Classification of Gastric Lesions Using Gabor Block Local Binary Patterns

The identification of cancer tissues in Gastroenterology imaging poses novel challenges to the computer vision community in designing generic decision support systems. This generic nature demands the image descriptors to be invariant to illumination gradients, scaling, homogeneous illumination, and rotation. In this article, we devise a novel feature extraction methodology, which explores the effectiveness of Gabor filters coupled with Block Local Binary Patterns in designing such descriptors. We effectively exploit the illumination invariance properties of Block Local Binary Patterns and the inherent capability of convolutional neural networks to construct novel rotation, scale and illumination invariant features. The invariance characteristics of the proposed Gabor Block Local Binary Patterns (GBLBP) are demonstrated using a publicly available texture dataset. We use the proposed feature extraction methodology to extract texture features from Chromoendoscopy (CH) images for the classification of cancer lesions. The proposed feature set is later used in conjuncture with convolutional neural networks to classify the CH images. The proposed convolutional neural network is a shallow network comprising of fewer parameters in contrast to other state-of-the-art networks exhibiting millions of parameters required for effective training. The obtained results reveal that the proposed GBLBP performs favorably to several other state-of-the-art methods including both hand crafted and convolutional neural networks-based features

Determinant factors of deprassion: a survey among university students

We compared the determinant factors of depression; among faculties in university, among junior and senior students, and gender differences among representative samples of faculties in university. Centre for Epidemiological Studies Depression (CES-D) consisting for 20 questions was used to assess the status of well-being of students. A total of 240 students participated and completed the assessment forms. The results were then compared and analyzed using the IBM SPSS Statistics version 21. There was no effect of faculty on depression (p=0.854). The association between year of study and depression was not statistically significant (p≥0.05). Likewise, the association between gender and depression was statistically not significant (p≥0.05). The study revealed absence of statistically significant effect of faculty on depression. It was also found that gender and depression as well as year of study and depression were not statistically significant. Keyword

Gaussian mixture model based probabilistic modeling of images for medical image segmentation

In this paper, we propose a novel image segmentation algorithm that is based on the probability distributions of the object and background. It uses the variational level sets formulation with a novel region based term in addition to the edge-based term giving a complementary functional, that can potentially result in a robust segmentation of the images. The main theme of the method is that in most of the medical imaging scenarios, the objects are characterized by some typical characteristics such a color, texture, etc. Consequently, an image can be modeled as a Gaussian mixture of distributions corresponding to the object and background. During the procedure of curve evolution, a novel term is incorporated in the segmentation framework which is based on the maximization of the distance between the GMM corresponding to the object and background. The maximization of this distance using differential calculus potentially leads to the desired segmentation results. The proposed method has been used for segmenting images from three distinct imaging modalities i.e. magnetic resonance imaging (MRI), dermoscopy and chromoendoscopy. Experiments show the effectiveness of the proposed method giving better qualitative and quantitative results when compared with the current state-of-the-art. INDEX TERMS Gaussian Mixture Model, Level Sets, Active Contours, Biomedical Engineerin

Feasibility of Primary PCI as the Reperfusion Strategy for Acute ST elevation MI at PIMS

Objective: To determine the feasibility of primary PCI in terms of frequency of patients with acute ST elevation MI found to have a first-medical-contact to needle time of 90 minutes.Methodology: The descriptive, cross sectional case series was conducted at department of Cardiology, PIMS, Islamabad from January 2017 to April 2017Results: A total of 350 patients were enrolled into the study, 67% (235) of which were males and 33% (115) females. Mean age of the participating population was 54 ±7.8 years. The mean first medical contact needle time was 2.3±1.1 hours, out of which a vast majority (73%) fell into the 90 minutes range. The patients had a median first medical contact to needle time of 74 minutes.Conclusion: The study concluded that majority of patients presenting to the emergency department with acute STEMI were found to be feasible for primary PCI as the reperfusion strategy with an FMC to needle time of less than 90 minutes. Therefore, accelerated efforts need to be made to develop this center as a primary PCI capable facility providing such standards of care to patients with ST-elevation MI

Depression as a Risk Factor for Coronary Artery Disease: Myth or Verity

Objectives: To determine the frequency of depression in patients with ischemic heart disease, subgroup analysis of prevalence of depression in patients with heart failure, acute STEMI and non STEMI-ACS and the effect of hospital stay and treatment of primary cardiac illness on depression scores.Methodology: All patients with heart failure, acute STEMI and non STEMI-ACS, presenting to cardiology clinics over a period of March-August, 2016 with a pre-calculated sample size were enrolled into the study by consecutive sampling. HAM-D questionnaire was administered at the time of hospital admission and discharge. SPSS was used for data analysis.Results: A total of 102 patients were included in the study out of which 47 (46%) were females and 55 (54%) were males. The mean age of the study population was 49.5±12 years. At the time of admission, 91/102 (89.2%) patients were found to be depressed, 32 (31.4%) had mild depression, 29 (28.4%) had moderate depression, 10 (9.8%) had severe depression and an alarming number (20 i.e. 19.6%) patients had very severe depression. At the time of discharge, 82/102 (80.3%) patients were found to be depressed, 35 (34.3%) had mild depression, 31 (30.4%) had moderate depression, 12 (11.8%) had severe depression and only 4 (3.9%) had very severe depression. The mean change in HAM-D score during hospital admission was -3.24±4 (Maximum +26, minimum -23). The difference in depression scores during hospital stay tended to inversely correlate with length of hospital stay. A greater proportion of patients with the diagnosis of STEMI had a severe or very severe depression.Conclusion: Depression was found to be alarmingly prevalent in acute coronary syndrome affectees and hospital stay and treatment led to a mean fall in the depression scores

Association of blood donation related fears with donors’ characteristics and their impact on future donation

Background: To find the association of blood donation related fears with the donors’ characteristics like age, education level and previous donation history and to find their impact on future donation. Methods: It was a cross-sectional study carried out at a tertiary care hospital in Lahore, Pakistan, from June to December 2022. A self-designed questionnaire addressing five types of fears was filled from 700 blood donors through interview after taking informed consent. Data was analyzed by using IBM-SPSS V23. Results: Among 700 participants, 91.1% were male with mean age ±SD of 28.9±7.1years and 8.9% were female with mean age of 24.5±3.7years. For previous donation experience, the cohort was divided into five subgroups in which 19.7% had never donated blood and 11.3% had six or more donations. Fears were significantly reduced among donors with frequent donations compared to those with reduced donations. However, 4-5% had some retained fears. Reduced fears were observed in donors with higher education except for the fear of needle. Fears of having blood drawn/seeing blood and fear of fainting were more in younger donors. Out of 5.71% donors who were not willing for voluntary blood donation in future, 95% had fears. Conclusions: Frequent blood donations, higher education level and age more than 30 years were associated with reduced blood donation associated fears among blood donors. The presence of fears has negative relation with willingness for future blood donations

Improving the robustness of neural networks using K-support norm based adversarial training

It is of significant importance for any classification and recognition system, which claims near or better than human performance to be immune to small perturbations in the dataset. Researchers found out that neural networks are not very robust to small perturbations and can easily be fooled to persistently misclassify by adding a particular class of noise in the test data. This, so-called adversarial noise severely deteriorates the performance of neural networks, which otherwise perform really well on unperturbed dataset. It has been recently proposed that neural networks can be made robust against adversarial noise by training them using the data corrupted with adversarial noise itself. Following this approach, in this paper, we propose a new mechanism to generate a powerful adversarial noise model based on K-support norm to train neural networks. We tested our approach on two benchmark datasets, namely the MNIST and STL-10, using muti-layer perceptron and convolutional neural networks. Experimental results demonstrate that neural networks trained with the proposed technique show significant improvement in robustness as compared to state-of-the-art techniques

Enhanced Spatial Stream of Two-Stream Network Using Optical Flow for Human Action Recognition

Introduction: Convolutional neural networks (CNNs) have maintained their dominance in deep learning methods for human action recognition (HAR) and other computer vision tasks. However, the need for a large amount of training data always restricts the performance of CNNs. Method: This paper is inspired by the two-stream network, where a CNN is deployed to train the network by using the spatial and temporal aspects of an activity, thus exploiting the strengths of both networks to achieve better accuracy. Contributions: Our contribution is twofold: first, we deploy an enhanced spatial stream, and it is demonstrated that models pre-trained on a larger dataset, when used in the spatial stream, yield good performance instead of training the entire model from scratch. Second, a dataset augmentation technique is presented to minimize overfitting of CNNs, where we increase the dataset size by performing various transformations on the images such as rotation and flipping, etc. Results: UCF101 is a standard benchmark dataset for action videos, and our architecture has been trained and validated on it. Compared with the other two-stream networks, our results outperformed them in terms of accuracy