machine Learning and Deep Learning Technologies in Breast Cancer Screening and Diagnosis: A Comprehensive Overview
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 186 - Number 23 |
| Year of Publication: 2024 |
| Authors: B. Srinivas, M. Sriram, V. Ganesan |
10.5120/ijca2024923683
|
B. Srinivas, M. Sriram, V. Ganesan . Emerging Machine Learning and Deep Learning Technologies in Breast Cancer Screening and Diagnosis: A Comprehensive Overview. International Journal of Computer Applications. 186, 23 ( May 2024), 54-58. DOI=10.5120/ijca2024923683
Abstract
One of the biggest causes of cancer-related mortality for women globally is still breast cancer. The prognosis and survival rates are greatly enhanced by early discovery, which makes precise and effective diagnostic tools necessary. The most current developments in deep learning (DL) and machine learning (ML) methods for breast cancer early detection are thoroughly reviewed in this study. We describe the features and difficulties of several datasets that are frequently utilized in breast cancer research, such as MRI, ultrasound, and mammography pictures. Due to their better performance in image processing, convolutional neural networks (CNNs) and its variants are the emphasis of this category, which also includes classic machine learning (ML) approaches and sophisticated deep learning (DL) models. Important issues in this field are also covered in the paper, including data imbalance, the requirement for sizable annotated datasets, and model interpretability. To evaluate these models thoroughly, we give an evaluation matrix including metrics like accuracy, precision, recall, F1-score, AUC-ROC, and specificity. Our research demonstrates that although deep learning approaches, in particular CNNs, have demonstrated encouraging outcomes in terms of increasing diagnostic accuracy, incorporating these models into clinical practice necessitates resolving issues related to regulatory approval, data diversity, and model transparency. In conclusion, we suggest avenues for future study to improve the validity and usefulness of machine learning and deep learning methods in identifying breast cancer. In conclusion, we suggest avenues for future study to improve the validity and usefulness of machine learning and deep learning methods in identifying breast cancer. With the use of machine learning and deep learning techniques, this study seeks to give readers a thorough grasp of the current state of the field and stimulate additional developments in breast cancer diagnosis.
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