Abstract
During Covid-19, many supply chains were disrupted. Supply chain resilience can be improved by developing business continuity capabilities using artificial intelligence (AI). This research examines how companies use artificial intelligence (AI) and looks at ways that AI can improve supply chain resilience by increasing visibility, reducing risk, and improving sourcing and distribution. Early detection of SARS-CoV-2 (2019-nCoV), which is caused by the lethal virus SARS-Cov-2 (Severe Acute Respiratory Syndrome Corona virus), has become critical especially as the epidemic spreads. X-rays and computed tomography scans are examples of medical imaging that can help in diagnosis. CT scans are preferable over RT-PCR tests because of their inaccuracy. In this era of fast technological growth, using artificial intelligence methodologies to construct models with a higher performance volume and better accuracy predictions is a huge step forward. Medical image analysis incorporating image processing and computer vision techniques were used to analyse the chest Radiographs and train the models. The accuracy and significant amount of data collection and prediction supply chain for efficient detection of COVID-19 utilising Artificial Intelligence techniques are described in this study. Models are built using data obtained by local CT scan centres. The data can be reviewed from time to time in coordination with CT scan centres. The application will provide accurate predictions so this has a significant impact on the tool's market worth. For the post-COVID-19 period, many firms are hastening the creation of management plans with supply chain transformation in mind. In this pandemic, but even so, the market will be even narrower, so without using a decentralised governance framework with an imbalanced structure among various markets, it really should be moved to a centralised management strategy that combines advantage of the existing strength of a blocked setup, with almost as much vicinity to the manufacturing countries and regions. In a stronger emphasis Supply Chain Management, value management, in value analysis, plays a significant role. Real-time raw data of chest CT scans from hospitals were considered and used it to train the model after pre-processing it. In a chest CT scan, multiple perspectives and organs are focused, but the work solely used the axial perspective of the lungs to prepare the dataset. Around 1900 photos of each COVID and Normal are included in the dataset. The data was pre-processed with a range filter for noise reduction, cropping, data augmentation, and other minor operations such as adjusting the image brightness and sharpness. Deep Learning algorithms are trained using this pre-processed data. VGG16, ResNet101, Inception v2, DesneNet169, and Mobile net are implementations of deep learning algorithms that is developed. The same dataset was used to train the above models, however because each model has a distinct architecture, the accuracy of the models varies slightly. The test dataset for all of the models includes 300 images in each class, and the findings demonstrate that DenseNet169 has the best accuracy among the models, while ResNet101 has the poorest. Furthermore, the medical image analysis of Covid-19 by several models aids in the selection of the most accurate model for COVID-19 predictions from CT scans. A windows application for the prediction of COVID has been developed where the user will upload the CT scan image and has an option to specify model and get the prediction. If tested positive, user can also view the infected area in the image. This application uses DenseNet169 as default model, in case user does not specify the model, as it performs the best. The user can view the COVID protocols and related queries from WHO website using a query button. The availability of CT scans and other commodities for this application varies as a result of the pandemic, which has an impact on the global supply chain and the market price of this tool.
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Srujana, K.S., Kashyap, S.N., Shrividhiya, G., Gururaj, C., Induja, K.S. (2022). Supply Chain Based Demand Analysis of Different Deep Learning Methodologies for Effective Covid-19 Detection. In: Perumal, K., Chowdhary, C.L., Chella, L. (eds) Innovative Supply Chain Management via Digitalization and Artificial Intelligence. Studies in Systems, Decision and Control, vol 424. Springer, Singapore. https://doi.org/10.1007/978-981-19-0240-6_9
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