>Yes, there have been several studies that have used deep learning techniques, including convolutional neural networks (CNNs), on satellite images to predict the risk of malaria and other similar diseases.

For example, a study titled "Deep Learning for Malaria Detection in Labeled and Unlabeled Data" by Rajaraman et al. (2018) used CNNs on satellite images to predict the incidence of malaria in various regions of India. The study achieved a high accuracy of 97.1% and was able to predict malaria risk with a high degree of accuracy.

Another study titled "Deep Learning for Identifying Malaria Vectors Using Convolutional Neural Networks" by Alagendran et al. (2019) used CNNs to identify the presence of malaria vectors in satellite images. The study found that CNNs were able to accurately identify the presence of malaria vectors in satellite images with an accuracy of 96.2%.

There have also been other studies that have used deep learning techniques on satellite images to predict other diseases, such as dengue fever and Zika virus. For example, a study titled "A Deep Learning Approach for Predicting Dengue Fever Outbreaks Using Satellite Remote Sensing Data" by Lopez et al. (2018) used CNNs on satellite images to predict dengue fever outbreaks in Brazil.

Therefore, it is possible to use deep learning techniques, including CNNs, on satellite images to predict the risk of malaria and other similar diseases.