Deep Learning-Based Classification of Macrofungi: Comparative Analysis of Advanced Models for Accurate Fungi Identification

被引:1
|
作者
Ozsari, Sifa [1 ]
Kumru, Eda [2 ]
Ekinci, Fatih [3 ]
Akata, Ilgaz [4 ]
Guzel, Mehmet Serdar [1 ]
Acici, Koray [5 ]
Ozcan, Eray [1 ]
Asuroglu, Tunc [6 ,7 ]
机构
[1] Ankara Univ, Fac Engn, Dept Comp Engn, TR-06830 Ankara, Turkiye
[2] Ankara Univ, Grad Sch Nat & Appl Sci, TR-06830 Ankara, Turkiye
[3] Ankara Univ, Inst Nucl Sci, Dept Med Phys, TR-06100 Ankara, Turkiye
[4] Ankara Univ, Fac Sci, Dept Biol, TR-06100 Ankara, Turkiye
[5] Ankara Univ, Fac Engn, Dept Artificial Intelligence & Data Engn, TR-06830 Ankara, Turkiye
[6] Tampere Univ, Fac Med & Hlth Technol, Tampere 33720, Finland
[7] VTT Tech Res Ctr Finland, Tampere 33101, Finland
关键词
macrofungi classification; deep learning; DenseNet121; fungi identification; machine learning models; MUSHROOMS;
D O I
10.3390/s24227189
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
This study focuses on the classification of six different macrofungi species using advanced deep learning techniques. Fungi species, such as Amanita pantherina, Boletus edulis, Cantharellus cibarius, Lactarius deliciosus, Pleurotus ostreatus and Tricholoma terreum were chosen based on their ecological importance and distinct morphological characteristics. The research employed 5 different machine learning techniques and 12 deep learning models, including DenseNet121, MobileNetV2, ConvNeXt, EfficientNet, and swin transformers, to evaluate their performance in identifying fungi from images. The DenseNet121 model demonstrated the highest accuracy (92%) and AUC score (95%), making it the most effective in distinguishing between species. The study also revealed that transformer-based models, particularly the swin transformer, were less effective, suggesting room for improvement in their application to this task. Further advancements in macrofungi classification could be achieved by expanding datasets, incorporating additional data types such as biochemical, electron microscopy, and RNA/DNA sequences, and using ensemble methods to enhance model performance. The findings contribute valuable insights into both the use of deep learning for biodiversity research and the ecological conservation of macrofungi species.
引用
收藏
页数:22
相关论文
共 50 条
  • [31] Deep learning-based segmentation for disease identification
    Mzoughi, Olfa
    Yahiaoui, Itheri
    ECOLOGICAL INFORMATICS, 2023, 75
  • [32] Deep learning-based identification of genetic variants: application to Alzheimer's disease classification
    Jo, Taeho
    Nho, Kwangsik
    Bice, Paula
    Saykin, Andrew J.
    BRIEFINGS IN BIOINFORMATICS, 2022, 23 (02)
  • [33] Deep learning-based application for fault location identification and type classification in active distribution
    Rizeakos, V.
    Bachoumis, A.
    Andriopoulos, N.
    Birbas, M.
    Birbas, A.
    APPLIED ENERGY, 2023, 338
  • [34] Identification and classification of pneumonia disease using a deep learning-based intelligent computational framework
    Yi, Rong
    Tang, Lanying
    Tian, Yuqiu
    Liu, Jie
    Wu, Zhihui
    NEURAL COMPUTING & APPLICATIONS, 2023, 35 (20) : 14473 - 14486
  • [35] Identification and classification of pneumonia disease using a deep learning-based intelligent computational framework
    Rong Yi
    Lanying Tang
    Yuqiu Tian
    Jie Liu
    Zhihui Wu
    Neural Computing and Applications, 2023, 35 : 14473 - 14486
  • [36] Deep learning-based bacterial genus identification
    Khan, Shafiur Rahman
    Khan, Ishrat
    Bag, Md. Abdus Sattar
    Uddin, Machbah
    Hassan, Md. Rakib
    Hassan, Jayedul
    JOURNAL OF ADVANCED VETERINARY AND ANIMAL RESEARCH, 2022, 9 (04) : 573 - 582
  • [37] Comparative Analysis of Deep Learning Models for Breast Cancer Classification on Multimodal Data
    Hussain, Sadam
    Ali, Mansoor
    Ali Pirzado, Farman
    Ahmed, Masroor
    Gerardo Tamez-Pena, Jose
    PROCEEDINGS OF THE FIRST INTERNATIONAL WORKSHOP ON VISION-LANGUAGE MODELS FOR BIOMEDICAL APPLICATIONS, VLM4BIO 2024, 2024, : 31 - 39
  • [38] Comparative Study for Optimized Deep Learning-Based Road Accidents Severity Prediction Models
    Hijazi, Hussam
    Sattar, Karim
    Al-Ahmadi, Hassan M.
    El-Ferik, Sami
    ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING, 2024, 49 (04) : 5853 - 5873
  • [39] Comparative Study for Optimized Deep Learning-Based Road Accidents Severity Prediction Models
    Hussam Hijazi
    Karim Sattar
    Hassan M. Al-Ahmadi
    Sami El-Ferik
    Arabian Journal for Science and Engineering, 2024, 49 : 5853 - 5873
  • [40] A comparative analysis and classification of cancerous brain tumors detection based on classical machine learning and deep transfer learning models
    Singh, Yajuvendra Pratap
    Lobiyal, D. K.
    MULTIMEDIA TOOLS AND APPLICATIONS, 2023, 83 (13) : 39537 - 39562