Intent to submit guide and timeline

The Intent to Submit Guide is located here.   To enhance success rates and better support application development, researchers are encouraged to review the full Intent to Submit Guide and Timeline prior to completing the Intent to Submit Form.

The Intent to Submit form can be accessed here – see heading ‘Researchers’.   The Intent to Submit forms and supporting documentation should be submitted to by COB 18 September 2020

The Intent to Submit forms will be forwarded to the relevant Associate Dean Research or Research Centres and further information on strategic advice and actions will be provided.

The Research Office will coordinate scheme specific information sessions with members of the ARC College of Experts/NHMRC assessors within the Charles Sturt research community to obtain further insight and advice for applying.  Further information and registration details in relation to these information sessions will be distributed to intending applicants following the close of the ITS call.   The workshops will be scheduled for October 2020.

Applicants seeking additional information regarding the Call for intent to Submit should contact the Research Office on 02 6933 2578 or email: .

COVID-19 Project publication

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Title of the paper: COVID-19 Detection Through Transfer Learning Using Multimodal Imaging Data

M. J. Horry, S. Chakraborty, M. Paul, A. Ulhaq, B. Pradhan, M. Saha, and N. Shukla (2020), “COVID-19 Detection Through Transfer Learning Using Multimodal Imaging Data,” IEEE Access, pp. 149808 – 149824, 2020. DOI: 10.1109/ACCESS.2020.3016780

Early COVID-19 detection helps in devising an appropriate treatment plan and disease containment decisions. In this study, we demonstrate how transfer learning from deep learning models can be used to perform COVID-19 detection using images from three most commonly used medical imaging modes X-Ray, Ultrasound, and CT scan. The aim is to provide over-stressed medical professionals a second pair of eyes through intelligent deep learning image classification models. The proposed model performs in considerable levels of COVID-19 detection against pneumonia or normal for all three lung image modes with the precision of up to 86% for X-Ray, 100% for Ultrasound and 84% for CT scans.

Access the full text of the paper from the following IEEE website: