The latest World Health Organization's (WHO) study on 2018 is showing that about 1.5 million people died and around 10 million people are infected with tuberculosis (TB) each year. Moreover, more than 4,000 people die every day from TB. A number of those deaths could have been stopped if the disease was identified sooner. In the recent literature, important work can be found on automating the diagnosis by applying techniques of deep learning (DL) to the medical images. While DL has yielded promising results in many areas, comprehensive TB diagnostic studies remain limited. DL requires a large number of high-quality training samples to yield better performance. Due to the low contrast of TB chest x-ray (CXR) images, they are often is in poor quality. This work assesses the effect of image enhancement on performance of DL technique to address this problem. The employed image enhancement algorithm was able to highlight the overall or local characteristics of the images, including some interesting features. Specifically, three image enhancement algorithms called Unsharp Masking (UM), High-Frequency Emphasis Filtering (HEF) and Contrast Limited Adaptive Histogram Equalization (CLAHE), were evaluated. The enhanced image samples were then fed to the pre-trained ResNet and EfficientNet models for transfer learning. In a TB image dataset, we achieved 89.92% and 94.8% of classification accuracy and AUC (Area Under Curve) scores, respectively. All the results are obtained using Shenzhen dataset, which are available in the public domain.

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Background: Assessment and evaluation for students is an essential component of teaching and learning process. Item analysis is the technique of collecting, summarizing, and using students’ response data to assess the quality of the Multiple Choice Question (MCQ) test by measuring indices of difficulty and discrimination, also distracter efficiency. Peer review practices improve quality of assessment validity in evaluating student performance.Method: We analyzed 150 student’s responses for 100 MCQs in Block Examination for its difficulty index (p), discrimination index (D) and distractor efficiency (DE) using Microsoft excel formula. The Correlation of p and D was analyzed using Spearman correlation test by SPSS 23.0. The result was analyzed to evaluate the peer-review strategy.Results: The median of difficulty index (p) was 54% or within the range of excellent level (p 40-60%) and the mean of discrimination index (D) was 0.24 which is reasonably good. There were 7 items with excellent p (40–60%) and excellent D (≥0.4). Nineteen of items had excellent discrimination index (D≥0.4). However,there were 9 items with negative discrimination index and 30 items with poor discrimination index, which should be fully revised. Forty-two of items had 4 functioning distracters (DE 0%) which suggested the teacher to be more precise and carefully creating the distracters.Conclusion: Based on item analysis, there were items to be fully revised. For better test quality, feedback and suggestions for the item writer should also be performed as a part of peer-review process on the basis of item analysis.