A reliable and low-cost deep learning model integrating convolutional neural network and transformer structure for fine-grained classification of chicken Eimeria species

He, Pengguang; Chen, Zhonghao; He, Yefan; Chen, Jin-Tian; Hayat, Khawar; Pan, Jinming; Lin, Hongjian

doi:10.1016/j.psj.2022.102459

Cited by 5 publications

(4 citation statements)

References 36 publications

(48 reference statements)

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“…An automated model for speciation of Eimeria sp. oocysts, with an accuracy of 96.9%, has been published recently [21]. For this validation model, these researchers used a publicly available database (http://www.coccidia.icb.usp.br/ accessed on 15 January 2023 [22], which only contains sporulated oocysts.…”

Section: Discussionmentioning

confidence: 99%

Automated Image Analysis for Detection of Coccidia in Poultry

Kellogg,

Roberts,

Crespo

2024

Animals

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Coccidiosis, caused by the protozoan Eimeria sp., is one of the most common and costly diseases impacting the poultry industry. To establish effective control measures, it is essential to identify these protozoa. Typical methods for identifying and determining the severity of the protozoal infection include intestinal lesion scoring or enumeration of the protozoal oocysts in fecal samples. Standard analysis methods require highly skilled technicians or veterinarians to manually identify and manually enumerate these protozoal parasites. This process is labor intensive, time-consuming, and susceptible to human error. None of the current methods available, including molecular flow cytometry or even digital image analysis, can determine if an oocyst is sporulated or not. Oocysts are not infectious until they sporulate. The goal of this study was to design an automated model using Artificial Intelligence (AI) to expedite the process of enumeration, improve the efficiency and accuracy of the species identification, and determine the ability of the oocysts to infect. To this end, we trained and evaluated computer vision models based on the Mask RCNN neural network architecture. A model was trained to detect and differentiate three species and to determine sporulation for each (totaling six detection groups). This model achieved a mean relative percentage difference (RPD) of 5.64%, representing a slight overcount compared to manual counts, averaging across all groups. The mean RPD for each group individually fell within a range from −33.37% to 52.72%. These results demonstrate that these models were speedy and had high agreement with manual counts, with minimal processing of field-quality samples. These models also could differentiate the sporulation status of the oocysts, providing critical diagnostic information for potential field applications.

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Section: Discussionmentioning

confidence: 99%

Automated Image Analysis for Detection of Coccidia in Poultry

Kellogg,

Roberts,

Crespo

2024

Animals

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“…S. Amraei et al 48) used ANN model to implement an automatic weight estimation approach. Through the analysis of diverse chicken images, researchers such as Z. Xu et al 69) , T. Fang et al 66) , X. Qiang et al 63) , A. G. R et al 67) , P. He et al 39) , A. A.…”

Section: A Comprehensive Chicken Poultry Study Based On Image Analysismentioning

confidence: 99%

Chicken Diseases Detection and Classification Based on Fecal Images Using EfficientNetB7 Model

Vandana,

Kuldeep Kumar Yogi,

Satya Prakash Yadav

2024

Evergreen

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The agriculture sector, particularly the chicken and poultry industries, is under pressure to produce more due to the increased demand for livestock products among consumers. Increased poultry production can lead to the enhanced spread of many infectious diseases in chickens, which can result in high bird fatality rates and significant financial losses. A shortage of trustworthy professionals or delayed diagnosis cause farmers to be losing an extensive number of domestic chickens. Deep learning algorithms can help the early detection of illnesses. This paper proposes a system based on convolutional neural networks to categorize chicken illnesses by identifying healthy and harmful fecal images. Unhealthy images may indicate a poultry illness. Through the use of deep learning algorithms and image analysis of chicken feces, the most common illnesses that affect chickens may be rapidly identified. With the use of a convolutional neural network (CNN) architecture, this research developed a model to identify different chicken ailments by classifying fecal images into two groups: those representing symptoms associated with healthy conditions and those representing symptoms associated with potentially dangerous conditions like Newcastle diseases, Coccidiosis, or Salmonella. To determine if chicken feces fell into one of four categories with the least amount of loss utilized the EfficientNetB7 model with additional layers that extracted the most appropriate features from the fecal images and achieved the highest accuracy. With an accuracy of 97.07%, the new proposed model generated the greatest results when compared to the aforementioned models.

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“… Zhao et al (2023) proposes a real-time mutton multipart classification and detection method using Swin-Transformer. He et al (2023b) presents Residual-Transformer-Fine-Grained ( ResTFG ), a model merging transformer and CNN for precise classification of 7 chicken Eimeria species from microscopic images.…”

Section: Introductionmentioning

confidence: 99%

CarcassFormer: an end-to-end transformer-based framework for simultaneous localization, segmentation and classification of poultry carcass defect

Tran,

Truong,

Fernandes

et al. 2024

Poultry Science

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A reliable and low-cost deep learning model integrating convolutional neural network and transformer structure for fine-grained classification of chicken Eimeria species

Cited by 5 publications

References 36 publications

Automated Image Analysis for Detection of Coccidia in Poultry

Automated Image Analysis for Detection of Coccidia in Poultry

Chicken Diseases Detection and Classification Based on Fecal Images Using EfficientNetB7 Model

CarcassFormer: an end-to-end transformer-based framework for simultaneous localization, segmentation and classification of poultry carcass defect

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