Cerebral toxoplasmosis is a frequent cause of focal brain lesions in the setting of immunodeficiency states, particularly the acquired immune deficiency syndrome (AIDS) and MR imaging is an important diagnostic modality to differentiate toxoplasmosis from tuberculoma, and primary central nervous system lymphoma with diverse therapeutic implications. Several imaging patterns have been described in cerebral toxoplasmosis. The “concentric target sign” is a recently described MRI sign on T2 weighted imaging of cerebral toxoplasmosis that has concentric alternating zones of hypo- and hyper intensities. It is believed to be more specific than the well known “eccentric target sign” in the diagnosis of cerebral toxoplasmosis and hence more useful in differentiation from other focal brain lesions in the context of AIDS. The concentric target sign, seen in deep parenchymal lesions is distinct from the surface based cortical “eccentric” target sign. The histopathological correlate of the latter has been recently described, but that of the concentric target sign is not known. In this study, we describe the neuropathological correlate of this “concentric target sign”, following post-mortem of a 40 year old man with AIDS associated cerebral toxoplasmosis. The concentric alternating zones of hypo/hyper/iso/and hyper intensities corresponded to zones of hemorrhage/fibrin rich necrosis with edema/coagulative compact necrosis/inflammation with foamy histiocytes admixed with hemorrhage forming the outer most zone respectively. The exclusive specificity of this sign in cerebral toxoplasmosis remains to be further elucidated.
In the world of Internet and social media, there are about 3.8 billion active social media users and 4.5 billion people accessing the internet daily. Every year there is a 9% growth in the number of users and half of the internet traffic consists of mostly bots. Bots are mainly categorized into two categories: good and bad bots; good bots consist of web crawlers and chat bots whereas bad bots consist of malicious bots which make up 20% of the traffic, the reason they are not good is that they are used for nefarious purposes, they can mimic human behavior, they can impersonate legal traffic, attack IoT devices and exploit their performance. Among all these concerns, the primary concern is for social media users as they represent a large group of active users on the internet, they are more vulnerable to breach of data, change in opinion based on data. Detection of such bots is crucial to prevent further mishaps. We use supervised Machine learning techniques in this paper such as Decision tree, K nearest neighbors, Logistic regression, and Naïve Bayes to calculate their accuracies and compare it with our classifier which uses Bag of bots’ word model to detect Twitter bots from a given training data set.
The human bones are categorized based on elemental micro architecture and porosity. The porosity of the inner trabecular bone is high that is 40-95% and the nature of the bone is soft and spongy whereas the cortical bone is harder and is less porous that is 5 to 15%. Osteoporosis is a disease that normally affects women usually after their menopause. It largely causes mild bone fractures and further stages lead to the demise of an individual. The detection of Osteoporosis in Lumbar Spine has been widely recognized as a promising way to frequent fractures. Therefore, premature analysis of osteoporosis will estimate the risk of the bone fracture which prevents life threats. The paper is systematized in two different sections to classify normal (non-osteoporosis) and abnormal(osteoporosis)Lumbar spine trabecular bone. In this method, the first section is based on discriminating the lumbar spine trabecular bone micro-architecture predisposing by means of first and second order directional derivative of Laplacian of Gaussian filter with different standard deviation to acquire the minimum and maximum responses. The dimension reduction of texture features, quantization and adjacent scale coding with weighted multipliers are used to lessen the intensity variations of texture features. The second section is based on the reduction of histogram features as a training data set for classification of normal and osteoporotic images of lumbar spine (L1-L4) using K-Nearest Neighborhood (KNN) classifier. The tested dataset result gives effective classification accuracy of 97.22% with lesser texture feature dimension. The usage of weight multiplier as well as quantization technique plays a major role for the improvement of accuracy to diagnose osteoporosis for an input noisy and noiseless image.
<p class="abstract">The human bones are categorized based on elemental micro architecture and porosity. The porosity of the inner trabecular bone is high that is 40-95% and the nature of the bone is soft and spongy where as the cortical bone is harder and is less porous that is 5 to 15%. Osteoporosis is a disease that normally affects women usually after their menopause. It largely causes mild bone fractures and further stages lead to the demise of an individual. This analysis is on the basis of bone mineral density (BMD) standards obtained through a variety of scientific methods experimented from different skeletal regions. The detection of osteoporosis in lumbar spine has been widely recognized as a promising way to frequent fractures. Therefore, premature analysis of osteoporosis will estimate the risk of the bone fracture which prevents life threats. This paper focuses on the advanced technology in imaging systems and fracture probability analysis of osteoporosis detection. The various segmentation techniques are explored to examine osteoporosis in particular region of the image and further significant attributes are extracted using different methods to classify normal and abnormal (osteoporotic) bones. The limitations of the reviewed papers are more in feature dimensions, lesser accuracy and expensive imaging modalities like computed tomography (CT), magnetic resonance imaging (MRI), and DEXA. To overcome these limitations it is suggested to have less feature dimensions, more accuracy and cost-effective imaging modality like X-ray. This is required to avoid bone fractures and to improve BMD with precision which further helps in the diagnosis of osteoporosis.</p>
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