Abstract
We propose a fully automated algorithm that is able to select a discriminative feature set from a training database via sequential forward selection (SFS), sequential backward selection (SBS), and F-score methods. We applied this scheme to microcalcifications cluster (MCC) detection in digital mammograms for early breast cancer detection. The system was able to select features fully automatically, regardless of the input training mammograms used. We tested the proposed scheme using a database of 111 clinical mammograms containing 1,050 microcalcifications (MCs). The accuracy of the system was examined via a free response receiver operating characteristic (fROC) curve of the test dataset. The system performance for MC identifications was Az = 0.9897, the sensitivity was 92%, and 0.65 false positives (FPs) were generated per image for MCC detection.
Highlights
Cancer is one of the major causes of death in the World
Based on the optimal parameters and settings determined in the training dataset, our system achieved 92% sensitivity with 0.65 false positives (FPs) per image in the test dataset
This study describes the development of a CADe system to detect MC clusters for early breast cancer detection
Summary
Cancer is one of the major causes of death in the World. There are many types of cancers, and its rates of occurrence differ, between men and women, and between geographical areas.Breast cancer is the most common type of cancer among women in many developed countries [1]. Cancer is one of the major causes of death in the World. There are many types of cancers, and its rates of occurrence differ, between men and women, and between geographical areas. Breast cancer is the most common type of cancer among women in many developed countries [1]. Breast cancer has a high mortality rate, but is difficult to diagnose before the development of signs or symptoms. Diagnosis and treatment plays an important role in improving survival rates and prognoses. For this reason, regular mammography and sonography screenings are recommended for women over forty years of age [2]
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