Name: Detection of Microcalcification in Mammograms Using AI & ML Techniques (en Inglés)
Price: 329.87 PEN
Availability: InStock
Author: Jaganathan, Subash Chandra Bose
ISBN: 9786203471823

portada Detection of Microcalcification in Mammograms Using AI & ML Techniques (en Inglés)

Formato

Libro Físico

Autor

Subash Chandra Bose Jaganathan

Editorial

LAP Lambert Academic Publishing

Idioma

Inglés

N° páginas

168

Encuadernación

Tapa Blanda

Dimensiones

22.9 x 15.2 x 1.0 cm

Peso

0.25 kg.

ISBN13

9786203471823

Categorías

Tecnología: cuestiones generales

Detection of Microcalcification in Mammograms Using AI & ML Techniques (en Inglés)

Subash Chandra Bose Jaganathan (Autor) · LAP Lambert Academic Publishing · Tapa Blanda

Libro Nuevo

S/ 329,87

S/ 659,74

Ahorras: S/ 329,87

50% descuento

Envío normal

Origen: Estados Unidos (Costos de importación incluídos en el precio)

Se enviará desde nuestra bodega entre el Miércoles 31 de Julio y el Viernes 09 de Agosto.

Lo recibirás en cualquier lugar de Perú entre 2 y 5 días hábiles luego del envío.

Reseña del libro "Detection of Microcalcification in Mammograms Using AI & ML Techniques (en Inglés)"

One of the new modern and most efficient methods for breast cancer early detection is mammography. A new method for the detection and classification of microcalcifications is presented. It can be done in four stages: first, preprocessing stage deals with noise removal, and normalized the image. The second stage, Fuzzy c-Means clustering (FCM) is used for segmentation and pectoral muscle extraction using area calculation and finally microcalcifications detection. The third stage consists of two-dimensional discrete wavelet transforms are extracted from the detection of microcalcifications. And then, nine statistical features are calculated from the LL band of the wavelet transform. Finally, the extracted features are fed as input to the Artificial Neural Network and are classified into normal or abnormal (benign or malignant) images. The given classification approach is applied to a database of 322 dense mammographic images, originating from the MIAS database. The results are analyzed using MATLAB.