Autokeras Approach: A Robust Automated Deep Learning Network for Diagnosis Disease Cases in Medical Images

Automated deep learning is promising in artificial intelligence (AI). However, a few applications of automated deep learning networks have been made in the clinical medical fields. Therefore, we studied the application of an open-source automated deep learning framework, Autokeras, for detecting smear blood images infected with malaria parasites. Autokeras is able to identify the optimal neural network to perform the classification task. Hence, the robustness of the adopted model is due to it not needing any prior knowledge from deep learning. In contrast, the traditional deep neural network methods still require more construction to identify the best convolutional neural network (CNN). The dataset used in this study consisted of 27,558 blood smear images. A comparative process proved the superiority of our proposed approach over other traditional neural networks. The evaluation results of our proposed model achieved high efficiency with impressive accuracy, reaching 95.6% when compared with previous competitive models.

Automated summary

Automated deep learning has shown promise in AI, but its applications in clinical medicine are limited. In this study, we used an open-source automated deep learning framework called Autokeras to detect malaria parasites in smear blood images. Autokeras can identify the optimal neural network for classification without prior knowledge from deep learning. Compared to traditional deep neural network methods, our proposed approach achieved superior results in analyzing a dataset of 27,558 blood smear images. The evaluation results showed high efficiency and impressive accuracy of 95.6%, outperforming previous competitive models.