In the control of MDR-TB among previously treated patients, lower socioeconomic groups, the expanding use of FDCs, and improving adherence to treatment by implementing Directly Observed Therapy Short Course-Plus (DOTS-Plus), strictly should become a priority that requires strong commitment and collaboration among health organizations.

Keratoconus (KC) is a noninflammatory ectatic disease characterized by progressive thinning and an apical cone-shaped protrusion of the cornea. In recent years, more and more researchers have been committed to automatic and semi-automatic KC detection based on corneal topography. However, there are few studies about the severity grading of KC, which is particularly important for the treatment of KC. In this work, we propose a lightweight KC grading network (LKG-Net) for 4-level KC grading (Normal, Mild, Moderate, and Severe). First of all, we use depth-wise separable convolution to design a novel feature extraction block based on the self-attention mechanism, which can not only extract rich features but also reduce feature redundancy and greatly reduce the number of parameters. Then, to improve the model performance, a multi-level feature fusion module is proposed to fuse features from the upper and lower levels to obtain more abundant and effective features. The proposed LKG-Net was evaluated on the corneal topography of 488 eyes from 281 people with 4-fold cross-validation. Compared with other state-of-the-art classification methods, the proposed method achieves 89.55% for weighted recall (W_R), 89.98% for weighted precision (W_P), 89.50% for weighted F1 score (W_F1) and 94.38% for Kappa, respectively. In addition, the LKG-Net is also evaluated on KC screening, and the experimental results show the effectiveness.