Parasite classification in copro images with transfer learning and data augmentation
Humans can harbor parasites; hence, it is fundamental an early detection to prevent diseases. Parasites can be observed in microscopic images, and computer vision may be a helpful approach to detect and classify those parasites in digital images. Deep learning models have shown to have a high perfor...
| Autores: | , , , , |
|---|---|
| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2022 |
| País: | México |
| Institución: | UNIVERSIDAD DE GUADALAJARA |
| Repositorio: | ReCIBE. Revista Electrónica de Computación, Informática, Biomédica y Electrónica |
| Idioma: | español |
| OAI Identifier: | oai:ojs.recibe.cucei.udg.mx:article/235 |
| Acceso en línea: | http://recibe.cucei.udg.mx/index.php/ReCIBE/article/view/235 |
| Access Level: | acceso abierto |
| Palabra clave: | Parasite Classification Data Augmentation Transfer Learning GAN AlexNet Clasificación de parásitos Aumento de datos Transferencia de aprendizaje ,AlexNet. |
| Sumario: | Humans can harbor parasites; hence, it is fundamental an early detection to prevent diseases. Parasites can be observed in microscopic images, and computer vision may be a helpful approach to detect and classify those parasites in digital images. Deep learning models have shown to have a high performance in image classification. Therefore, this article presents various multi-class deep learning classifiers to recognize 8 classes: 7 types of parasites and non-parasite class. The designed classifiers are based on transfer learning from an AlexNet modified architecture. By having a reduce amount of parasite images samples, a data augmentation was done, employing traditional methods and images generation with an adversarial neural network (GAN) designed for this purpose. The classifier with best performance presented a 99.94%, 98.97% and 98.18% accuracy in the for training, validation and testing sets, respectively. |
|---|