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Sputum smear microscopic examination is an effective, fast, and low-cost technique that is highly specific in areas with a high prevalence of pulmonary tuberculosis. Since manual screening needs trained pathologist in high prevalence zones, the possibility of deploying adequate technicians during the epidemic sessions would be impractical. This condition can cause overburdening and fatigue of working technicians which may tend to reduce the potential efficiency of Tuberculosis (TB) diagnosis. Hence, automation of sputum inspection is the most appropriate aspect in TB outbreak zones to maximize the detection ability. Sputum collection, smear preparing, staining, interpreting smears, and reporting of TB severity are all part of the diagnosis of tuberculosis. This study has analyzed the risk of automating TB severity grading. According to the findings of the analysis, numerous TB-positive cases do not fit into the standard TB severity grade while applying direct rule-driven strategy. The manual investigation, on the other hand, arbitrarily labels the TB grade on those cases. To counter the risk of automation, a fuzzy-based Tuberculosis Severity Level Categorizing Algorithm (TSLCA) is introduced to eliminate uncertainty in classifying the level of TB infection. TSLCA introduces the weight factors, which are dependent on the existence of maximum number of Acid-Fast Bacilli (AFB) per microscopic Field of View (FOV). The fuzzification and defuzzification operations are carried out using the triangular membership function. In addition, the α -cut approach is used to eliminate the ambiguity in TB severity grading. Several uncertain TB microscopy screening reports are tested using the proposed TSLCA. Based on the experimental results, it is observed that the TB grading by TSLCA is consistent, error-free, significant and fits exactly into the standard criterion. As a result of the proposed TSLCA, the uncertainty of grading is eliminated and the reliability of tuberculosis diagnosis is ensured when adapting automatic diagnosis. [ABSTRACT FROM AUTHOR]

• Effective ML that combines histogram statistics with support vector machine ability. • Explores saturation attainment of gamma-corrected versions to detect contrast change • Encodes pattern as slope of kurtosis and mean skewness displacement features. • Achieves excellent large-margin separability of images with 97% accuracy on Dresden Photo attack and anti-forgery techniques have been on the rise in the internet age. Attackers conceal tampering by applying anti-forgery techniques, such as contrast enhancement. Digital image forensics concerns with detecting traces of these operations subjected upon the image. In this work, a novel Anti-Forensic Contrast Enhancement Detector (AFCED) scheme has been proposed to assess contrast changes from anti-forensic digital images. The proposed model is based on the underlying observation that, with repeated gamma correction, the rate of attaining contrast saturation in normal images is greater than already contrast-improved images. Thus, we employ skewness and kurtosis of the gamma-corrected second-order derivative histograms as features to distinguish normal images from contrast-enhanced images. These features are robustly classified on a Support Vector Machine. The proposed model achieved 97.40% F1-score and 98.18% sensitivity on the Dresden and Wikiart image databases on-par with state-of-the-art results. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we propose a noise removal algorithm for digital images. This algorithm is based on hypergraph model of image, which enables us to distinguish noisy pixels in the image from the noise-free ones. Hence, our algorithm obviates the need for denoising all the pixels, thereby preserving as much image details as possible. The identified noisy pixels are denoised through Root Mean Square (RMS) approximation. The performance of our algorithm, based on peak-signal-to-noise-ratio (PSNR) and mean-absolute-error (MAE), was studied on various benchmark images, and found to be superior to that of other traditional filters and other hypergraph based denoising algorithms. [Copyright &y& Elsevier]