Abstract: According to a report released by the World Health Organization， the global incidence of malaria and tuberculosis remains high. Manual microscopy of thick and thin slides and sputum smears are an important method for the diagnosis of malaria and tuberculosis， and one of the disadvantages of this approach is that it is highly dependent on medical inspectors， prone to subjective misjudgment. The lack of highly skilled laboratory personnel in remote areas of low-income and developing countries， coupled with the variable shape， small size and uncertainty of plasmodium and mycobacterium tuberculosis in microscopic images and factors of some cell body uncertainty， resulted in difficult detection of plasmodium and mycobacterium tuberculosis. This paper proposes an improved faster R-CNN-based algorithm for automatic screening of plasmodium and mycobacterium tuberculosis from microscopic images. Firstly， the proposed algorithm addes a convolutional filter layer to the original Faster R-CNN framework and uses a deep residual network to extract features to improve the detection performance of the model. Then， this paper evaluates the performance of the improved model on two different microscopy tasks： AP value reached 94.55% on the thick-blood smear malaria micrograph image dataset， and 97.96% on the sputum smear tuberculosis microscopic image dataset. Compared with the original Faster R-CNN model， the improvement is 7.40 percentage points and 8.04 percentage points. The results show that the modified Faster R-CNN model can detect plasmodium and mycobacterium tuberculosis sites from images captured on a microscope eyepiece on a smartphone， reducing the dependence on manual microscopy and assisting researchers in diagnosis， which shows that the model is suitable for deployment in under-resourced areas.

Key words: deep learning, Faster R-CNN, microscopic images, malaria, tuberculosis, auxiliary diagnosis