Soft Computing Techniques-based Digital Video Forensics for Fraud Medical Anomaly Detection

Sunpreet Kaur Nanda; Deepika Ghai; P. V. Ingole; Sagar Pande

doi:10.24423/cames.447

Sunpreet Kaur Nanda Lovely Professional University, Phagwara, Punjab, India
Deepika Ghai Lovely Professional University, Phagwara, Punjab, India
P. V. Ingole Ram Meghe Institute of Technology & Research, Amravati, India
Sagar Pande School of Computer Science and Engineering, VIT-AP University, Amaravati, Andhra Pradesh, India

DOI: http://dx.doi.org/10.24423/cames.447

Abstract

The current pandemic situation has made it important for everyone to wear masks. Digital image forensics plays an important role in preventing medical fraud and in object detection. It is helpful in avoiding the high-risk situations related to the health and security of the individuals or the society, including getting the proper evidence for identifying the people who are not wearing masks. A smart system can be developed based on the proposed soft computing technique, which can be helpful to detect precisely and quickly whether a person wears a mask or not and whether he/she is carrying a gun. The proposed method gave 100% accurate results in videos used to test such situations. The system was able to precisely differentiate between those wearing a mask and those not wearing a mask. It also effectively detects guns, which can be used in many applications where security plays an important role, such as the military, banks, etc.

Keywords

smart healthcare system, medical imaging, healthcare frauds, MRI imaging, digital image forensics, object detection, YOLO architecture, customized CNN,

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