Pt. Jawahar Lal Nehru Memorial Medical College

About: Pt. Jawahar Lal Nehru Memorial Medical College is a education organization based out in Raipur, India. It is known for research contribution in the topics: Malaria & Medicine. The organization has 53 authors who have published 47 publications receiving 396 citations. The organization is also known as: Pt J.N.M. Medical College.

Coronavirus disease (COVID-19) detection in Chest X-Ray images using majority voting based classifier ensemble

Abstract: Novel coronavirus disease (nCOVID-19) is the most challenging problem for the world. The disease is caused by severe acute respiratory syndrome coronavirus-2 (SARS-COV-2), leading to high morbidity and mortality worldwide. The study reveals that infected patients exhibit distinct radiographic visual characteristics along with fever, dry cough, fatigue, dyspnea, etc. Chest X-Ray (CXR) is one of the important, non-invasive clinical adjuncts that play an essential role in the detection of such visual responses associated with SARS-COV-2 infection. However, the limited availability of expert radiologists to interpret the CXR images and subtle appearance of disease radiographic responses remains the biggest bottlenecks in manual diagnosis. In this study, we present an automatic COVID screening (ACoS) system that uses radiomic texture descriptors extracted from CXR images to identify the normal, suspected, and nCOVID-19 infected patients. The proposed system uses two-phase classification approach (normal vs. abnormal and nCOVID-19 vs. pneumonia) using majority vote based classifier ensemble of five benchmark supervised classification algorithms. The training-testing and validation of the ACoS system are performed using 2088 (696 normal, 696 pneumonia and 696 nCOVID-19) and 258 (86 images of each category) CXR images, respectively. The obtained validation results for phase-I (accuracy (ACC) = 98.062%, area under curve (AUC) = 0.956) and phase-II (ACC = 91.329% and AUC = 0.831) show the promising performance of the proposed system. Further, the Friedman post-hoc multiple comparisons and z-test statistics reveals that the results of ACoS system are statistically significant. Finally, the obtained performance is compared with the existing state-of-the-art methods.

Prenatal Depression and Its Associated Risk Factors Among Pregnant Women in Bangalore: A Hospital Based Prevalence Study

Abstract: Background: Depression is the commonest psychological problem that affects a woman during her perinatal period worldwide. The risk of prenatal depression increases as the pregnancy progresses and clinically significant depressive symptoms are common in the mid and late trimester. There is a paucity of research on depression during the prenatal period in India. Given this background, the present study aimed to assess the prevalence of prenatal depression and its associated risk factors among pregnant women in Bangalore, Southern India. Methods: The study was nested within an on-going cohort study. The study participants included 280 pregnant women who were attending the antenatal clinic at Jaya Nagar General Hospital (Sanjay Gandhi Hospital) in Bangalore. The data was collected by using a structured questionnaire which included. Edinburgh Postnatal Depression Scale (EPDS) to screen for prenatal depression. Results: The proportion of respondents who screened positive for prenatal depression was 35.7%. Presence of domestic violence was found to impose a five times higher and highly significant risk of developing prenatal depression among the respondents. Pregnancy related anxiety and a recent history of catastrophic events were also found to be a positive predictors of prenatal depression. Conclusion: The high prevalence of prenatal depression in the present study is suggestive of its significance as a public health problem. Health care plans therefore can include screening and diagnosis of prenatal depression in the antenatal care along with other health care facilities provided.

Automatic detection of tuberculosis related abnormalities in Chest X-ray images using hierarchical feature extraction scheme

Abstract: Machine learning techniques have been widely used for abnormality detection in medical images. Chest X-ray images (CXR) are among the non-invasive diagnostic tools used to detect various disease pathologies. The ambiguous anatomical structure of soft tissues is one of the major challenges for segregating normal and abnormal images. The main objective of this study is to mimic the expert radiologist’s interpretation procedure in computer-aided diagnosis (CAD) systems. We propose an automatic technique for detection of abnormal CXR images containing one or more pathologies like pleural effusion, infiltration, fibrosis, hila enlargement, dense consolidation, etc. due to tuberculosis (TB). The proposed abnormality detection technique is based on the hierarchical feature extraction scheme in which the features are used in two-level of hierarchy to categorize healthy and unhealthy groups. In level one the handcrafted geometrical features like shape, size, eccentricity, perimeter, etc. and in level 2 traditional first order statistical feature along with texture features like energy, entropy, contrast, correlation, etc. are extracted from segmented lung-fields. Further, a supervised classification approach is employed on the extracted features to detect normal and abnormal CXR images. The performance of the algorithm is validated on a total of 800 CXR images from two public datasets, namely the Montgomery set and Shenzhen set. The obtained results (accuracy = 95.60 ± 5.07% and area under curve (AUC) = 0.95 ± 0.06 for Montgomery collection, and accuracy = 99.40 ± 1.05% and AUC = 0.99 ± 0.01 for Shenzhen collection) shows the promising performance of the proposed technique for TB detection compared to the existing state of the art approaches. Further, the obtained results are statistically validated using Friedman post-hoc multiple comparison methods, which confirms the significance of the proposed method.