Abstract
COVID-19 outbreak is ones in hundred years’ experience for each human being around the globe. Frequent lockdowns and unpredictable days shaken whole world. While researchers are struggling for genome sequencing and understanding the changes occurring in virus sequences, on the other hand, common people are struggling to control their fear about the future in all aspects. After the successful research for vaccination, the medical experts analyzed the post-COVID-19 impacts on various health fronts like heart failures, thrombosis, impact on brain, and many more complications. Out of which, identification of post-COVID-19 impact on brain took more time to understand the exact way the virus is affecting because psychological behavior is the first symptom and that takes keen observation to suggest the possibility of neurological infections. But, by that time, the illness reaches to more serious complications. Also, post-vaccination evidence shows that the blood clot formations becoming a new challenge for brain tumor patients. The blood clots in nervous systems are so tiny that by MRI/CT, it is not possible to differentiate between cerebral fluids. Hence, it becomes necessary to operate patient immediately with a clear vision facility for blood clots. Hence, this paper suggests the new deep learning algorithm which can be a great solution for image analysis with high level of accuracy. The proposed deep CNN module further can be used as a software package for needle camera for robotic assisted surgery which in turn saves time for image analysis and direct location of tumor can be identified during live camera surgery.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
L. Sher, The impact of the COVID-19 pandemic on suicide rates. QJM: Int. J. Med. 113(10), 707–712 (2020)
S. Filetti, The COVID-19 pandemic requires a unified global response. Springer Endocrine 68(1), 020–022 (2020)
C. Shorten, T.M. Khoshgoftaar, B. Furht, Deep learning applications for COVID-19. Springer, J. Big Data 8(1), 1–54 (2021)
M. Umer, et al., COVINet: a convolutional neural network approach for predicting COVID-19 from chest X-ray images. J. Amb. Intell. Human. Comput. 1–13 (2021)
M. Kocher, et al., Applications of radiomics and machine learning for radiotherapy of malignant brain tumors. Springer, Strahlentherapie und Onkologie 196, 856–867 (2020)
E. Guedj, et al., 18 F-FDG brain PET hypometabolism in patients with long COVID. Eur. J. Nucl. Med. Mol. Imag. 1–11 (2021)
F. Benedetti, et al., Brain correlates of depression, post-traumatic distress, and inflammatory biomarkers in COVID-19 survivors: a multimodal magnetic resonance imaging study. Brain Behav. Immun.-Health 100387 (2021)
J. Zhang, et al., Implementation of a novel Bluetooth technology for remote deep brain stimulation programming: the pre–and post–COVID‐19 Beijing experience. Movement Disorders (2020)
B. Oronsky, et al., A review of persistent post-COVID syndrome (PPCS). Clin. Rev. Allergy Immunol. 1–9 (2021)
G.P. Castelli, et al., Cerebral venous sinus thrombosis associated with thrombocytopenia post-vaccination for COVID-19. Critical Care 25(1), 1–2 (2021)
H. Mzoughi, et al., Deep multi-scale 3D convolutional neural network (CNN) for MRI Gliomas brain tumor classification. J. Dig. Imag. 33, 903–915 (2020)
N. Noreen, et al., A deep learning model based on concatenation approach for the diagnosis of brain tumor. IEEE Access 8, 55135–55144 (2020)
D. Fischer, et al., Intact brain network function in an unresponsive patient with COVID‐19. Ann. Neurol. 88(4), 851–854 (2020)
A. Bhattacharya, et al. Predictive analysis of the recovery rate from coronavirus (COVID-19). in Cyber Intelligence and Information Retrieval eds by J.M.R.S. Tavares, P. Dutta, S. Dutta, D. Samanta. Lecture Notes in Networks and Systems, vol. 291. Springer, Singapore. https://doi.org/10.1007/978-981-16-4284-5_27
A. Radmanesh, et al. Brain imaging use and findings in COVID-19: a single academic center experience in the epicenter of disease in the United States. Am. J. Neuroradiol. 41(7), 1179–1183 (2020)
S. Maqsood, D. Robertas, M.S. Faisal, in An Efficient Approach for the Detection of Brain Tumor Using Fuzzy Logic and U-NET CNN Classification. International Conference on Computational Science and Its Applications. Springer, Cham (2021)
W. Wang, et al., in Transbts: Multimodal Brain Tumor Segmentation Using Transformer. International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham (2021)
V.K. Anand, et al., in Brain Tumor Segmentation and Survival Prediction Using Automatic Hard Mining in 3D CNN Architecture. International MICCAI Brainlesion Workshop. Springer, Cham (2020)
P. Keshavarz, et al., lymphadenopathy following covid-19 vaccination: imaging findings review. Academ. Radiol. (2021)
D.G. Corrêa, et al., Neurological symptoms and neuroimaging alterations related with COVID-19 vaccine: cause or coincidence? Clin. Imag. 80. 348–352 (2021)
N. Sohail, et al., Smart approach for glioma segmentation in magnetic resonance imaging using modified convolutional network architecture (U-NET). Cybern. Syst. 1–16 (2020)
J. Xue, et al., Hypergraph membrane system based F2 fully convolutional neural network for brain tumor segmentation. Appl. Soft Comput. 94, 106454 (2020)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Khadke, K.S. (2023). Development of DeepCovNet Using Deep Convolution Neural Network for Analysis of Neuro-Infections Causing Blood Clots in Brain Tumor Patients: A COVID-19 Post-vaccination Scenario. In: Dutta, P., Bhattacharya, A., Dutta, S., Lai, WC. (eds) Emerging Technologies in Data Mining and Information Security. Advances in Intelligent Systems and Computing, vol 1348. Springer, Singapore. https://doi.org/10.1007/978-981-19-4676-9_30
Download citation
DOI: https://doi.org/10.1007/978-981-19-4676-9_30
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-4675-2
Online ISBN: 978-981-19-4676-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)