Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
A Systematic Literature Review of Breast Cancer Diagnosis Using Machine Intelligence Techniques
Abstract Breast cancer is one of the most common diseases in women; it can have long-term implications and can even be fatal. However, early detection, achieved through recent advancements in technology, can help reduce mortality. In this paper, different machine intelligence techniques [machine learning (ML), and deep learning (DL)] were analysed in the context of breast cancer. In addition, the classification of breast cancer into malignant and benign using different breast cancer image modalities were discussed. Furthermore, the diagnosis of breast cancer using various publicly and privately available image datasets, pre-processing techniques, feature extraction techniques, comparison between conventional ML and different convolutional neural network (CNN) architectures, and transfer learning techniques were discussed in detail. It also correlates the parameters and attributes impact in case of different methods applied. Advantages and the limitations of the machine intelligence approaches were highlighted based on the discussion and analysis. A total of 162 research publications was considered for the time period of 2015–2021. These are in the chronological order of their appearance. This systematic literature review will be helpful to the researchers due to the detailed analysis of different methodologies and in conducting further investigations.
A Systematic Literature Review of Breast Cancer Diagnosis Using Machine Intelligence Techniques
Abstract Breast cancer is one of the most common diseases in women; it can have long-term implications and can even be fatal. However, early detection, achieved through recent advancements in technology, can help reduce mortality. In this paper, different machine intelligence techniques [machine learning (ML), and deep learning (DL)] were analysed in the context of breast cancer. In addition, the classification of breast cancer into malignant and benign using different breast cancer image modalities were discussed. Furthermore, the diagnosis of breast cancer using various publicly and privately available image datasets, pre-processing techniques, feature extraction techniques, comparison between conventional ML and different convolutional neural network (CNN) architectures, and transfer learning techniques were discussed in detail. It also correlates the parameters and attributes impact in case of different methods applied. Advantages and the limitations of the machine intelligence approaches were highlighted based on the discussion and analysis. A total of 162 research publications was considered for the time period of 2015–2021. These are in the chronological order of their appearance. This systematic literature review will be helpful to the researchers due to the detailed analysis of different methodologies and in conducting further investigations.
A Systematic Literature Review of Breast Cancer Diagnosis Using Machine Intelligence Techniques
Nemade, Varsha (Autor:in) / Pathak, Sunil (Autor:in) / Dubey, Ashutosh Kumar (Autor:in)
2022
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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