AN AUTOMATED BREAST CANCER DETECTION BY HEURISTIC-BASED ENSEMBLE DEEP CLASSIFIER USING MAMMOGRAM AND TOMOSYNTHESIS IMAGES

Abstract

Nowadays, breast cancer is the most founded cancer among women. Moreover, 2.3 million new cases of breast cancer have been detected among women since 2020 as reported by World Health Organization (WHO). Many research studies based on breast cancer mainly aim at ultrasound, mammography, and Magnetic Resonance Images (MRI). However, there exist certain limitations such as lack of access for the detection of disease in rural and remote cities and insufficient knowledge about the availability of computer-aided systems. In order to overcome the problem, it is necessary to develop an effective detection model for breast cancer. For the experimentation, the input images like mammogram images and tomosynthesis images are garnered from the benchmark online resources. The input image further undergoes the pre-processing stage, which is made by using “Contrast Limited Adaptive Histogram Equalization (CLAHE) and histogram equalization (HE)”. Then, the pre-processed images are further given as input to the segmentation, in which deeplabv3 is deployed. Consequently, with the assistance of segmented images, the relevant features like “texture features, color features, shape features, deep features, statistical features and morphological features” are extracted. Then, these obtained features are used in weighted feature selection, where the optimal feature selection is performed, and weights are optimized by the Enhanced Adaptive Prey Location-Based Pelican Optimization Algorithm (APL-POA). Finally, the weighted accurate features are given as input to the Ensemble Deep Learning (EDC) model. The ensemble model is structured by “Deep Neural Network (DNN), Recurrent Neural Network (RNN), Bidirectional Long Short-Term Memory (Bi-LSTM), Deep Temporal Convolution Networks (DTCN) and Gated Recurrent Unit (GRU)”, in which the hyperparameters of every classifier are smoothened optimally by the enhanced APL-POA algorithm. Through the experimental analysis, the proposed work tends to provide an improved classification rate and rapid detection of disease that aids in better diagnosis of the patients.