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

2024/08/25 by Yujia Lin, Lin, Yujia, Aiwei Lian +4
Computer Science · #AI in cancer detection #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2408.13800

openalex publication_date 2024/08/25 · openalex created_date 2024/09/21 · openalex updated_date 2026/07/28

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.

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