Fusion of ESRGAN, Adaptive NSCT, and Multi-Attention CNN With Wavelet Transform for Histopathological Image Classification

Breast cancer is a prominent reason of death amidst females and is considered one of the most widespread types of cancer. Early detection is crucial for effective treatment. Research has demonstrated that computerized approaches can aid in the detection procedure and offer benefits beyond accustomed evaluation schemes. The study presents a contemporary approach for classifying breast cancer images by virtue of histopathology. This novel approach combines the fusion of ESRGAN and NSCT algorithms with a multi-attention-based CNN that utilizes wavelet transform. The original size of an image from the Patch Camelyon (PCam) dataset is $96\times 96$ , which is too small to extract features for the Convolutional Neural Network (CNN) effectively. An enhanced-super-resolution-generative-adversarial network, also known as the ESRGAN model, and the adaptive nonsubsampled contourlet transform were employed to rectify image quality. The proposed model was designed using Symlet, Coiflet, and a combination of both wavelets to identify which preserves better classification features and yields a promising result. The model proposed in this study utilizes the multi-head attention mechanism combined with CNN layers to operate in parallel multiple times. The model's training, validation, and testing were conducted on the PCam dataset, which includes 327,680 histopathological images. The trained model was assessed using test data and achieved an accuracy of 93.67% and AUC of 94%, surpassing the performance of several other existing models.