A DYNAMIC APPROACH FOR BRAIN TUMOR DETECTION USING EDGE DETECTION TECHNIQUE

Image process is most typically victimized framework in medical imaging. A foundation uncovering is alive for its trustiness and warrant that delivers a stronger understanding of seeable representation within the applications of laptop modality, same prosy catching, confronting perception, and recording force succeed. Machine Learning and Deep Learning algorithms are principally victimized for analyzing the medical pictures which may make, stage and categorize the tumor into sub classes, coherent with that the identification would be through by the professionals. during this production, we've mentioned the technique that's used for tumor pre-processing, and sorting

Role of Duration of Diabetes on Ventilatory Capacities and Expiratory Flow Rates in Type 2 Diabetes Mellitus

Diabetes mellitus is a chronic debilitating problem with increasing incidence and long term complications such as diabetic nephropathy, diabetic neuropathy, diabetic retinopathy etc. These complications are mainly a consequence of macro vascular and micro vascular damages of the target organs. The magnitude of the complications of diabetes is related to its duration. Less has been known about the after effects of diabetes on lungs. So this work was carried out to know the relation between duration of diabetes and lung volumes and capacities in Type 2 DM patients. The presence of an extensive micro vascular circulation and abundant connective tissue in the lungs raises the possibility that lung tissue may be affected by Microangiopathy process and non-enzymatic glycosylation of tissue proteins, induced by chronic hyperglycemia, there by rendering the lung a “target organ” in diabetic patients.  This is a cross-sectional study, the test group were Type 2 Diabetes Mellitus patients (n=50) with duration of 2-35 years, the control group were staff of Narayana medical college (n=50). Written consent was obtained from them. The following lung function parameters were recorded: Forced Vital Capacity (FVC), Forced Expiratory Volume in the first second (FEV1), Forced Expiratory Volume percent (FEV1/FVC %), Peak Expiratory Flow Rate (PEFR), Forced Expiratory Flow 25-75% (FEF25-75%), Maximum Voluntary Ventilation (MVV). The mean FVC, FEV1, PEFR, FEF25-75%, MVV values are low in diabetics compared to controls (p value <0.001) and the parameters showed significant negative correlation with duration of diabetes. Key words: Chronic hyperglycemia, Diabetes mellitus, Microangiopathy, Micro vascular circulation, Pulmonary function test

Perceptual Characterization of the Macronutrient Picture System (MaPS) for Food Image fMRI

Food image fMRI paradigms are used widely for investigating the neural basis of ingestive behavior. However, these paradigms have not been validated in terms of ingestive behavior constructs, engagement of food-relevant neural systems, or test-retest reliability, making the generalizability of study findings unclear. Therefore, we validated the Macronutrient Picture System (MaPS) (McClernon et al., 2013), which includes food images from the six categories represented in the Geiselman Food Preference Questionnaire (FPQ) (Geiselman et al., 1998). Twenty-five healthy young adults ( = 21 female, mean age = 20.6 ± 1.1 years, mean BMI = 22.1 ± 1.9 kg/m) rated the MaPS images in terms of visual interest, appetitive quality, nutrition, emotional valence, liking, and frequency of consumption, and completed the FPQ. In a second study, 12 individuals (n=8 female, mean age = 25.0 ± 6.5 years, mean BMI = 28.2 ± 8.7 kg/m) viewed MaPS and control images (vegetables and non-food) during two separate 3T BOLD fMRI scans after fasting overnight. Intuitively, high fat/high sugar (HF/HS) and high fat/high complex carbohydrate (HF/HCCHO) images achieved higher liking and appetitive ratings, and lower nutrition ratings, than low fat/low complex carbohydrate/high protein (LF/LCHO/HP) images on average. Within each food category, FPQ scores correlated strongly with MaPS image liking ratings ( \u3c 0.001). Brain activation differences between viewing images of HF/HS and vegetables, and between HF/HCCHO and vegetables, were seen in several reward-related brain regions (e.g., putamen, insula, and medial frontal gyrus). Intra-individual, inter-scan agreement in a summary measure of brain activation differences in seven reward network regions of interest was high (ICC = 0.61), and was even higher when two distinct sets of food images with matching visual ratings were shown in the two scans (ICC = 0.74). These results suggest that the MaPS provides valid representation of food categories and reliably activates food-reward-relevant neural systems

Frequency of Missed Findings on Chest Radiographs (CXRs) in an International, Multicenter Study: Application of AI to Reduce Missed Findings

Background: Missed findings in chest X-ray interpretation are common and can have serious consequences. Methods: Our study included 2407 chest radiographs (CXRs) acquired at three Indian and five US sites. To identify CXRs reported as normal, we used a proprietary radiology report search engine based on natural language processing (mPower, Nuance). Two thoracic radiologists reviewed all CXRs and recorded the presence and clinical significance of abnormal findings on a 5-point scale (1—not important; 5—critical importance). All CXRs were processed with the AI model (Qure.ai) and outputs were recorded for the presence of findings. Data were analyzed to obtain area under the ROC curve (AUC). Results: Of 410 CXRs (410/2407, 18.9%) with unreported/missed findings, 312 (312/410, 76.1%) findings were clinically important: pulmonary nodules (n = 157), consolidation (60), linear opacities (37), mediastinal widening (21), hilar enlargement (17), pleural effusions (11), rib fractures (6) and pneumothoraces (3). AI detected 69 missed findings (69/131, 53%) with an AUC of up to 0.935. The AI model was generalizable across different sites, geographic locations, patient genders and age groups. Conclusion: A substantial number of important CXR findings are missed; the AI model can help to identify and reduce the frequency of important missed findings in a generalizable manner