Wound Detection by Simple Feedforward Neural Network

被引:7
|
作者
Marijanovic, Domagoj [1 ]
Nyarko, Emmanuel Karlo [1 ]
Filko, Damir [1 ]
机构
[1] Comp Sci & Informat Technol Osijek, Fac Elect Engn, Osijek 31000, Croatia
关键词
chronic wounds; wound detection; wound segmentation; feedforward neural network; robot; SEGMENTATION;
D O I
10.3390/electronics11030329
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Chronic wounds are a heavy burden on medical facilities, so any help in treating them is most welcome. Current research focuses on wound analysis, especially wound tissue classification, wound measurement, and wound healing prediction to assist medical personnel in wound treatment, with the main goal of reducing wound healing time. The first phase of wound analysis is wound segmentation, where the task is to extract wounds from the healthy tissue and image background. In this work, a standard feedforward neural network was developed for the purpose of wound segmentation using data from the MICCAI 2021 Foot Ulcer Segmentation (FUSeg) Challenge. It proved to be a simple yet efficient method for extracting wounds from images. The proposed algorithm is part of a compact system that analyzes chronic wounds using a robotic manipulator, RGB-D camera and 3D scanner. The feedforward neural network consists of only five fully connected layers, the first four with Rectified Linear Unit (ReLU) activation functions and the last with sigmoid activation functions. Three separate models were trained and tested using images provided as part of the challenge. The predicted images were post-processed and merged to improve the final segmentation performance.The accuracy metrics observed during model training and selection were Precision, Recall and F1 score. The experimental results of the proposed network provided a recall value of 0.77, precision value of 0.72, and an F1 score (Dice score) of 0.74.
引用
收藏
页数:18
相关论文
共 50 条
  • [21] Stable criticality in a feedforward neural network
    Ceccatto, A
    Navone, H
    Waelbroeck, H
    REVISTA MEXICANA DE FISICA, 1996, 42 (05) : 810 - 825
  • [22] Breast Cancer Detection via Hu Moment Invariant and Feedforward Neural Network
    Zhang, Xiaowei
    Yang, Jiquan
    Nguyen, Elijah
    PROCEEDINGS OF THE 2ND INTERNATIONAL CONFERENCE ON APPLIED SCIENCES (ICAS-2), 2018, 1954
  • [23] Colorectal Polyp Detection Using Feedforward Neural Network with Image Feature Selection
    Muhammad, Arif Wirawan
    Sasmito, Ginanjar Wiro
    Riadi, Imam
    2018 INTERNATIONAL SYMPOSIUM ON ADVANCED INTELLIGENT INFORMATICS (SAIN), 2018, : 26 - 31
  • [24] Transmission of neural activity in a feedforward network
    Wang, ST
    Wang, W
    NEUROREPORT, 2005, 16 (08) : 807 - 811
  • [25] A multilayer feedforward fuzzy neural network
    Savran, Aydogan
    ARTIFICIAL INTELLIGENCE AND NEURAL NETWORKS, 2006, 3949 : 78 - 83
  • [26] Genetic design for feedforward neural network
    Lu, Jianfeng
    Shang, Shang
    Yang, Jingyu
    Nanjing Li Gong Daxue Xuebao/Journal of Nanjing University of Science and Technology, 1999, 23 (06): : 486 - 489
  • [27] Generalized feedforward neural network classifier
    Arulampalam, G
    Bouzerdoum, A
    PROCEEDINGS OF THE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS 2003, VOLS 1-4, 2003, : 1429 - 1434
  • [28] Local coupled feedforward neural network
    Sun, Jianye
    NEURAL NETWORKS, 2010, 23 (01) : 108 - 113
  • [29] A Simple Neural Network for Collision Detection of Collaborative Robots
    Czubenko, Michal
    Kowalczuk, Zdzislaw
    SENSORS, 2021, 21 (12)
  • [30] Simple Global Thresholding Neural Network for Shadow Detection
    Li, Guiyuan
    Zong, Changfu
    Zhang, Dong
    Zhu, Tianjun
    Li, Jianying
    SENSORS AND MATERIALS, 2021, 33 (09) : 3307 - 3316