Automatic characterisation of dye decolourisation in fungal strains using expert, traditional, and deep features

Arredondo-Santoyo, Marina; Domínguez, César; Heras, Jónathan; Mata, Eloy; Pascual, Vico; Vázquez-Garcidueñas, Maria Soledad; Vázquez-Marrufo, Gerardo

doi:10.1007/s00500-019-03832-8

Cited by 10 publications

(7 citation statements)

References 57 publications

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“…Blindness [20] 3662 5 Diabetic retinopathy images Chest X Ray [22] 2355 2 Chest X-Rays images Fungi [1] 1204 4 Dye decolourisation of fungal strain HAM 10000 [44] 10015 7 Dermatoscopic images of skin lesions ISIC [6] 1500 7 Colour images of skin lesions Kvasir [31] 8000 8 Gastrointestinal disease images Open Sprayer [21] 6697 2 Dron pictures of broad leaved docks Plants [11] 5500 12 Colour images of plants Retinal OCT [22] 84484 4…”

Section: Number Of Images Number Of Classes Descriptionmentioning

confidence: 99%

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Semi-Supervised Learning for Image Classification using Compact Networks in the BioMedical Context

Inés¹,

Díaz-Pinto²,

Domínguez³

et al. 2022

Preprint

Self Cite

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Background and objectives: The development of mobile and on the edge applications that embed deep convolutional neural models has the potential to revolutionise biomedicine. However, most deep learning models require computational resources that are not available in smartphones or edge devices; an issue that can be faced by means of compact models. The problem with such models is that they are, at least usually, less accurate than bigger models. In this work, we study how this limitation can be addressed with the application of semi-supervised learning techniques.Methods: We conduct several statistical analyses to compare performance of deep compact architectures when trained using semi-supervised learning methods for tackling image classification tasks in the biomedical context. In particular, we explore three families of compact networks, and two families of semi-supervised learning techniques for 10 biomedical tasks.Results: By combining semi-supervised learning methods with compact networks, it is possible to obtain a similar performance to standard size networks. In general, the best results are obtained when combining data distillation with MixNet, and plain distillation with ResNet-18. Also, in general, NAS networks obtain better results than manually designed networks and quantized networks.Conclusions: The work presented in this paper shows the benefits of apply semisupervised methods to compact networks; this allow us to create compact models that are not only as accurate as standard size models, but also faster and lighter. Finally, we have developed a library that simplifies the construction of compact models using semi-supervised learning methods.

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Section: Number Of Images Number Of Classes Descriptionmentioning

confidence: 99%

“…All the networks used in our experiments are implemented in Pytorch [30], and have been trained thanks to the functionality of the FastAI library [16] using a GPU Nvidia RTX 2080 Ti with 11 GB RAM. In addition, the two quantized newtorks were built using the Pytorch quantization API 1 .…”

Section: Training and Evaluation Proceduresmentioning

confidence: 99%

Semi-Supervised Learning for Image Classification using Compact Networks in the BioMedical Context

Inés¹,

Díaz-Pinto²,

Domínguez³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…The model achieved promising classification accuracy of 94.8%.The authors also proposed a novel dataset consisted of 40,800 labeled fungus spores images. Arredondo-Santoyo et al [ 119 ] presented a transfer learning based approach for the characterization of dye de-colorization in fungal strains. The study was performed on an imbalanced dataset of 1024 fungi assay images.…”

Section: In Microorganisms Image Recognitionmentioning

confidence: 99%

“…= 10,800 Acc. = 94.8% Image quality needs to be improved Arredondo-Santoyo et al [ 119 ] To Characterise the dye decolourisation of fungal strains – Texture features, adhoc expert features and deep features Extremely randomized trees, SVM, KNN, MLP, RF, logistic regression C = 4 TI = 1024 Acc. = 96.5% Less details about training and test sets Zhou et al [ 120 ] Identification of Aspergillus Terreus Threshold method Deep features CNN C = 3 TI = 70 Acc.…”

Section: In Microorganisms Image Recognitionmentioning

confidence: 99%

Machine Learning and Deep Learning Based Computational Approaches in Automatic Microorganisms Image Recognition: Methodologies, Challenges, and Developments

Rani

Kotwal

Manhas

et al. 2021

Arch Computat Methods Eng

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Microorganisms or microbes comprise majority of the diversity on earth and are extremely important to human life. They are also integral to processes in the ecosystem. The process of their recognition is highly tedious, but very much essential in microbiology to carry out different experimentation. To overcome certain challenges, machine learning techniques assist microbiologists in automating the entire process. This paper presents a systematic review of research done using machine learning (ML) and deep leaning techniques in image recognition of different microorganisms. This review investigates certain research questions to analyze the studies concerning image pre-processing, feature extraction, classification techniques, evaluation measures, methodological limitations and technical development over a period of time. In addition to this, this paper also addresses the certain challenges faced by researchers in this field. Total of 100 research publications in the chronological order of their appearance have been considered for the time period 1995–2021. This review will be extremely beneficial to the researchers due to the detailed analysis of different methodologies and comprehensive overview of effectiveness of different ML techniques being applied in microorganism image recognition field.

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“…The best model was Resnet-C-SVM with an accuracy of 96.5%. [121] "Classification of rubberized coir fibres using deep learningbased neural fuzzy decision tree approach"…”

Section: Images Of Dye Decolourisation Assaysmentioning

confidence: 99%

Smart Farming Becomes Even Smarter With Deep Learning—A Bibliographical Analysis

Ünal

2020

IEEE Access

100

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Smart farming is a new concept that makes agriculture more efficient and effective by using advanced information technologies. The latest advancements in connectivity, automation, and artificial intelligence enable farmers better to monitor all procedures and apply precise treatments determined by machines with superhuman accuracy. Farmers, data scientists and, engineers continue to work on techniques that allow optimizing the human labor required in farming. With valuable information resources improving day by day, smart farming turns into a learning system and becomes even smarter. Deep learning is a type of machine learning method, using artificial neural network principles. The main feature by which deep learning networks are distinguished from neural networks is their depth and that feature makes them capable of discovering latent structures within unlabeled, unstructured data. Deep learning networks that do not need human intervention while performing automatic feature extraction have a significant advantage over previous algorithms. The focus of this study is to explore the advantages of using deep learning in agricultural applications. This bibliography reviews the potential of using deep learning techniques in agricultural industries. The bibliography contains 120 papers from the database of the Science Citation Index on the subject that were published between 2016 and 2019. These studies have been retrieved from 39 scientific journals. The papers are classified into the following categories as disease detection, plant classification, land cover identification, precision livestock farming, pest recognition, object recognition, smart irrigation, phenotyping, and weed detection.

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Automatic characterisation of dye decolourisation in fungal strains using expert, traditional, and deep features

Cited by 10 publications

References 57 publications

Semi-Supervised Learning for Image Classification using Compact Networks in the BioMedical Context

Semi-Supervised Learning for Image Classification using Compact Networks in the BioMedical Context

Machine Learning and Deep Learning Based Computational Approaches in Automatic Microorganisms Image Recognition: Methodologies, Challenges, and Developments

Smart Farming Becomes Even Smarter With Deep Learning—A Bibliographical Analysis

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