Efficient Machine Learning and Deep Learning Techniques for Detection of Breast Cancer Tumor
The detection of cancer tumors is an essential component that has important consequences for the speedy involvement of medical professionals and the enhancement of patient outcomes. This review paper presents a complete study of the current body of research and methodology, as well as an in-depth as...
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| Format: | Article |
| Language: | English |
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QAASPA Publisher
2024-06-01
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| Series: | BioMed Target Journal |
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| Online Access: | https://qaaspa.com/index.php/bmtj/article/view/23 |
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| author | Dlshad Abdalrahman Mahmood Sadegh Abdullah Aminfar |
| author_facet | Dlshad Abdalrahman Mahmood Sadegh Abdullah Aminfar |
| author_sort | Dlshad Abdalrahman Mahmood |
| collection | DOAJ |
| description | The detection of cancer tumors is an essential component that has important consequences for the speedy involvement of medical professionals and the enhancement of patient outcomes. This review paper presents a complete study of the current body of research and methodology, as well as an in-depth assessment of the use of machine learning (ML) and deep learning (DL) in the detection of cancer tumors. In addition, the article gives a full analysis of the approaches involved. Machine learning and deep learning, which effectively handle ambiguity in the identification of malignant tumors, provide an alternative method for dealing with the complexity of brain tissue. This method is offered by a combination of machine learning and deep learning. The first part of the review draws attention to the significance of making an accurate diagnosis of breast cancer, highlights the limits of traditional diagnostic methods, and investigates the cutting-edge area of medical imaging technology. After that, it investigates the fundamentals of ML and DL and how they might be used to deal with the challenges that are inherent in the interpretation of complicated imaging data. In addition, the paper explores the ways in which models enhance the processes of feature extraction, picture segmentation, and classification in breast tumor detection systems. |
| format | Article |
| id | doaj-art-673f556f9e4449b289e726d99a745e99 |
| institution | Kabale University |
| issn | 2960-1428 |
| language | English |
| publishDate | 2024-06-01 |
| publisher | QAASPA Publisher |
| record_format | Article |
| series | BioMed Target Journal |
| spelling | doaj-art-673f556f9e4449b289e726d99a745e992025-01-10T21:54:26ZengQAASPA PublisherBioMed Target Journal2960-14282024-06-012111310.59786/bmtj.21123Efficient Machine Learning and Deep Learning Techniques for Detection of Breast Cancer TumorDlshad Abdalrahman Mahmood0https://orcid.org/0009-0006-0442-6043Sadegh Abdullah Aminfar1https://orcid.org/0000-0002-8277-6130Department of Computer Science, Faculty of Science, Soran University, Soran, 44008, IraqDepartment of Computer Science, Faculty of Science, Soran University, Soran. 44008, IraqThe detection of cancer tumors is an essential component that has important consequences for the speedy involvement of medical professionals and the enhancement of patient outcomes. This review paper presents a complete study of the current body of research and methodology, as well as an in-depth assessment of the use of machine learning (ML) and deep learning (DL) in the detection of cancer tumors. In addition, the article gives a full analysis of the approaches involved. Machine learning and deep learning, which effectively handle ambiguity in the identification of malignant tumors, provide an alternative method for dealing with the complexity of brain tissue. This method is offered by a combination of machine learning and deep learning. The first part of the review draws attention to the significance of making an accurate diagnosis of breast cancer, highlights the limits of traditional diagnostic methods, and investigates the cutting-edge area of medical imaging technology. After that, it investigates the fundamentals of ML and DL and how they might be used to deal with the challenges that are inherent in the interpretation of complicated imaging data. In addition, the paper explores the ways in which models enhance the processes of feature extraction, picture segmentation, and classification in breast tumor detection systems.https://qaaspa.com/index.php/bmtj/article/view/23breast tumormachine learningdeep learningdiagnosis |
| spellingShingle | Dlshad Abdalrahman Mahmood Sadegh Abdullah Aminfar Efficient Machine Learning and Deep Learning Techniques for Detection of Breast Cancer Tumor BioMed Target Journal breast tumor machine learning deep learning diagnosis |
| title | Efficient Machine Learning and Deep Learning Techniques for Detection of Breast Cancer Tumor |
| title_full | Efficient Machine Learning and Deep Learning Techniques for Detection of Breast Cancer Tumor |
| title_fullStr | Efficient Machine Learning and Deep Learning Techniques for Detection of Breast Cancer Tumor |
| title_full_unstemmed | Efficient Machine Learning and Deep Learning Techniques for Detection of Breast Cancer Tumor |
| title_short | Efficient Machine Learning and Deep Learning Techniques for Detection of Breast Cancer Tumor |
| title_sort | efficient machine learning and deep learning techniques for detection of breast cancer tumor |
| topic | breast tumor machine learning deep learning diagnosis |
| url | https://qaaspa.com/index.php/bmtj/article/view/23 |
| work_keys_str_mv | AT dlshadabdalrahmanmahmood efficientmachinelearninganddeeplearningtechniquesfordetectionofbreastcancertumor AT sadeghabdullahaminfar efficientmachinelearninganddeeplearningtechniquesfordetectionofbreastcancertumor |