Automated Brain Tumor Detection using Ideal Shallow Neural Network with Artificial Jellyfish Optimization.

INTRODUCTION: Brain tumors are predicted from Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) scan images. In recent years, image processing-based automated tools are developed to predict tumor areas with less human interference. However, such automated tools are suffering from computational complexity and reduced accuracy in certain critical images. In the proposed work, an Ideal Shallow Neural Network (ISNN) is utilized to improve the prediction accuracy, and the computational complexity is reduced by implementing an Artificial Jellyfish Optimization (AJO) algorithm for minimizing the feature dimensionality. METHOD: The proposed method utilizes MRI images for the verification process as they are more informative than the CT scan image. The BRATS and the Kaggle datasets are used in this work and a Gabor filtering technique is used for noise reduction and a histogram equalization is used for enhancing the tumor boundary regions. The classification results observed from the AJO-ISNN are further forwarded towards the segmentation process and which uses the Centroid Weighted Segmentation (WCS) along with a Grasshopper Optimization Algorithm (GOA) for improving the segmentation over the boundary regions of the brain tumor. RESULT: The experimental result indicates a classification accuracy of 95.14% on the proposed AJO-ISNN model and AJO-ISNN is comparatively better than the Convolutional Neural Network (CNN) model accuracy of 85.41% and VGG 19 model accuracy of 93.75% while implemented with the AJO optimization model. Similarly, the Dice Similarity Coefficient of the proposed CWS-GOA also reaches 93.15% when performed with both BRATS and Kaggle datasets. CONCLUSION: Apart from the accuracy attainments the proposed work classifies and segments the tumor region in around 65 seconds on average of 200 image verifications and that is comparatively better than the previous multi-cascaded CNN and the InceptionV3 models.

Zaluzanin D: a fungistatic sesquiterpene from Vernonia arborea.

Zaluzanin D, a sesquiterpene lactone has been isolated from the aerial parts of Vernonia arborea as a major constituent (0.04%). Antifungal activity of zaluzanin D has been studied against six plant-pathogenic fungi.

Steatosis of granular pneumocytes in alcoholics with acute alveolar injury.

Accumulation of neutral lipid in the type II alveolar epithelial cells of the lung has been described in experiments involving animals with conditions such as hypoxia or on alcohol administration. In two cases involving human subjects, this change was observed at autopsy by histochemical stains and electron microscopy. In both instances, the patients had had severe alcoholic liver disease, as well as extreme hypoxia resulting from acute alveolar injury. The lungs of six alcoholic patients with liver disease but without acute alveolar injury showed no lipid vesicles on histochemical staining. These observations suggest that a metabolic insult or combination of insults, such as alcohol or hypoxia, might lead to accumulation of neutral lipid, especially in regenerating alveolar epithelial cells that may be more susceptible to such injury.