APPLICATIONS OF THREE DIMENSIONAL FACIAL COMPUTED TOMOGRAPHY IN HEAD AND NECK REGION - A REVIEW

3D facial CT has evolved and revolutionized diagnosis leading to better management. The limitations of conventional 2D methods limit the therapeutic options related to leading to an erroneous treatment. Although 3D facial CT is expensive, its advantages outweigh the cons. They are used in surgery, prosthetic replacement, orthodontics and forensics.

An efficient breast cancer detection with secured cloud storage & reliability analysis using FMEA

Breast cancer is considered as a most dangerous type of cancer found in women among all the cancers. Around 2.3 million women in the world are affected by this cancer and there is no cure if it is left untreated at an earlier stage. Therefore, early diagnosis of this disease is an important consideration to save the life of millions of women. Many machine learning models have been evolved in the recent years for breast cancer detection. However, all the currently available works focused only on improving the prediction accuracy, they need more attention on providing reliable services. This work presents an efficient breast cancer detection mechanism using deep learning strategies. The various assortments like breast image shapes, the intensity of images, regions of an image, illuminations, and contrast are the conceivable factors that define breast cancer identification. This study offers a strong image detection process for breast cancer mammography images by considering the whole slide image. Here, the input process for the preprocessing stage will remove the noise present in the image using Gaussian Filter (GF). The preprocessed image moves to the image segmentation and then forward to the feature extraction for extracting the features of the images using Cauchy distribution-based segmentation and Shearlet based feature extraction. Then the specialized features can be isolated using the Entropy PCA based feature selection. Finally, the breast cancer area is to be detected as benign or malignant accurately by using the Unified probability with LSTM neural network classification (UP-LSTM) for whole slide image (WSI). The attained outcomes and the detected outcomes were stored in cloud using a security mechanism for further monitoring purposes. To provide an efficient security, a Bio-inspired Iterative Honey Bee (BI-IHB) encryption is employed which is decrypted on user request. The reliability of the stored data is then found using FMEA (Failure mode and effective analysis) approach. From the experimental analysis, it is observed that UP-LSTM classifier model offers accuracy of 99.26% , sensitivity of 100% , and precision value of 98.59% which is better than the other state of the art techniques.

The Impact of COVID-19 on Diabetes Mellitus and Hypertension: A Retrospective Study

COVID-19 is a pathogenic virus that caused a pandemic outbreak in December 2019. The impact of this virus may be severe in the patients having co-morbidities like diabetes, hypertension, Chronic Kidney Disease, cardiovascular disease, etc. Aim and Objectives: This study Aims in Assessing the impact of COVID-19 on Diabetic and Hypertensive patients as well as COVID-19 patients without any co-morbidities. Objective of this is to evaluate the association between COVID-19 and its risk factors (diabetes and hypertension) and to evaluate whether the severity of the symptoms in COVID-19 patients is due to comorbidities or past medications. Methodology: A Retrospective study was conducted in SRM Hospital (Medical Records Department) for a period of 3 Months with the study population 670 at the age group of 25, known case of Diabetes and Hypertension. Cases of Pregnant women are excluded from the study. The patients were grouped into 4 categories 1) control group (patient without any co-morbidities) 2) diabetic patients 3) Hypertensive patient 4) Diabetic+Hypertension patient and studied their prescribing pattern by collecting the past medication history. Results and Discussion: There is a significant decrease in a lymphocyte in covid-19 Type 2 diabetic patients in our study. These results suggest that different mechanism exists for hypertension and diabetes mellitus as risk factors for covid-19. It is also known that these patients have impaired immune response to many infections [30]. In our retrospective study, we collected 670 covid-19 cases. It consists of 12.5% of diabetic patients and 6.6% of hypertensive patients. This study compared COVID-19 patients without any comorbidity (neither Type 2 diabetes mellitus nor hypertension) with covid- 19 patients with comorbidities (Type 2 diabetes mellitus and hypertension). COVID-19 patients with T2DM have an increased level of D-dimer compared to non-T2DM patients. Conclusion: Diabetic and hypertensive patients affected with COVID-19 are low in our study. Out of the total study population, only 12.5% are diabetic, 6.56% are hypertensive, and 9.25% were both diabetic and hypertensive. But when comparing in terms of severity, hypertensive and diabetic patients have severe effects than the control patients. In simpler terms, not every person who has diabetes and hypertension are affected with COVID-19, but those who were affected by COVID-19 showed more severity than the patients who don't have any comorbidities