Andere Kunden interessierten sich auch für
![Segmentation du noyau pour le dépistage du cancer du col de l'utérus par frottis cervical]( "Segmentation du noyau pour le dépistage du cancer du col de l'utérus par frottis cervical")
Segmentation du noyau pour le dépistage du cancer du col de l'utérus par frottis cervical29,99 €![Segmentazione del nucleo per lo screening del cancro cervicale con il Pap test]( "Segmentazione del nucleo per lo screening del cancro cervicale con il Pap test")
Segmentazione del nucleo per lo screening del cancro cervicale con il Pap test29,99 €![Kernsegmentierung für das Gebärmutterhalskrebs-Screening mit Pap-Abstrichen]( "Kernsegmentierung für das Gebärmutterhalskrebs-Screening mit Pap-Abstrichen")
Kernsegmentierung für das Gebärmutterhalskrebs-Screening mit Pap-Abstrichen43,90 €![Improving Classification Accuracy of Automated Text Classifiers]( "Improving Classification Accuracy of Automated Text Classifiers")
Improving Classification Accuracy of Automated Text Classifiers27,99 €![Data Mining for Information Security: An Experimental Approach]( "Data Mining for Information Security: An Experimental Approach")
Data Mining for Information Security: An Experimental Approach37,99 €![DSS for Precision Agriculture using Geo-ICT & WSN Technologies]( "DSS for Precision Agriculture using Geo-ICT & WSN Technologies")
DSS for Precision Agriculture using Geo-ICT & WSN Technologies43,99 €![Image-Based Plant Disease Detection]( "Image-Based Plant Disease Detection")
Image-Based Plant Disease Detection29,99 €
Cervical cancer, the second most common cancer globally, is highly curable if detected early. However, rural areas face high mortality rates due to poor resources and limited screening programs. Automated diagnosis can address these gaps by distinguishing abnormal Pap smear cells based on nuclear shape. This study evaluates segmentation methods on the AGMC-TU Pap-Smear dataset, achieving a classification accuracy of 92.83% with SVM Linear and improving to 97.65% using optimized features and the FCM method. Accurate nucleus segmentation is crucial for reliable abnormal cell prediction, enhancing cervical cancer screening efficacy.