Modelling of Hybrid Meta heuristic Based Parameter Optimizers with Deep Convolutional Neural Network for Mammogram Cancer Detection

Abstract

Breast cancer (BC) is the common type of cancer among females. Mortality from BC could be decreased by identifying and diagnosing it atan earlierphase. Different imaging modalities are used to detect BC, like mammography. Even withproven records as a BC screening tool, mammography istime-consuming and hasconstraints, namely lower sensitivity in women with dense breast tissue. Computer-Aided Diagnosis or Detection (CAD) system assistsaproficient radiologist to identifyBC at an earlier stage. Recently, the advancementin deep learning (DL)methodsareemployed to mammography assist radiologists to increase accuracy and efficiency. Therefore, this study presents a metaheuristic-based hyperparameter optimization with deep learning-based breast cancer detection on mammogram images (MHODL-BCDMI) technique. The presented MHODL-BCDMI technique mainly focused on the recognition and classification of breast cancer on digital mammograms. To achieve this, the MHODL-BCDMI technique employs pre-processing in two stages: Wiener Filter (WF) based noise elimination and contrast enhancement. Besides, the MHODL-BCDMI technique exploits densely connected networks (DenseNet201) model for feature extraction purposes. For BC classification and detection, a hybrid convolutional neural network with a gated recurrent unit (HCNN-GRU) model is used. Furthermore, three hyperparameter optimizers are employed namely cat swarm optimization (CSO), harmony search algorithm (HSA), and hybrid grey wolf whale optimization algorithm (HGWWOA). Finally, the U2Net segmentation approach is used for the classification of benign and malignant types of cancer. The experimental analysis of the MHODL-BCDMI method is tested on a digital mammogram image dataset and the outcomes are assessed in terms of diverse metrics. The simulation results highlighted the enhanced cancer detection performance of the MHODL-BCDMI technique over other recent algorithms.