Fractional Pelican African Vulture Optimization-based classification of breast cancer using mammogram images

Abstract

ABSTRACT Owing to the tiny abnormal developments in breast masses, examination and detection of breast cancer utilizing high-resolution images havebecome a complex process. Thus, an innovative framework for breast cancer classification is constructed, named SqueezeNet-Fractional Pelican African Vulture Optimization (FPAVO). Here, preprocessingis done utilizing a median filter and Region of Interest (ROI) extrication. The segmentation process is done using a hybrid network classifier, Recurrent Prototypical Network (RP-Net) and KNet,which is trained by the Pelican African Vulture Optimization (PAVO). The PAVO is the consolidation of the Pelican Optimization Algorithm (POA) and African Vulture Optimization (AVO). Then,texture and statistical features are extricated. After that, the data augmentation is done. Then, the SqueezeNet is used for classification, and it is trained by FPAVO, which integrates the fractionalcalculus (FC) into PAVO. The SqueezeNet-FPAVO produced high testing accuracy, sensitivity, specificity and F-measure of 0.920, 0.924, 0.925, and 0.897.