Pneumonia remains a highly prevalent and dangerous infectious disease of the respiratory system, with early and accurate detection deemed essential for effective treatment. This work is concerned with developing an automatic system for the recognition of pneumonia, named the Pneumonia Recognition System (PRS), through the analysis of digital chest X-ray images to assist in the diagnosis of this lethal disease. This system aims to improve the effectiveness of medical staff in early pneumonia detection through a quick and reliable diagnostic tool. Our PRS uses state-of-the-art techniques with the help of innovations; more specifically, CNNs analyze chest X-rays for signs indicative of pneumonia. We developed an extensive dataset in the thousands, which included X-ray images, for training and validating the model across different pneumonia and non-pneumonia cases. The model was trained and fine-tuned rigorously, which has led to great accuracy and sensitivity in the detection of pneumonia. It has further been tuned to reduce the number of false positives by large margins, which is an essential consideration in the clinical setup. In addition, time and workload burdens on the radiologists are poised to be reduced quite substantially from the exceptional diagnostic powers vested in this PRS so that they can better use their expertise more efficiently. In addition, it will be an essential tool in places where access to health facilities is poor and mainly because of this existing gap in early detection and treatment. Experimental results show the effectiveness and robustness of the proposed Pneumonia Recognition System. It holds promise to serve as an adjunctive diagnostic tool to better care for and improve patient outcomes in the fight against pneumonia. This requires further validation and clinical integration to realize its full benefit to patients and healthcare providers.

Chest Radiography is a non-invasive imaging modality for diagnosing and managing chronic lung disorders, encompassing conditions such as pneumonia, tuberculosis, and COVID-19. While it is crucial for disease localization and severity assessment, existing computer-aided diagnosis (CAD) systems primarily focus on classification tasks, often overlooking these aspects. Additionally, prevalent approaches rely on class activation or saliency maps, providing only a rough localization. This research endeavors to address these limitations by proposing a comprehensive multi-stage framework. Initially, the framework identifies relevant lung areas by filtering out extraneous regions. Subsequently, an advanced fuzzy-based ensemble approach is employed to categorize images into specific classes. In the final stage, the framework identifies infected areas and quantifies the extent of infection in COVID-19 cases, assigning severity scores ranging from 0 to 3 based on the infection's severity. Specifically, COVID-19 images are classified into distinct severity levels, such as mild, moderate, severe, and critical, determined by the modified RALE scoring system. The study utilizes publicly available datasets, surpassing previous state-of-the-art works. Incorporating lung segmentation into the proposed ensemble-based classification approach enhances the overall classification process. This solution can be a valuable alternative for clinicians and radiologists, serving as a secondary reader for chest X-rays, reducing reporting turnaround times, aiding clinical decision-making, and alleviating the workload on hospital staff.

Delivery Tracker" is a comprehensive delivery management system designed to streamline package delivery services within an educational institution. The primary goal of this initiative is to simplify the process for students by allowing them to effortlessly create, monitor, and confirm the delivery of their packages. Leveraging cutting-edge technologies such as Express, Next. js, React, MongoDB, Paytm UPI, and Socket. io, this system promises efficiency and convenience. With "Delivery Tracker," senders can initiate the delivery process by furnishing recipient details, specifying package attributes, and supplying essential information. Each package is allocated a unique identifier, and the system promptly notifies a nearby student who is available to collect and deliver the package to the intended recipient’s address. Real-time tracking capabilities ensure that deliveries are not only prompt but also secure. Upon successful delivery, recipients can confirm the receipt of their package through the system. Furthermore, the system boasts an integrated payment gateway, streamlining the transaction process. "Delivery Tracker" represents a significant leap forward in enhancing the package delivery experience within educational institutions, prioritizing simplicity, efficiency, and transparency.

ObjectiveTo compare the renal artery (RA) flow indices (RI and PI) among normohydramnios, idiopathic oligohydramnios, and polyhydramnios and determine applicability of fetal RA Doppler indices in predicting the pregnancy outcome.MethodsTotal 106 3rd trimester pregnant patients were divided into cases and controls based on amniotic fluid index. Routine antenatal and color Doppler (including kidneys) ultrasound was performed for all patients in this study. The postnatal follow‐up was done, and the pregnancies having poor outcomes in terms of NICU admissions were assessed.ResultsStatistically significant differences were noted when comparing RI and PI values of normohydramnios (0.91 ± 0.04 and 2.38 ± 0.21, respectively) with oligohydramnios (1.02 ± 0.07 and 2.99 ± 0.38, respectively) and polyhydramnios (0.90 ± 0.12 and 2.7 ± 0.84, respectively) independently (p value < 0.05). Our study demonstrated an increase in NICU admissions in the fetus having raised values of RI and PI.ConclusionFetal RA RI and PI can be used as an antenatal predictor for the pregnancy outcome associated with idiopathic oligohydramnios; however, fetal RA PI was found to be a better predictor for the pregnancy outcome than fetal RA RI value in our study.

Automatic computer-aided diagnosis (CAD) system has been widely used as an assisting tool for mass screening and risk assessment of infectious pulmonary diseases (PDs). However, such a system still lacks clinical acceptability and trust due to the integration gap between the patient’s metadata, radiologist feedback, and the CAD system. This paper proposed three integration frameworks, namely—direct integration (DI), rule-based integration (RBI), and weight-based integration (WBI). The proposed framework helps clinicians diagnose lung inflammation and provide an end-to-end robust diagnostic system. Initially, the feasibility of integrating patients’ symptoms, clinical pathologies, and radiologist feedback with CAD system to improve the classification performance is investigated. Subsequently, the patient’s metadata and radiologist feedback are integrated with the CAD system using the proposed integration frameworks. The proposed method’s performance is evaluated using a private dataset consisting of 70 chest X-ray (CXR) images (31 COVID-19, 14 other diseases, and 25 normal). The obtained results reveal that the proposed WBI achieved the highest classification performance (accuracy = 98.18%, F1 score = 97.73%, and Matthew’s correlation coefficient = 0.969) compared to DI and RI. The generalization capability of the proposed framework is also verified from an external validation set. Furthermore, the Friedman average ranking and Shaffer and Holm post hoc statistical methods reveal the obtained results’ statistical significance. Methodological diagram of proposed integration frameworks

Background and Objectives: Chest X-ray (CXR) is a non-invasive imaging modality used in the prognosis and management of chronic lung disorders like tuberculosis (TB), pneumonia, coronavirus disease (COVID-19), etc. The radiomic features associated with different disease manifestations assist in detection, localization, and grading the severity of infected lung regions. The majority of the existing computeraided diagnosis (CAD) system used these features for the classification task, and only a few works have been dedicated to disease-localization and severity scoring. Moreover, the existing deep learning approaches use class activation map and Saliency map, which generate a rough localization. This study aims to generate a compact disease boundary, infection map, and grade the infection severity using proposed multistage superpixel classification-based disease localization and severity assessment framework. Methods: : The proposed method uses a simple linear iterative clustering (SLIC) technique to subdivide the lung field into small superpixels. Initially, the different radiomic texture and proposed shape features are extracted and combined to train different benchmark classifiers in a multistage framework. Subsequently, the predicted class labels are used to generate an infection map, mark disease boundary, and grade the infection severity. The performance is evaluated using a publicly available Montgomery dataset and validated using Friedman average ranking and Holm and Nemenyi post-hoc procedures. Results: : The proposed multistage classification approach achieved accuracy (ACC)= 95.52%, F-Measure (FM)= 95.48%, area under the curve (AUC)= 0.955 for Stage-I and ACC=85.35%, FM=85.20%, AUC=0.853 for Stage-II using calibration dataset and ACC = 93.41%, FM = 95.32%, AUC = 0.936 for Stage-I and ACC = 84.02%, FM = 71.01%, AUC = 0.795 for Stage-II using validation dataset. Also, the model has demonstrated the average Jaccard Index (B) of 0.82 and Pearson's correlation coefficient (r) of 0.9589. Conclusions: : The obtained classification results using calibration and validation dataset confirms the promising performance of the proposed framework. Also, the average JI shows promising potential to localize the disease, and better agreement between radiologist score and predicted severity score (r) confirms the robustness of the method. Finally, the statistical test justified the significance of the obtained results. (C) 2022 Elsevier B.V. All rights reserved.

Chest X-ray (CXR) is the most popular imaging modality used for preliminary diagnosis of pulmonary diseases. In automatic computer-aided diagnosis (CAD), the number of false-positive cases can be reduced by segmenting out the normal anatomical structures. Demarcating lung parenchyma on CXR image is challenging due to complex anatomical structures of the human thoracic cavity. The extracted lungs boundary suffers from undesirable artifacts such as ridges and pits. This paper presents an algorithm to adaptively scan the inner lung boundary to correct the undesirable artifacts. Further, the algorithm is context-aware, it takes care of normal cavity due to the aortic knuckle and diaphragm borders. The algorithm is tested on 138 binary lung mask extracted from digital CXR images from Montgomery dataset. The quantitative, qualitative, statistical results reveal that the proposed algorithm outperforms the existing method. The average increase in segmentation accuracy is 2.561%.

•Developed a hierarchical method to mimic radiologist’s interpretation procedure.•Proposed seventeen geometrical shape features to encode thoracic abnormalities.•Combined shape features with texture features for improved abnormality detection.•Disease detection performance improved significantly using combined feature set.•The statistical evaluation proved the significance of the obtained results.

Chest X-rays (CXR) are widely used radio-imaging modality for preliminary diagnosis of thoracic abnormalities. Automatic detection and localization of diseases will greatly enhance real-world diagnosis processes. The imprecise and subtle appearance of disease responses on chest radiographs makes it difficult to localize sometimes even for an expert radiologists. The radiographic pattern in the normal regions is different from the disease affected regions. The affected areas in the X-ray images exhibit a cloudy pattern that shows the symptoms of various diseases like tuberculosis, pneumonia, pleural effusion, etc. The clustering algorithms have the capability to form clusters based on extracted features from the image. In this paper, we have investigated the effectiveness of Fuzzy C-Means (FCM) and K-Means (KM) clustering techniques to localize the suspected abnormal regions in CXR images. The analysis is performed on small image patches extracted from the segmented lung fields, and the results are compared with ground truth data provided by the radiologist. The proposed system is validated using a publicly available Montgomery dataset. The results demonstrate the promising performance of the proposed technique in delimiting the suspected abnormal regions.

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.