Andere Kunden interessierten sich auch für
![Benign Malignant Classification of Lung Nodules using Machine Learning]( "Benign Malignant Classification of Lung Nodules using Machine Learning")
Benign Malignant Classification of Lung Nodules using Machine Learning67,99 €![Segmentation of mammographic images for computer aided diagnosis]( "Segmentation of mammographic images for computer aided diagnosis")
Segmentation of mammographic images for computer aided diagnosis46,99 €![Prediction of breast cancer using various machine learning methods]( "Prediction of breast cancer using various machine learning methods")
Prediction of breast cancer using various machine learning methods36,99 €![MRI Image Segmentation for Detection of Brain Tumors]( "MRI Image Segmentation for Detection of Brain Tumors")
MRI Image Segmentation for Detection of Brain Tumors29,99 €![An Automatic Algorithm for QA of MRI Scanners using a DWI Phantom]( "An Automatic Algorithm for QA of MRI Scanners using a DWI Phantom")
An Automatic Algorithm for QA of MRI Scanners using a DWI Phantom27,99 €![Cloud Computing Anomaly and Threat Detection]( "Cloud Computing Anomaly and Threat Detection")
Cloud Computing Anomaly and Threat Detection36,99 €![Neural Information Processing]( "Neural Information Processing")
Neural Information Processing37,99 €
Breast cancer is one of the most common kinds of cancer, as well as the leading cause of mortality among women. Mammography is currently the most effective imaging modality for the detection of breast cancer and the diagnosis of the anomalies which can identify cancerous cells. Retrospective studies show that, in current breast cancer screenings approximately 15 to 30 percent of breast cancer cases are missed by radiologists. With the advances in digital image processing techniques, it is envisaged that radiologists will have opportunities to decrease this margin of error and hence, improve their diagnosis.