Isbn: 9786209575037 - early detection and classification of breast cancer using ml (11 resultados)

PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.

Condición: New.

Condición: New.

Taschenbuch. Condición: Neu. Early Detection and Classification of Breast Cancer Using ML | P. Narasimhaiah (u. a.) | Taschenbuch | Englisch | 2026 | LAP LAMBERT Academic Publishing | EAN 9786209575037 | Verantwortliche Person für die EU: SIA OmniScriptum Publishing, Brivibas Gatve 197, 1039 RIGA, LETTLAND, customerservice[at]vdm-vsg[dot]de | Anbieter: preigu. …

Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 120 pp. Englisch.

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The most common cancer among the women in the world with high mortality rate is breast cancer. Early detection and accurate classification of breast cancer are essential for improving patient survival and enabling timely clinical intervention. Machine learning techniques have been widely applied to analyze diagnostic data from mammography, ultrasound, magnetic resonance imaging, and histopathological examinations. Traditional machine learning methods use handcrafted features classified by algorithms such as Support Vector Machines and Random Forests, while deep learning models, particularly convolutional neural networks, automatically learn discriminative feature representations from raw data. Experimental studies show that these models can effectively distinguish between benign and malignant lesions with high accuracy, supporting radiologists in early-stage diagnosis.…

Paperback. Condición: new. Paperback. The most common cancer among the women in the world with high mortality rate is breast cancer. Early detection and accurate classification of breast cancer are essential for improving patient survival and enabling timely clinical intervention. Machine learning techniques have been widely applied to analyze diagnostic data from mammography, ultrasound, magnetic resonance imaging, and histopathological examinations. Traditional machine learning methods use handcrafted features classified by algorithms such as Support Vector Machines and Random Forests, while deep learning models, particularly convolutional neural networks, automatically learn discriminative feature representations from raw data. Experimental studies show that these models can effectively distinguish between benign and malignant lesions with high accuracy, supporting radiologists in early-stage diagnosis. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Condición: New. Print on Demand.

Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -The most common cancer among the women in the world with high mortality rate is breast cancer. Early detection and accurate classification of breast cancer are essential for improving patient survival and enabling timely clinical intervention. Machine learning techniques have been widely applied to analyze diagnostic data from mammography, ultrasound, magnetic resonance imaging, and histopathological examinations. Traditional machine learning methods use handcrafted features classified by algorithms such as Support Vector Machines and Random Forests, while deep learning models, particularly convolutional neural networks, automatically learn discriminative feature representations from raw data. Experimental studies show that these models can effectively distinguish between benign and malignant lesions with high accuracy, supporting radiologists in early-stage diagnosis.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 120 pp. Englisch.…

Condición: New. PRINT ON DEMAND.