Breast cancer remains one of the most prevalent and life-threatening diseases affecting women worldwide. Early and accurate detection plays a crucial role in improving survival rates and guiding effective treatment strategies. With the rapid advancements in Artificial Intelligence (AI), machine learning and computer vision techniques are increasingly being applied to automate the processes of breast cancer classification and image segmentation. This study focuses on the development of an intelligent framework that integrates recursive feature elimination (RFE) with a Support Vector Machine (SVM) classifier to enhance the accuracy and reliability of breast cancer detection and analysis. Experimental results demonstrate that the combination of segmentation techniques, RFE-based feature optimization, and SVM classification significantly improves diagnostic performance when compared to conventional machine learning approaches. The model achieves high accuracy, precision, and recall, making it suitable for clinical applications where reliability is critical.
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Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Breast cancer remains one of the most prevalent and life-threatening diseases affecting women worldwide. Early and accurate detection plays a crucial role in improving survival rates and guiding effective treatment strategies. With the rapid advancements in Artificial Intelligence (AI), machine learning and computer vision techniques are increasingly being applied to automate the processes of breast cancer classification and image segmentation. This study focuses on the development of an intelligent framework that integrates recursive feature elimination (RFE) with a Support Vector Machine (SVM) classifier to enhance the accuracy and reliability of breast cancer detection and analysis. Experimental results demonstrate that the combination of segmentation techniques, RFE-based feature optimization, and SVM classification significantly improves diagnostic performance when compared to conventional machine learning approaches. The model achieves high accuracy, precision, and recall, making it suitable for clinical applications where reliability is critical. N° de réf. du vendeur 9786208457150
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Paperback. Etat : new. Paperback. Breast cancer remains one of the most prevalent and life-threatening diseases affecting women worldwide. Early and accurate detection plays a crucial role in improving survival rates and guiding effective treatment strategies. With the rapid advancements in Artificial Intelligence (AI), machine learning and computer vision techniques are increasingly being applied to automate the processes of breast cancer classification and image segmentation. This study focuses on the development of an intelligent framework that integrates recursive feature elimination (RFE) with a Support Vector Machine (SVM) classifier to enhance the accuracy and reliability of breast cancer detection and analysis. Experimental results demonstrate that the combination of segmentation techniques, RFE-based feature optimization, and SVM classification significantly improves diagnostic performance when compared to conventional machine learning approaches. The model achieves high accuracy, precision, and recall, making it suitable for clinical applications where reliability is critical. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. N° de réf. du vendeur 9786208457150
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Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -Breast cancer remains one of the most prevalent and life-threatening diseases affecting women worldwide. Early and accurate detection plays a crucial role in improving survival rates and guiding effective treatment strategies. With the rapid advancements in Artificial Intelligence (AI), machine learning and computer vision techniques are increasingly being applied to automate the processes of breast cancer classification and image segmentation. This study focuses on the development of an intelligent framework that integrates recursive feature elimination (RFE) with a Support Vector Machine (SVM) classifier to enhance the accuracy and reliability of breast cancer detection and analysis. Experimental results demonstrate that the combination of segmentation techniques, RFE-based feature optimization, and SVM classification significantly improves diagnostic performance when compared to conventional machine learning approaches. The model achieves high accuracy, precision, and recall, making it suitable for clinical applications where reliability is critical.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 52 pp. Englisch. N° de réf. du vendeur 9786208457150
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