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Breast cancer detection employing stacked ensemble model with convolutional features.
Karamti, Hanen; Alharthi, Raed; Umer, Muhammad; Shaiba, Hadil; Ishaq, Abid; Abuzinadah, Nihal; Alsubai, Shtwai; Ashraf, Imran.
Afiliación
  • Karamti H; Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.
  • Alharthi R; Department of Computer Science and Engineering, University of Hafr Al-Batin, Hafar, Saudi Arabia.
  • Umer M; Department of Computer Science and Information Technology, The Islamia University of Bahawalpur, Bahawalpur, Pakistan.
  • Shaiba H; Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.
  • Ishaq A; Department of Computer Science and Information Technology, The Islamia University of Bahawalpur, Bahawalpur, Pakistan.
  • Abuzinadah N; Faculty of Computer Science and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia.
  • Alsubai S; Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia.
  • Ashraf I; Department of Information and Communication Engineering, Yeungnam University, Gyeongsan-si, Korea.
Cancer Biomark ; 40(2): 155-170, 2024.
Article en En | MEDLINE | ID: mdl-38160347
ABSTRACT
Breast cancer is a major cause of female deaths, especially in underdeveloped countries. It can be treated if diagnosed early and chances of survival are high if treated appropriately and timely. For timely and accurate automated diagnosis, machine learning approaches tend to show better results than traditional methods, however, accuracy lacks the desired level. This study proposes the use of an ensemble model to provide accurate detection of breast cancer. The proposed model uses the random forest and support vector classifier along with automatic feature extraction using an optimized convolutional neural network (CNN). Extensive experiments are performed using the original, as well as, CNN-based features to analyze the performance of the deployed models. Experimental results involving the use of the Wisconsin dataset reveal that CNN-based features provide better results than the original features. It is observed that the proposed model achieves an accuracy of 99.99% for breast cancer detection. Performance comparison with existing state-of-the-art models is also carried out showing the superior performance of the proposed model.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias de la Mama / Redes Neurales de la Computación Límite: Female / Humans Idioma: En Revista: Cancer Biomark Asunto de la revista: BIOQUIMICA / NEOPLASIAS Año: 2024 Tipo del documento: Article País de afiliación: Arabia Saudita Pais de publicación: Países Bajos
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias de la Mama / Redes Neurales de la Computación Límite: Female / Humans Idioma: En Revista: Cancer Biomark Asunto de la revista: BIOQUIMICA / NEOPLASIAS Año: 2024 Tipo del documento: Article País de afiliación: Arabia Saudita Pais de publicación: Países Bajos