A New Feature Ensemble with a Multistage Classification Scheme for Breast Cancer Diagnosis

Abstract

A new and effective feature ensemble with a multistage classification is proposed to be implemented in a computer-aided diagnosis (CAD) system for breast cancer diagnosis. A publicly available mammogram image dataset collected during the Image Retrieval in Medical Applications (IRMA) project is utilized to verify the suggested feature ensemble and multistage classification. In achieving the CAD system, feature extraction is performed on the mammogram region of interest (ROI) images which are preprocessed by applying a histogram equalization followed by a nonlocal means filtering. The proposed feature ensemble is formed by concatenating the local configuration pattern-based, statistical, and frequency domain features. The classification process of these features is implemented in three cases: a one-stage study, a two-stage study, and a three-stage study. Eight well-known classifiers are used in all cases of this multistage classification scheme. Additionally, the results of the classifiers that provide the top three performances are combined via a majority voting technique to improve the recognition accuracy on both two- and three-stage studies. A maximum of 85.47%, 88.79%, and 93.52% classification accuracies are attained by the one-, two-, and three-stage studies, respectively. The proposed multistage classification scheme is more effective than the single-stage classification for breast cancer diagnosis.

Aggregate Linear Discriminate Analyzed Feature Extraction and Ensemble of Bootstrap with Knn Classifier for Malicious Tumour Detection

“…Finally, the third and fourth measure, sensitivity and specificity are evaluated. Table 2 records the measure of four metrics, namely, tumour classification accuracy, tumour classification time, sensitivity and specificity obtained from (11), (12), (13) and (14). It proves from Table 2 that our malicious tumour detection method, ALDA-EC provides better performance measure among all the other methods.…”

“…Another multimodality model using wavelets and machine learning investigates the feature [13]. A multistage classification model proposed [14] for breast cancer diagnosis applying a histogram equalization and nonlocal means filtering. This multistage classification model resulted in the improvement of classification accuracy for breast cancer diagnosis.…”

“…Specificity also called the true negative rate measures the proportion of negatives that correctly identified as such. In other words, specificity or true negative rate refers to the percentage of non-malicious tumour classes which correctly identified as not having the condition (14) V. DISCUSSION…”

Remote Diagnosis Decision Support System for Breast Cancer Screen

“…Breast cancer among woman is the second largest cancer in the world, as per the American cancer data 2, 66,120 new cases and 40,920 Deaths in 2018.the mortality rate is to be deceased by creating awareness.screening.detectimg early [1]. The increasing diagnosis and detecting capability of a breast cancer is improve the life time of the woman.…”

“…These CAD programmes are invoked for clinically appropriate and related mammogram scans of suspect areas in the CBIR (content-based image recovery system) [3][4]. A multistage classification based computer-aided diagnosis (CAD) system for breast cancer diagnosis is reported [1]. Feature extraction is performed on the mammogram region of interest (ROI) images.…”