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Your search keyword '"Premnath S."' showing total 2 results
Start Over Author "Premnath S." Topic feature extraction
2 results on '"Premnath S."'

1. MSRFNet for skin lesion segmentation and deep learning with hybrid optimization for skin cancer detection.

Author

Baskaran, Diwan, Nagamani, Yanda, Merugula, Suneetha, and Premnath, S P

Subjects
DEEP learning, SKIN cancer, EARLY detection of cancer, BLENDED learning, FEATURE extraction, OPTIMIZATION algorithms
Abstract

Skin cancer is the irregular growth of skin cells, which is most often termed as cancer, developed by exposure of ultraviolet rays from sun. In this research paper, deep learning enabled hybrid optimization is followed for skin cancer detection and lesion segmentation. Two optimization algorithms are followed for skin lesion segmentation and cancer detection. Here, pre-processing is done by anisotropic diffusion followed by skin lesion segmentation. Here, Multi-Scale Residual Fusion Network (MSRFNet) is utilized for skin lesion segmentation, which is trained by proposed Average Subtraction Student Psychology Based Optimization (ASSPBO). After skin lesion segmentation, necessary features are extracted, followed by skin cancer detection. Skin cancer is detected by Deep Residual Network (DRN) trained by proposed Fractional ASSPBO (FrASSPBO). Moreover, performance of proposed FrASSPBO-DRN is analysed by three performance metrics like testing accuracy, True Positive Rate (TPR), and False Positive Rate (FPR) with values of 93.4%, 94%, and 8.2%. [ABSTRACT FROM AUTHOR]

Published
2023
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2. A novel approach for image restoration using convolution network-based image denoising technique.

Author

Premnath, S. P., Renjit, J. Arokia, Shanmugapriya, P., Joel, T., Muthukumar, K., and Nayanatara, C.

Subjects
*IMAGE reconstruction, *IMAGE denoising, *ATMOSPHERIC turbulence, *FEATURE extraction
Abstract

When imaging a target in space, atmospheric turbulence might change the direction of the path of light. This might be caused by the random motion of the turbulent medium leading to drastic distortion of the image. In order to correct the temporally varying blur, reduction spatially, and geometric distortion, a blind deblurring atmospheric turbulence based on the convolutional network is proposed. This includes image reconstruction subnetwork, asymmetric U-net, and feature extraction noise suppression block (FENSB). In the U-net, instead of the traditional convolution layer that is normally used, a deblurring noise suppression block (DNSB) is used. The basic idea behind this methodology is to reduce the noise before deblurring occurs. Hence the aspect of noise suppression before deblurring is carried out by the DNSB block along with FENSB. At the time of convolutional encoding, DNSB and FENSB are used to identify and extract the rich features of the maps and further suppress noise. In order to fuse high-level and low-level features, the subnetwork is skip connected with FENSB thereby improving the integrity of the system during the process of reconstruction. This mechanism of increasing the training difficulty of the network in a step-by-step manner is used to slowly converge into a complex model facilitating better quality of images despite the turbulence. The results of simulation data, as well as that of real data, indicate that it is possible to suppress the noise and restore the image with high efficiency. [ABSTRACT FROM AUTHOR]

Published
2022
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