S Bavankumar's scientific contributions

The prime objective of Hybrid Multimodal Medical Image Fusion (HMMIF) method is preservation of important features of images and details about various images from source for creating a visually robust enough single fused image provides a very promising diagnostic tool with numerous clinical and healthcare applications. The Non subsampled shearlet Transform (NSST) with Pulse Coupled Neural Network (PCNN) based hybrid algorithms are proposed for MMIF in this paper. In the proposed method, initially input images are decomposed to less and high frequencies with the application of NSST. The components with lesser frequency are applied with averaging fusion rule. The maximum fusion rule with PCNN is applied on high frequency components. The coefficients produced by every frequency bands are inverse transformed to provide fused images. The proposed algorithms provide the best fused images without distortion and false artefacts. Comparison of proposed technique is done with the pre-existing conventional techniques. The images obtained by fusing both sources' content with the help of the above algorithm gives the best with respect to visualization and diagnosis of the condition.

Current scenario of COVID-19 (Corona Virus Disease) pandemic makes almost everyone to wear a mask in order to effectively prevent the spread of the virus. This almost makes conventional facial recognition technology ineffective in many cases, such as community access control, face access control, facial attendance, facial security checks at train stations, etc. Therefore, it is very urgent to improve the recognition performance of the existing face recognition technology on the masked faces. For that detecting the people with face mask is very essential. In this work, a reliable method based on discard masked region is proposed in order to address the problem of masked face recognition process. The first step is to discard the masked face region and extract the forehead and eyes region. Next, a pre-trained deep Convolutional neural networks (CNN) is applied to extract the best features from the obtained regions. Finally, it show experimental result is achieved an accuracy of more than 98% validation dataset.