BCDNet: A Fast Residual Neural Network For Invasive Ductal Carcinoma Detection

TL;DR

BCDNet is a fast, efficient residual neural network designed for early detection of invasive ductal carcinoma in histopathological images, achieving high accuracy with reduced computational costs.

Contribution

The paper introduces BCDNet, a novel neural network architecture that upsamples images with residual blocks and uses smaller convolutions and MLPs for effective IDC detection.

Findings

Achieves 91.6% average accuracy in IDC detection.

Reduces training time compared to ResNet 50 and ViT-B-16.

Effective in histopathological RGB image analysis.

Abstract

It is of great significance to diagnose Invasive Ductal Carcinoma (IDC) in early stage, which is the most common subtype of breast cancer. Although the powerful models in the Computer-Aided Diagnosis (CAD) systems provide promising results, it is still difficult to integrate them into other medical devices or use them without sufficient computation resource. In this paper, we propose BCDNet, which firstly upsamples the input image by the residual block and use smaller convolutional block and a special MLP to learn features. BCDNet is proofed to effectively detect IDC in histopathological RGB images with an average accuracy of 91.6% and reduce training consumption effectively compared to ResNet 50 and ViT-B-16.