Sharada Guptha M N

EDUCATION

Dr. Sharada Guptha M N , Completed Bachelor of Engineering in Medical Electronics and Master of Technology in VLSI and Embedded Systems. Awarded with Ph.D degree in November 2023 for the work in the area of VLSI signal processing.

RESEARCH, TEACHING, or OTHER INTERESTS

Biomedical Engineering, Signal Processing, Computer Vision and Pattern Recognition, Health Informatics

Scopus Publications

Breast cancer detection through attention based feature integration model
Sharada Guptha, Murundi N Eshwarappa
Iaes International Journal of Artificial Intelligence, 2024
<span lang="EN-US">Breast cancer is detected by screening mammography wherein X-rays are used to produce images of the breast. Mammograms for screening can detect breast cancer early. This research focuses on the challenges of using multi-view mammography to diagnose breast cancer. By examining numerous perspectives of an image, an attention-based feature-integration mechanism (AFIM) model that concentrates on local abnormal areas associated with cancer and displays the essential features considered for evaluation, analyzing cross-view data. This is segmented into two views the bi-lateral attention module (BAM) module integrates the left and right activation maps for a similar projection is used to create a spatial attention map that highlights the impact of asymmetries. Here the module's focus is on data gathering through medio-lateral oblique (MLO) and bilateral craniocaudal (CC) for each breast to develop an attention module. The proposed AFIM model generates using spatial attention maps obtained from the identical image through other breasts to identify bilaterally uneven areas and</span><span lang="EN-US">class activation map (CAM) generated from two similar breast images to emphasize the feature channels connected to a single lesion in a breast. AFIM model may easily be included in ResNet-style architectures to develop multi-view classification models.</span>
Optimized Deep Learning-Based Fully Resolution Convolution Neural Network for Breast Tumour Segmentation on Field Programmable Gate Array
Sharada Guptha M N, M N Eshwarappa
Computer Methods in Biomechanics and Biomedical Engineering Imaging and Visualization, 2023
Deep learning (DL) approaches have been highly interesting in segmentation and classification in recent years. During breast cancer detection, a convolutional neural network (CNN) requires several up-sampling operations to recover the original image from the feature map. This research introduces an optimised fully resolution-CNN (FR-CNN) based breast tumour segmentation in the field programmable gate array (FPGA) platform. The FPGA implementation of FR-CNN considers both fixed and floating point operations to find the best trade-off between accuracy and hardware complexity. The FR-CNN network model usually requires several adder and multiplier units that consume more power and area. Hence, an optimised Vedic multiplier based on a carry select adder with Simplified Sum-Carry Generation Logic (VCSA-SSCGL) is introduced. In addition, the particle swarm optimisation algorithm (PSO) is introduced for tuning the parameters in the network model. In the experimental scenario, the proposed model achieved an accuracy of 96.89%, precision of 95.84%, F-score of 96.08%, specificity of 96.73%, mean absolute error (MAE) of 0.87, dice similarity coefficient (DSC) of 0.93, and Jaccard coefficient (JC) of 0.9. Also, the FPGA design of a proposed model consumed only 0.6124W power and a LUT of 12,167. The experimental results prove the efficiency of a proposed method.
RL-BLED: A Reversible Logic Design of Bit Level Encryption/Decryption Algorithm for Secure Mammogram Data Transmission
M. N. Sharada Guptha, M. N. Eshwarappa
Wireless Personal Communications, 2022

RECENT SCHOLAR PUBLICATIONS

Optimized Deep Learning-Based Fully Resolution Convolution Neural Network for Breast Tumour Segmentation on Field Programmable Gate Array
SG MN, MN Eshwarappa
Computer Methods in Biomechanics and Biomedical Engineering: Imaging … , 2023
2023.0
Citations: 7
RL-BLED: A Reversible Logic Design of Bit Level Encryption/Decryption Algorithm for Secure Mammogram Data Transmission
MNS Guptha, MN Eshwarappa
Wireless Personal Communications 125 (1), 939-963 , 2022
2022.0
Citations: 9
Potential of a Reversible logic Machine - A review
sharada guptha mn
9th international conference of recent engineering and technology 2019 , 2019
2019.0
MN: FPGA implementation of high performance reversible logic method of array multiplier
S Guptha, E MN
JAC J. Compos. Theory XIII , 0
A VLSI Approach for Cache Compression in Microprocessor
MN Sharada Guptha, HS Pradeep, MZ Kurian
International Journal of Instrumentation, Control and Automation (IJICA … , 0
Citations: 2

MOST CITED SCHOLAR PUBLICATIONS

RL-BLED: A Reversible Logic Design of Bit Level Encryption/Decryption Algorithm for Secure Mammogram Data Transmission
MNS Guptha, MN Eshwarappa
Wireless Personal Communications 125 (1), 939-963 , 2022
2022.0
Citations: 9
Optimized Deep Learning-Based Fully Resolution Convolution Neural Network for Breast Tumour Segmentation on Field Programmable Gate Array
SG MN, MN Eshwarappa
Computer Methods in Biomechanics and Biomedical Engineering: Imaging … , 2023
2023.0
Citations: 7
A VLSI Approach for Cache Compression in Microprocessor
MN Sharada Guptha, HS Pradeep, MZ Kurian
International Journal of Instrumentation, Control and Automation (IJICA … , 0
Citations: 2
Potential of a Reversible logic Machine - A review
sharada guptha mn
9th international conference of recent engineering and technology 2019 , 2019
2019.0
MN: FPGA implementation of high performance reversible logic method of array multiplier
S Guptha, E MN
JAC J. Compos. Theory XIII , 0