CFPNet-M

This repository contains the implementation of a novel light-weight real-time network (CFPNet-Medicine: CFPNet-M) to segment different types of biomedical images. It is a medical version of CFPNet, and the dataset we used from top to bottom are DRIVE, ISBI-2012, Infrared Breast, CVC-ClinicDB and ISIC 2018. The details of CFPNet-M and CFPNet can be found here respectively.

Infrared-Breast

ISIC2018

Cvc-ClinicDB

Architecture of CFPNet-M

CFP module

cfp-module

CFPNet-M

fig-3

Dataset

In this project, we test five datasets:

  • [x] Infrared Breast Dataset
  • [x] Endoscopy (CVC-ClinicDB)
  • [x] Electron Microscopy (ISBI-2012)
  • [x] Drive (Digital Retinal Image)
  • [x] Dermoscopy (ISIC-2018)

Usage

Prerequisities

The following dependencies are needed:

  • Kearas == 2.2.4
  • Opencv == 3.3.1
  • Tensorflow == 1.10.0
  • Matplotlib == 3.1.3
  • Numpy == 1.19.1

training

You can download the datasets you want to try, and just run: for UNet, DC-UNet, MultiResUNet, ICNet, CFPNet-M, ESPNet and ENet, the code is in the folder network. For Efficient-b0, MobileNet-v2 and Inception-v3, the code is in the main.py. Choose the segmentation model you want to test and run:

main.py

Segmentation Results of Five datasets

seg_table_1

seg_table_2

Speed and FLOPs

The code of test speed and FLOPs are in main.py, you can run them after training.
speed

GitHub

https://github.com/AngeLouCN/CFPNet-Medicine