Hyperparameter Tuned Deep Hybrid Denoising Autoencoder Breast Cancer Classification on Digital Mammograms

Breast Cancer (BC) is considered the most commonly scrutinized can-cer in women worldwide, affecting one in eight women in a lifetime. Mammogra-phy screening becomes one such standard method that is helpful in identifying suspicious masses' malignancy of BC at an initial level. However, the prior iden-tification of masses in mammograms was still challenging for extremely dense and dense breast categories and needs an effective and automatic mechanisms for helping radiotherapists in diagnosis. Deep learning (DL) techniques were broadly utilized for medical imaging applications, particularly breast mass classi-fication. The advancements in the DL field paved the way for highly intellectual and self-reliant computer-aided diagnosis (CAD) systems since the learning cap-ability of Machine Learning (ML) techniques was constantly improving. This paper presents a new Hyperparameter Tuned Deep Hybrid Denoising Autoenco-der Breast Cancer Classification (HTDHDAE-BCC) on Digital Mammograms. The presented HTDHDAE-BCC model examines the mammogram images for the identification of BC. In the HTDHDAE-BCC model, the initial stage of image preprocessing is carried out using an average median filter. In addition, the deep convolutional neural network-based Inception v4 model is employed to generate feature vectors. The parameter tuning process uses the binary spider monkey opti-mization (BSMO) algorithm. The HTDHDAE-BCC model exploits chameleon swarm optimization (CSO) with the DHDAE model for BC classification. The experimental analysis of the HTDHDAE-BCC model is performed using the MIAS database. The experimental outcomes demonstrate the betterments of the HTDHDAE-BCC model over other recent approaches.