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Purpose: The current study explores the application of 3D U-Net architectures combined with Inception and ResNet modules for precise lung nodule detection through deep learning-based segmentation technique. This investigation is motivated by the objective of developing a Computer-Aided Diagnosis (CAD) system for effective diagnosis and prognostication of lung nodules in clinical settings. Methods: The proposed method trained four different 3D U-Net models on the retrospective dataset obtained from AIIMS Delhi. To augment the training dataset, affine transformations and intensity transforms were utilized. Preprocessing steps included CT scan voxel resampling, intensity normalization, and lung parenchyma segmentation. Model optimization utilized a hybrid loss function that combined Dice Loss and Focal Loss. The model performance of all four 3D U-Nets was evaluated patient-wise using dice coefficient and Jaccard coefficient, then averaged to obtain the average volumetric dice coefficient (DSCavg) and average Jaccard coefficient (IoUavg) on a test dataset comprising 53 CT scans. Additionally, an ensemble approach (Model-V) was utilized featuring 3D U-Net (Model-I), ResNet (Model-II), and Inception (Model-III) 3D U-Net architectures, combined with two distinct patch sizes for further investigation. Results: The ensemble of models obtained the highest DSCavgof 0.84 ± 0.05 and IoUavgof 0.74 ± 0.06 on the test dataset, compared against individual models. It mitigated false positives, overestimations, and underestimations observed in individual U-Net models. Moreover, the ensemble of models reduced average false positives per scan in the test dataset (1.57 nodules/scan) compared to individual models (2.69–3.39 nodules/scan). Conclusions: The suggested ensemble approach presents a strong and effective strategy for automatically detecting and delineating lung nodules, potentially aiding CAD systems in clinical settings. This approach could assist radiologists in laborious and meticulous lung nodule detection tasks in CT scans, improving lung cancer diagnosis and treatment planning.

Purpose: The proposed work aims to develop an algorithm to precisely segment the lung parenchyma in thoracic CT scans. To achieve this goal, the proposed technique utilized a combination of deep learning and traditional image processing algorithms. The initial step utilized a trained convolutional neural network (CNN) to generate preliminary lung masks, followed by the proposed post-processing algorithm for lung boundary correction. Methods: First, the proposed method trained an improved 2D U-Net CNN model with Inception-ResNet-v2as its backbone. The model was trained on 32 CT scans from two different sources: one from the VESSEL12 grand challenge and the other from AIIMS Delhi. Further, the model’s performance was evaluated on a test dataset of 16 CT scans with juxta-pleural nodules obtained from AIIMS Delhi and the LUNA16 challenge. The model’s performance was assessed using evaluation metrics such as average volumetric dice coefficient (DSCavg), average IoU score (IoUavg), and average F1 score (F1avg). Finally, the proposed post-processing algorithm was implemented to eliminate false positives from the model's prediction and to include juxta-pleural nodules in the final lung masks. Results: The trained model reported a DSCavgof 0.9791 ± 0.008, IoUavgof 0.9624 ± 0.007, and F1avgof 0.9792 ± 0.004 on the test dataset. Applying the post-processing algorithm to the predicted lung masks obtained a DSCavgof 0.9713 ± 0.007, IoUavgof 0.9486 ± 0.007, and F1avgof 0.9701 ± 0.008. The post-processing algorithm successfully included juxta-pleural nodules in the final lung mask. Conclusions: Using a CNN model, the proposed method for lung parenchyma segmentation produced precise segmentation results. Furthermore, the post-processing algorithm addressed false positives and negatives in the model’s predictions. Overall, the proposed approach demonstrated promising results for lung parenchyma segmentation. The method has the potential to be valuable in the advancement of computer-aided diagnosis (CAD) systems for automatic nodule detection.

ObjectivesTo assess the cost-effectiveness of addition of olanzapine to a prophylactic antiemetic regimen containing aprepitant, dexamethasone and ondansetron among children receiving highly emetogenic chemotherapy (HEC) in India, Bangladesh, Indonesia, the UK and the USA.MethodsHealth states were estimated using individual patient-level outcome data from a randomised trial. The incremental cost–utility ratio (ICUR), incremental cost-effectiveness ratio and net monetary benefit (NMB) were calculated from the patient perspective for India, Bangladesh, Indonesia, the UK and the USA. One-way sensitivity analysis was done by varying the cost of olanzapine, cost of hospitalisation and utility values by ±25%.ResultsThe olanzapine arm had an increment of 0.0018 quality-adjusted life-years (QALY) over the control arm. The mean total expenditure in the olanzapine arm was greater by US$0.51, US$0.43, US$6.73, US$11.05 and US$12.35 in India, Bangladesh, Indonesia, the UK and the USA, respectively. The ICUR($/QALY) was US$282.60 in India, US$241.42 in Bangladesh, US$3755.93 in Indonesia, US$6161.83 in the UK and US$6887.41 in the USA. The NMB was US$9.86, US$10.12, US$14.08, US$44.74 and US$98.79 for India, Bangladesh, Indonesia, the UK and the USA, respectively. The ICUR estimates of the base case and sensitivity analysis were below the willingness-to-pay threshold in all scenarios.ConclusionThe addition of olanzapine as a fourth agent for antiemetic prophylaxis is cost-effective despite an increase in overall expenditure. Olanzapine should be uniformly considered for children receiving HEC.

AbstractOne of the most challenging issues during the pandemic is managing uncertainties in demand, customer behavior, and market trends. Such instability and unpredictability resulted in numerous cases of excess stock when demand declined or a shortage of commodities when demand for certain goods increased significantly. The research presented in this paper contributes to modelling and forecasting rice sales demand in a Fair Price Shop (FPS) in Kerala, India by employing a time series technique. Our research shows how past demand data can be used to estimate future demand and how these forecasts impact the Public Distribution System (PDS). Our study employs Autoregressive Integrated Moving Average (ARIMA) and Double Exponential Smoothing (DES) techniques to develop future prediction models that significantly increase the efficiency and accuracy of demand and inventory forecasting. The forecast models generated from past data are verified and validated in the real case application using the Mean Absolute Percentage Error (MAPE) that helps to forecast the demand of inventory required in FPS. The proposed ARIMA and DES outperform the forecasts made by the empirical model, with ARIMA doing better in terms of future forecasts.

COVID 19 pandemic is causing a global health epidemic. The most powerful safety tool is wearing a face mask in public places and everywhere else. The COVID 19 outbreak forced governments around the world to implement lockdowns to deter virus transmission. According to survey reports, wearing a face mask at public places reduces the risk of transmission significantly. In this paper, an IoT-enabled smart door that uses a machine learning model for monitoring body temperature and face mask detection. The proposed model can be used for any shopping mall, hotel, apartment entrance, etc. As an outcome a cost-effective and reliable method of using AI and sensors to build a healthy environment. Evaluation of the proposed framework is done by the Face Mask Detection algorithm using the TensorFlow software library. Besides, the body temperature of the individual is monitored using a non-contact temperature sensor. This proposed system can detect the users from COVID 19 by enabling the Internet of Things (IoT) technology.

Effective organisation of a warehouse's incoming goods section is important for its productivity as ensuring efficient shelving systems. When the incoming goods section is not properly configured, this almost automatically causes major interruptions throughout the subsequent storage phase. For effective storage of goods in warehouse farm optimisation algorithm (FOA) is proposed. The efficacy of the proposed approach was demonstrated using BR datasets and it is compared with different optimisation algorithms. From this experiment, it is noted that the suggested FOA fulfils the objective of efficient arrangement of goods in the warehouse. The order in which the goods are placed into the warehouse is also noted to be ideal than other competitive optimisation algorithms.