Sitaramanjaneya Reddy Guntur

Scopus Publications

TL-LFF Net: transfer learning based lighter, faster, and frozen network for the detection of multi-scale mixed intracranial hemorrhages through genetic optimization algorithm
Lakshmi Prasanna Kothala, Sitaramanjaneya Reddy Guntur
International Journal of Machine Learning and Cybernetics, 2025
GEL-TTA Net: a Global ensemble learning network for the localization of small-scale and mixed intracranial hemorrhages through test time augmentations
Lakshmi Prasanna Kothala, Sitaramanjaneya Reddy Guntur
Multimedia Tools and Applications, 2025
Classification of hand movements based on EMD-CCT feature extraction method through EMG using machine learning
M. Karuna, Sitaramanjaneya Reddy Guntur
Multimedia Tools and Applications, 2025
Optimisation of processing parameters for wear properties of polylactic acid biopolymer parts in FDM process
Nagarjuna Maguluri, Gamini Suresh, Sitaramanjaneya Reddy Guntur
Advances in Materials and Processing Technologies, 2025
Currently, additive manufacturing (AM) technology has been expanding in advanced engineering and biomedical applications. The fused deposition modelling (FDMTM) method, also known as fused filament fabrication (FFF), is one of the most extensively used AM methods owing to its simple operation, reliability and potential to manufacture intricate components. The mechanical and wear properties of these components depend mainly on the proper selection of processing parameters. This study aims to determine the optimum parameters for low wear rate, less frictional force and good surface roughness. To achieve this aim, a hybrid approach based on user preference ranking is adopted to evaluate the effect of the four processing parameters including nozzle temperature, fill density, layer thickness and printing speed, on the wear behaviour, frictional force and surface roughness. The study procedure includes Taguchi L27 experiment design, finding the weight ratios, calculating utility index (UI) values and optimising UI values using Taguchi methodology for maximum utilisation. The results found that at 220°C of nozzle temperature, 100% fill density, 0.24 mm layer thickness and a printing speed of 60 mm/s are optimal parameters for the maximum utility index value. From the results, the percentage of contribution of fill density, nozzle temperature, layer thickness and printing speed were observed as 69.071%, 15.974%, 5.431% and 2.306%, respectively.
A comparative analysis of preprocessing techniques on ultrasound images of CCA
Prathiba Jonnala, Sitaramanjaneya Reddy Guntur
International Journal of System Assurance Engineering and Management, 2024
Efficient Multi-Modal MR-PET medical Image Integration using Wavelet Level Depended Fusion Rules
Shaik Shehanaz, Sitaramanjaneya Reddy Guntur
2024 15th International Conference on Computing Communication and Networking Technologies Icccnt 2024, 2024
Multi-modal medical image fusion (MMF) is a technique that combines information from different modalities for better diagnosis and treatment planning. MRI displays anatomical information in grayscale with high spatial resolution, whereas PET displays the metabolism of an organ or specific tissue in color with low spatial resolution. This study aims to develop novel Multi-modal medical image fusion including optimum max fusion rule with improved Laplacian of Gaussian in a multi-level approach. The objective of this work is to investigate the optimum max fusion rule for enhancing the structural and edge quality using an improved Laplacian of Gaussian algorithm. The MRI-PET fusion image framework developed in this study uses multi-level wavelet decomposition with feature-dependent multi-fusion rules. DWT high-low frequency coefficients are used to generate detailed and approximate components. The weighted average and max fusion rule were used to obtain detailed and approximate components. The weights are calculated using improved Laplacian and Gaussian filter techniques based on normalized energy. Finally, detailed and approximate components were integrated for the formation of fused images. Subjective and objective evaluations were performed on three datasets of MRI-PET images. The present work introduces a multi-fusion rule-based framework for MRI and PET image fusion using DWT and an improved Laplacian of Gaussian filter. The experimental results illustrate that the proposed approach outperforms conventional methods in terms of enhancing structural (SSIM) and Edge-based similarity (Q(ABF)) features. The results demonstrated that the present MMF approach has greater structural and edge quality than other existing techniques. It can also be used in any application that requires image fusion at the preprocessing stage, such as MRI-PET pattern recognition.
Removal of Interference from Electromyogram based on Empirical Mode Decomposition and Correlation Coefficient Thresholding
M. Karuna, Sitaramanjaneya Reddy Guntur
Current Signal Transduction Therapy, 2024
Introduction:: Electromyography (EMG) signals are contaminated by various noise components. These noises directly degrade the EMG processing performance, thereby affecting the classification accuracy of the EMG signals for implementing various hand movements of the prosthetic arm from the amputee’s residual muscle. Methods:: This study mainly aims to denoise the EMG signals using the empirical mode decomposition (EMD) and correlation coefficient thresholding (CCT) methods. The noisy EMG signal is obtained from NinaPro Database 2. Then, EMD is used to decompose it into intrinsic mode functions. Each hand movement noise is identified within specific modes and removed separately using correlation coefficient–dependent thresholding and wavelet denoising. The performance metrics signal-to-noise ratio (SNR) and root mean square error (RMSE) were used to evaluate the noise removal performance from the EMG signals of five intact subjects. The proposed method outperforms the wavelet denoising method in terms of noise interference removal. In this method, the SNR is obtained in the 17-22 dB range with a very low RMSE. Results:: The experimental results illustrate that the proposed method removes noise from six repetitions of six movements performed by five subjects. This study explores the special characteristics of EMD and demonstrates the possibility of using the EMD-based CCT filter for denoising EMG signals. Conclusion:: The proposed filter is more efficient than wavelet denoising in removing noise interference. It can also be used in any application that requires EMG signal filtering at the preprocessing stage, such as EMG pattern recognition.
An efficient stacked bidirectional GRU-LSTM network for intracranial hemorrhage detection
Lakshmi Prasanna Kothala, Sitaramanjaneya Reddy Guntur
International Journal of Imaging Systems and Technology, 2024
Intracranial hemorrhage (ICH) is a dangerous condition that needs prompt diagnosis and treatment. Computed tomography (CT) images are employed in examination of individuals with ICH, which produces better results and cost‐effective than MRI. The existing convolutional neural network (CNN) models are unable to consider inter‐pixel dependency, which leads to false predictions while considering the input CT Images. In this study, we implemented an efficient model of a stack of bidirectional gated recurrent unit (Bi‐GRU) with a bidirectional long short‐term memory (Bi‐LSTM) based CNN to improve detection accuracy in the case of 2D slices. The proposed model holds slice‐wise information by accessing the properties of both Bi‐LSTM and Bi‐GRU modules in a single unit. As a result, the model attained a testing and training accuracy of 96.2% and 93.4%, respectively, with a test loss score of 0.126. In addition, the proposed model could outperform the state‐of‐the‐art CNN in identifying brain hemorrhages.
Classification of Artifacts in Multimodal Fused Images using Transfer Learning with Convolutional Neural Networks
Shehanaz Shaik, Sitaramanjaneya Reddy Guntur
Current Medical Imaging, 2024
Introduction: Multimodal medical image fusion techniques play an important role in clinical diagnosis and treatment planning. The process of combining multimodal images involves several challenges depending on the type of modality, transformation techniques, and mapping of structural and metabolic information. Methods: Artifacts can form during data acquisition, such as minor movement of patients, or data pre-processing, registration, and normalization. Unlike single-modality images, the detection of an artifact is a more challenging task in complementary fused multimodal images. Many medical image fusion techniques have been developed by various researchers, but not many have tested the robustness of their fusion approaches. The main objective of this study is to identify and classify the noise and artifacts present in the fused MRI-SPECT brain images using transfer learning by fine-tuned CNN networks. Deep neural network-based techniques are capable of detecting minor amounts of noise in images. In this study, three pre-trained convolutional neural network-based models (ResNet50, DenseNet 169, and InceptionV3) were used to detect artifacts and various noises including Gaussian, Speckle, Random, and mixed noises present in fused MRI -SPECT brain image datasets using transfer learning. Results: The five-fold stratified cross-validation (SCV) is used to evaluate the performance of networks. The obtained performance results for the pretrained DenseNet169 model for various folds were greater as compared with the rest of the models; the former had an average accuracy of five-fold of 93.8±5.8%, 98%±3.9%, 97.8±1.64%, and 93.8±5.8%, whereas InceptionNetV3 had a value of 90.6±9.8%, 98.8±1.6%, 91.4±9.74%, and 90.6±9.8%, and ResNet50 had a value of 75.8±21%.84.8±7.6%, 73.8±22%, and 75.8±21% for Gaussian, speckle, random and mixed noise, respectively. Conclusion: Based on the performance results obtained, the pre-trained DenseNet 169 model provides the highest accuracy among the other four used models.
Blockchain technology integrity in health informatics using hyper ledger platform
Innovations in Healthcare Informatics from Interoperability to Data Analysis, 2024
Semantic web of things for healthcare interoperability using IoMT technologies
Semantic Technologies for Intelligent Industry 4 0 Applications, 2023
Localization of mixed intracranial hemorrhages by using a ghost convolution-based YOLO network
Lakshmi Prasanna Kothala, Prathiba Jonnala, Sitaramanjaneya Reddy Guntur
Biomedical Signal Processing and Control, 2023
Image de-noising of Ultrasound Carotid artery images using various filters
Prathiba Jonnala, G. Sitaramanjaneya Reddy
2023 4th International Conference for Emerging Technology Incet 2023, 2023
Improved U-Net Framework for Accurate Lumen Segmentation in Cross-Sectional Ultrasound Images of the Carotid Artery
Prathiba Jonnala, Sitaramanjaneya Reddy Guntur
2023 1st International Conference on Advances in Electrical Electronics and Computational Intelligence Icaeeci 2023, 2023
An Improved Mosaic Method for the Localization of Intracranial Hemorrhages Through Bounding Box
Lakshmi Prasanna Kothala, Sitaramanjaneya Reddy Guntur
5th IEEE International Conference on Cybernetics Cognition and Machine Learning Applications Icccmla 2023, 2023
Improving the Hand Movement Classification Accuracy through EMG based on Histogram Feature Enhancement from Time Domain Representation
M. Karuna, Sitaramanjaneya Reddy Guntur
2023 2nd International Conference on Electrical Electronics Information and Communication Technologies Iceeict 2023, 2023
A Cost-Effective Reusable Tissue Mimicking Phantom for High Intensity Focused Ultrasonic Liver Surgery
Sitaramanjaneya Reddy Guntur, Seong-Chan Kim, Min-Joo Choi
Bioengineering, 2022
An efficient False Negative Reduction System for the Identification of Intracranial Hemorrhage
Lakshmi Prasanna Kothala, Sitaramanjaneya Reddy Guntur
2022 International Conference on Futuristic Technologies Incoft 2022, 2022
Segmentation of Intracranial Hemorrhage through an EfficientNetB7-based UNET model
Lakshmi Prasanna Kothala, Sitaramanjaneya Reddy Guntur
2022 International Conference on Smart Generation Computing Communication and Networking Smart Gencon 2022, 2022
Multi-Class Classification of Intracranial Hemorrhages in a 3-Channel CT image by using a Transfer Learning based DenseNet121 model
Lakshmi Prasanna Kothala, Sitaramanjaneya Reddy Guntur
2022 International Conference on Smart Generation Computing Communication and Networking Smart Gencon 2022, 2022
Classification of Hand Movements via EMG using Machine Learning Methods for Prosthesis
M. Karuna, Sitaramanjaneya Reddy Guntur
2022 2nd International Conference on Artificial Intelligence and Signal Processing Aisp 2022, 2022
Wavelet Based Classification of Finger Movements with Machine Learning Classifier
M. Karuna, Ippili Krishna Rao, M. Vijay Kumar, Sitaramanjaneya Reddy Guntur
2022 2nd International Conference on Advances in Electrical Computing Communication and Sustainable Technologies Icaect 2022, 2022
Single Image Haze Removal Based on transmission map estimation using Encoder-Decoder based deep learning architecture
Sivaji Satrasupalli, Ebenezer Daniel, Sitaramanjaneya Reddy Guntur
Optik, 2021
Optimum weighted multimodal medical image fusion using particle swarm optimization
Shaik Shehanaz, Ebenezer Daniel, Sitaramanjaneya Reddy Guntur, Sivaji Satrasupalli
Optik, 2021
DroneMap: An IoT Network Security in Internet of Drones
Rajani Reddy Gorrepati, Sitaramanjaneya Reddy Guntur
Studies in Systems Decision and Control, 2021
End to end system for hazy image classification and reconstruction based on mean channel prior using deep learning network
Sivaji Satrasupalli, Ebenezer Daniel, Sitaramanjaneya Reddy Guntur, Shaik Shehanaz
Iet Image Processing, 2020
Preparation and characterization of a novel sprayable hydrogel for skin preparation to record ECG and other biopotentials
Sunil Tej Boppudi, Suliman Belal, Sitaramanjaneya Reddy Guntur
Biomedical Engineering Letters, 2020
Temperature Dependence of Tissue Thermal Parameters Should Be Considered in the Thermal Lesion Prediction in High-Intensity Focused Ultrasound Surgery
Sitaramanjaneya Reddy Guntur, Min Joo Choi
Ultrasound in Medicine and Biology, 2020
EMG Signal Analysis Using Intrinsic Mode Functions to Discriminate Upper Limb Movements
M. Karuna, Sitaramanjaneya Reddy Guntur
2020 International Conference on Artificial Intelligence and Signal Processing Aisp 2020, 2020
Upper limb movements identification through EMG signal using artificial neural network
International Journal of Engineering and Advanced Technology, 2019
Robotics in Healthcare: An Internet of Medical Robotic Things (IoMRT) Perspective
Sitaramanjaneya Reddy Guntur, Rajani Reddy Gorrepati, Vijaya R. Dirisala
Machine Learning in Bio Signal Analysis and Diagnostic Imaging, 2019
In Vitro Studies of the Antimicrobial and Free-Radical Scavenging Potentials of Silver Nanoparticles Biosynthesized From the Extract of Desmostachya bipinnata
Sitaramanjaneya Reddy Guntur, NS Sampath Kumar, Manasa M Hegde, Vijaya R Dirisala
Analytical Chemistry Insights, 2018
Influence of temperature-dependent thermal parameters on temperature elevation of tissue exposed to high-intensity focused ultrasound: Numerical simulation
Sitaramanjaneya Reddy Guntur, Min Joo Choi
Ultrasound in Medicine and Biology, 2015
An Improved Tissue-Mimicking Polyacrylamide Hydrogel Phantom for Visualizing Thermal Lesions with High-Intensity Focused Ultrasound
Sitaramanjaneya Reddy Guntur, Min Joo Choi
Ultrasound in Medicine and Biology, 2014
A Tissue Mimicking Polyacrylamide Hydrogel Phantom for Visualizing Thermal Lesions Generated by High Intensity Focused Ultrasound
Min Joo Choi, Sitaramanjaneya Reddy Guntur, Kang IL Lee, Dong Guk Paeng, Andrew Coleman
Ultrasound in Medicine and Biology, 2013
Temperature-Dependent Thermal Properties of exVivo Liver Undergoing Thermal Ablation
Sitaramanjaneya Reddy Guntur, Kang Il Lee, Dong-Guk Paeng, Andrew John Coleman, Min Joo Choi
Ultrasound in Medicine and Biology, 2013
Changes in ultrasonic properties of liver tissue in vitro during heating-cooling cycle concomitant with thermal coagulation
Min Joo Choi, Sitaramanjaneya Reddy Guntur, Joo Myoung Lee, Dong Guk Paeng, Kang I.L. Lee, Andrew Coleman
Ultrasound in Medicine and Biology, 2011
Reusable ultrasonic tissue mimicking hydrogels containing nonionic surface-active agents for visualizing thermal lesions
Seong Keun Park, S. R. Anjaneya Reddy Guntur, Kang Il Lee, Dong-Guk Paeng, Min Joo Choi
IEEE Transactions on Biomedical Engineering, 2010

RECENT SCHOLAR PUBLICATIONS

TL-LFF Net: transfer learning based lighter, faster, and frozen network for the detection of multi-scale mixed intracranial hemorrhages through genetic optimization algorithm
LP Kothala, SR Guntur
International Journal of Machine Learning and Cybernetics 16 (10), 7059-7085 , 2025
2025
Citations: 10
GEL-TTA Net: a Global ensemble learning network for the localization of small-scale and mixed intracranial hemorrhages through test time augmentations
LP Kothala, SR Guntur
Multimedia Tools and Applications 84 (14), 13005-13036 , 2025
2025
Citations: 7
Classification of hand movements based on EMD-CCT feature extraction method through EMG using machine learning
M Karuna, SR Guntur
Multimedia Tools and Applications 84 (10), 7987-8013 , 2025
2025
Citations: 6
Optimisation of processing parameters for wear properties of polylactic acid biopolymer parts in FDM process
N Maguluri, G Suresh, S Reddy Guntur
Advances in Materials and Processing Technologies 11 (1), 92-108 , 2025
2025
Citations: 3
Efficient Multi-Modal MR-PET medical Image Integration using Wavelet Level Depended Fusion Rules
S Shehanaz, SR Guntur
2024 15th International Conference on Computing Communication and Networking … , 2024
2024
Citations: 1
A comparative analysis of preprocessing techniques on ultrasound images of CCA
P Jonnala, SR Guntur
International Journal of System Assurance Engineering and Management 15 (6 … , 2024
2024
Removal of Interference from Electromyogram based on Empirical Mode Decomposition and Correlation Coefficient Thresholding
M Karuna, SR Guntur
Current Signal Transduction Therapy 19 (1), 48-56 , 2024
2024
Citations: 1
Classification of artifacts in multi model fused image using transfer learning convolution neural network
Shehanaz Shaik,Sitaramanjaneya Reddy
Current Medical Imaging , 2024
2024
Citations: 1
Improved U-Net Framework for Accurate Lumen Segmentation in Cross-Sectional Ultrasound Images of the Carotid Artery
P Jonnala, SR Guntur
2023 First International Conference on Advances in Electrical, Electronics … , 2023
2023
Citations: 1
An improved mosaic method for the localization of intracranial hemorrhages through bounding box
LP Kothala, SR Guntur
2023 IEEE 5th International Conference on Cybernetics, Cognition and Machine … , 2023
2023
Citations: 6
An efficient stacked bidirectional GRU‐LSTM network for intracranial hemorrhage detection
LP Kothala, SR Guntur
International Journal of Imaging Systems and Technology , 2023
2023
Citations: 7
Image de-noising of Ultrasound Carotid artery images using various filters
P Jonnala, GS Reddy
2023 4th International conference for emerging technology (INCET), 1-4 , 2023
2023
Citations: 5
Improving the Hand Movement Classification Accuracy through EMG based on Histogram Feature Enhancement from Time Domain Representation
M Karuna, SR Guntur
2023 Second International Conference on Electrical, Electronics, Information … , 2023
2023
Citations: 1
Localization of mixed intracranial hemorrhages by using a ghost convolution-based YOLO network
LP Kothala, P Jonnala, SR Guntur
Biomedical Signal Processing and Control 80, 104378 , 2023
2023
Citations: 33
Semantic Web of Things for Healthcare Interoperability using IoMT Technologies
RR Gorrepati, P Jonnala, SR Guntur, DH Kim
River Publishers , 2023
2023
Citations: 5
Segmentation of Intracranial Hemorrhage through an EfficientNetB7-based UNET model
LP Kothala, SR Guntur
2022 International Conference on Smart Generation Computing, Communication … , 2022
2022
Citations: 3
Multi-Class Classification of Intracranial Hemorrhages in a 3-Channel CT image by using a Transfer Learning based DenseNet121 model
LP Kothala, SR Guntur
2022 International Conference on Smart Generation Computing, Communication … , 2022
2022
Citations: 7
A cost-effective reusable tissue mimicking phantom for high intensity focused ultrasonic liver surgery
SR Guntur, SC Kim, MJ Choi
Bioengineering 9 (12), 786 , 2022
2022
Citations: 7
An efficient False Negative Reduction System for the Identification of Intracranial Hemorrhage
LP Kothala, SR Guntur
2022 International Conference on Futuristic Technologies (INCOFT), 1-6 , 2022
2022
Citations: 1
Effect of printing parameters on the hardness of 3D printed poly-lactic acid parts using DOE approach
N Maguluri, G Suresh, SR Guntur
IOP Conference Series: Materials Science and Engineering 1248 (1), 012004 , 2022
2022
Citations: 27

MOST CITED SCHOLAR PUBLICATIONS

A tissue mimicking polyacrylamide hydrogel phantom for visualizing thermal lesions generated by high intensity focused ultrasound
MJ Choi, SR Guntur, KIL Lee, DG Paeng, A Coleman
Ultrasound in medicine & biology 39 (3), 439-448 , 2013
2013
Citations: 147
Temperature-dependent thermal properties of ex vivo liver undergoing thermal ablation
SR Guntur, KI Lee, DG Paeng, AJ Coleman, MJ Choi
Ultrasound in medicine & biology 39 (10), 1771-1784 , 2013
2013
Citations: 126
In Vitro Studies of the Antimicrobial and Free-Radical Scavenging Potentials of Silver Nanoparticles Biosynthesized From the Extract of Desmostachya bipinnata
VRD Sitaramanjaneya Reddy Guntur, NS Sampath Kumar, Manasa M Hegde
Anal Chem Insights. 13, 1-13 , 2018
2018
Citations: 85
Optimum weighted multimodal medical image fusion using particle swarm optimization
S Shehanaz, E Daniel, SR Guntur, S Satrasupalli
Optik 231, 166413 , 2021
2021
Citations: 69
Robotics in healthcare: an internet of medical robotic things (IoMRT) perspective
SR Guntur, RR Gorrepati, VR Dirisala
Machine learning in bio-signal analysis and diagnostic imaging, 293-318 , 2019
2019
Citations: 62
Changes in ultrasonic properties of liver tissue in vitro during heating-cooling cycle concomitant with thermal coagulation
MJ Choi, SR Guntur, JM Lee, DG Paeng, KIL Lee, A Coleman
Ultrasound in medicine & biology 37 (12), 2000-2012 , 2011
2011
Citations: 61
An improved tissue-mimicking polyacrylamide hydrogel phantom for visualizing thermal lesions with high-intensity focused ultrasound
SR Guntur, MJ Choi
Ultrasound in Medicine & Biology 40 (11), 2680-2691 , 2014
2014
Citations: 47
Internet of medical things
SR Guntur, RR Gorrepati, VR Dirisala
Medical big data and internet of medical things 4 (6), 271-297 , 2018
2018
Citations: 39
Influence of temperature-dependent thermal parameters on temperature elevation of tissue exposed to high-intensity focused ultrasound: numerical simulation
SR Guntur, MJ Choi
Ultrasound in medicine & biology 41 (3), 806-813 , 2015
2015
Citations: 37
Reusable ultrasonic tissue mimicking hydrogels containing nonionic surface-active agents for visualizing thermal lesions
SK Park, SRAR Guntur, KI Lee, DG Paeng, MJ Choi
IEEE transactions on biomedical engineering 57 (1), 194-202 , 2009
2009
Citations: 34
Localization of mixed intracranial hemorrhages by using a ghost convolution-based YOLO network
LP Kothala, P Jonnala, SR Guntur
Biomedical Signal Processing and Control 80, 104378 , 2023
2023
Citations: 33
Effect of printing parameters on the hardness of 3D printed poly-lactic acid parts using DOE approach
N Maguluri, G Suresh, SR Guntur
IOP Conference Series: Materials Science and Engineering 1248 (1), 012004 , 2022
2022
Citations: 27
Temperature dependence of tissue thermal parameters should be considered in the thermal lesion prediction in high-intensity focused ultrasound surgery
SR Guntur, MJ Choi
Ultrasound in Medicine & Biology 46 (4), 1001-1014 , 2020
2020
Citations: 22
Single Image Haze Removal Based on transmission map estimation using Encoder-Decoder based deep learning architecture
S Satrasupalli, E Daniel, SR Guntur
Optik 248, 168197 , 2021
2021
Citations: 15
DroneMap: an IoT network security in internet of drones
RR Gorrepati, SR Guntur
Development and Future of Internet of Drones (IoD): Insights, Trends and … , 2021
2021
Citations: 11
TL-LFF Net: transfer learning based lighter, faster, and frozen network for the detection of multi-scale mixed intracranial hemorrhages through genetic optimization algorithm
LP Kothala, SR Guntur
International Journal of Machine Learning and Cybernetics 16 (10), 7059-7085 , 2025
2025
Citations: 10
End to end system for hazy image classification and reconstruction based on mean channel prior using deep learning network
S Satrasupalli, E Daniel, SR Guntur, S Shehanaz
IET Image Processing 14 (17), 4736-4743 , 2020
2020
Citations: 10
Internet of Medical things: Remote healthcare and health monitoring perspective
SR Guntur, RR Gorrepati, VR Dirisala
Medical big data and internet of medical things, 271-297 , 2018
2018
Citations: 9
GEL-TTA Net: a Global ensemble learning network for the localization of small-scale and mixed intracranial hemorrhages through test time augmentations
LP Kothala, SR Guntur
Multimedia Tools and Applications 84 (14), 13005-13036 , 2025
2025
Citations: 7
An efficient stacked bidirectional GRU‐LSTM network for intracranial hemorrhage detection
LP Kothala, SR Guntur
International Journal of Imaging Systems and Technology , 2023
2023
Citations: 7

Sitaramanjaneya Reddy Guntur

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Scopus Publications

RECENT SCHOLAR PUBLICATIONS

MOST CITED SCHOLAR PUBLICATIONS

Publications