Development of deep learning-based mobile application for the identification of Coccidia species in pigs using microscopic images

Coccidiosis is a gastrointestinal parasitic disease caused by different species of Eimeria and Isospora, poses a significant threat to pig farming, leading to substantial economic losses attributed to reduced growth rates, poor feed conversion, increased mortality rates, and the expense of treatment. Traditional methods for identifying Coccidia species in pigs rely on fecal examination and microscopic analysis, necessitating expert personnel for accurate species identification. To address this need, a deep learning-based mobile application capable of automatically identifying different species of Eimeria and Isospora was developed. The present study focused on six species, namely, E. debliecki, E. perminuta, E. porci, E. spinosa, E. suis, and Isospora suis, commonly found in pigs of the North Eastern Hill (NEH) region of India. Utilizing a two-stage approach, segmentation of coccidia oocysts in microscopic images using convolutional neural networks (CNNs), followed by species identification by same network was carried out in this work. Resource-efficient models, including EfficientNetB0, EfficientNetB1, MobileNet, and MobileNetV2, within an encoder-decoder architecture were utilized to extract features. Transfer learning was applied to enhance model accuracy during training. Additionally, a marker-controlled watershed algorithm was implemented to separate touching cells, thus reducing misclassification. The results demonstrate that all the developed models effectively segmented/classified Coccidia species, achieving mean Intersectionover-Union (m-IoU) values exceeding 0.92, with individual class IoU scores above 0.90. MobileNetV2 exhibited the highest m-IoU of 0.95, followed by EfficientNetB1 with an m-IoU of 0.94. For classification, MobileNetV2 demonstrated the highest performance, with accuracy, precision, and recall values of 0.93, 0.96, and 0.96, respectively. EfficientNetB1 yielded an accuracy of 0.91. The developed mobile application, tested on new data, achieved an identification accuracy of 91.0 %. These findings highlight the potential of deep learning-based mobile applications in effectively identifying Coccidia species in pigs, thus, providing a promising solution to mitigate reliance on expert personnel and laborious time-consuming experiments in this domain.