Efficient breast cancer classification network with dual squeeze and excitation in histopathological images

HIGHLIGHTS

• who: Md. Mostafa Kamal Sarker and collaborators from the National Subsea Center, Robert Gordon University, Aberdeen , AQ, UK have published the paper: Efficient Breast Cancer Classification Network with Dual Squeeze and Excitation in Histopathological Images, in the Journal: Diagnostics 2023, 13, 103. of /2023/
• what: The authors propose a convolutional neural network (CNN)-based classification method fused mobile inverted bottleneck convolutions (FMB-Conv) and mobile inverted bottleneck convolutions (MBConv) with a dual squeeze and excitation (DSE) network to accurately classify tissue into binary (benign and malignant) and eight subtypes using histopathology images. One of the . . .
  

   

  

  If you want to have access to all the content you need to log in!

  Thanks :)

  Username or Email

  Password

  Remember me

  Lost your password?

  If you don't have an account, you can create one here.