A comparative analysis of deep learning architectures on high variation malaria parasite classification dataset
| Autor: | Hasib Zunair, M.R.C. Mahdy, Aimon Rahman, M. Sohel Rahman, Tamanna Rahman Reme |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Plasmodium
Context (language use) 02 engineering and technology Biology Machine learning computer.software_genre 03 medical and health sciences Deep Learning parasitic diseases 0202 electrical engineering electronic engineering information engineering medicine Animals Humans Parasites 030304 developmental biology 0303 health sciences Artificial neural network business.industry Deep learning 020207 software engineering Cell Biology General Medicine medicine.disease Class (biology) Object detection Malaria Diagnosis of malaria Databases as Topic Test set Artificial intelligence business computer Algorithms Developmental Biology |
| Zdroj: | Tissuecell. 69 |
| ISSN: | 1532-3072 |
| Popis: | Malaria, one of the leading causes of death in underdeveloped countries, is primarily diagnosed using microscopy. Computer-aided diagnosis of malaria is a challenging task owing to the fine-grained variability in the appearance of some uninfected and infected class. In this paper, we transform a malaria parasite object detection dataset into a classification dataset, making it the largest malaria classification dataset (63,645 cells), and evaluate the performance of several state-of-the-art deep neural network architectures pretrained on both natural and medical images on this new dataset. We provide detailed insights into the variation of the dataset and qualitative analysis of the results produced by the best models. We also evaluate the models using an independent test set to demonstrate the model's ability to generalize in different domains. Finally, we demonstrate the effect of conditional image synthesis on malaria parasite detection. We provide detailed insights into the influence of synthetic images for the class imbalance problem in the malaria diagnosis context. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full Text from ScienceDirect