A Comprehensive Study on Deep Learning Models for the Detection of Ovarian Cancer and Glomerular Kidney Disease using Histopathological Images

Kumar, S J K Jagadeesh; Kanna, G. Prabu; Raja, D. Prem; Kumar, Yogesh

doi:10.1007/s11831-024-10130-6

Arch Computat Methods Eng

2024

DOI: 10.1007/s11831-024-10130-6

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A Comprehensive Study on Deep Learning Models for the Detection of Ovarian Cancer and Glomerular Kidney Disease using Histopathological Images

S J K Jagadeesh Kumar,

G. Prabu Kanna,

D. Prem Raja

et al.

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

Ozsari,

Kumru,

Ekinci

et al. 2024

Sensors

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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.

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

Ozsari,

Kumru,

Ekinci

et al. 2024

Sensors

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show abstract

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A Comprehensive Study on Deep Learning Models for the Detection of Ovarian Cancer and Glomerular Kidney Disease using Histopathological Images

Cited by 1 publication

References 42 publications

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

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

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