This is Dr. Sri Laxmi K, currently working in MVSREC.I am a dedicated researcher with a passion for leveraging cutting-edge technologies to address critical issues in healthcare, particularly in the field of diabetic retinopathy. My research focuses on the development and application of advanced deep learning techniques for the detection and classification of diabetic retinopathy using retinal fundus images. I have developed novel frameworks that encompass pre-processing, segmentation, feature extraction, and classification stages, resulting in comprehensive and efficient approaches for diagnosing diabetic retinopathy. My work has contributed to significant advancements in this domain. My research mainly focuses on medical image processing, big data analytics, Data sciences, and retrieval models. Passionate to work on real-time problems. I have Several publications in National and International journals.
EDUCATION
Ph.D (CSE) , M.TECH(CSE),
RESEARCH, TEACHING, or OTHER INTERESTS
Computer Vision and Pattern Recognition, Multidisciplinary, Artificial Intelligence, Computer Engineering
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Scopus Publications
Scopus Publications
MALICIOUS DOMAIN DETECTION USING INTEGRATED SUPERVISED AND UNSUPERVISED MACHINE LEARNING APPROACHES Journal of Theoretical and Applied Information Technology, 2025
Deep Learning Empowered Diabetic Retinopathy Detection and Classification using Retinal Fundus Images K. Venkata Ramana, A Muralidhar, Bhanu Chander Balusa, M. Bhavsingh, Sravanya Majeti International Journal on Recent and Innovation Trends in Computing and Communication, 2023 Itemsets have been extracted by utilising high utility item (HUI) mining, which provides more benefits to the consumer. This could be one of the significant domains in data mining and be resourceful for several real-time implementations. Even though modern HUI mining algorithms may identify item sets that meet the minimum utility threshold, However, fixing the minimum threshold utility value has not been a simple task, and often it is intricate for the consumers when we keep the minimum utility value low. It might generate a massive amount of itemsets, and when the value is at its maximum, it might provide a smaller amount of itemsets. To avoid these issues, top-k HUI mining, where k represents the number of HUIs to be identified, has been proposed. Further, in this manuscript, the authors projected an algorithm called the top-k exact utility (TKEU) algorithm, which works without computing and comparing transaction weighted utilisation (TWU) values and deliberates the individual utility item values for deriving the top-k HUI. The datasets are pre-processed by the proposed algorithm to lessen the system memory space and to provide optimal outcomes for condensed datasets.
Meta Heuristic Fusion Model for Classification with Modified U-Net-based Segmentation Sri Laxmi Kuna, A. V. Krishna Prasad, Suneetha Bulla International Journal of Advanced Computer Science and Applications, 2023 —General cause of diabetes mellitus is Diabetic Retinopathy (DR), which outcomes in lesions on the retinas that impair vision. If it is not detected in time, the result is severe blindness issues. Regrettably, there is no treatment for DR. Early diagnosis and treatment of DR can greatly lower the risk of visual loss. In contrast to computer-aided diagnosis methods, the manual diagnosis of DR using retina fundus images is more time-consuming effort, and high cost as well, as it is highly prone to error. Deep learning has emerged as one of the most popular methods for improving performance, particularly in the classification and analysis of medical images. Therefore, a deep structure-based DR detection and severity classification has been demonstrated for treating the DR with the usage of fundus images. The major aim of this developed technique is to classify the severity level of the retinal region of the human eye from the fundus images. At first, the required retinal fundus images are collected from the standard benchmark data sources. Secondly, image enhancement techniques are applied to the collected fundus images to improve the quality of images. Thirdly, the abnormality segmentations are carried out by using the optic disc removal process using active contouring model and then, the regional segmentation is done via the Modified U-Net method. Finally, the segmented image is subjected to the hybrid classifier network named a Hybrid Soft Attention-based DenseNet with Multi-Scale Gated ResNet (HSADMGR Net) for classifying the retinal fundus images and finding the severity level of the retinal images with higher accuracy. Furthermore, the parameters present inside the hybrid classifier network are optimized with the help of implemented Multi-Armed Bandits Groundwater Flow Algorithm (MABGFA). The test results regarding the developed deep structure-based DR model are validated with the existing DR detection and classification approaches by using different performance measures
An Efficient Meta-Heuristic-Feature Fusion Model using Deep Neuro-Fuzzy Classifier Sri Laxmi Kuna, A. V. Krishna Prasad International Journal of Advanced Computer Science and Applications, 2022 —Diabetic Retinopathy (DR) is the major cause of the loss of vision among adults worldwide. DR patients generally do not have any symptoms till they reach the final stage. The categorization of retinal images is a remarkable application in detecting DR. Due to the level of sugar available in the blood, the categorization of DR severity becomes complicated to determine the grading level of the damages caused in the retina. To rectify these challenges, a new DR severity classification model is proposed for detecting and treating the DR. The main objective of the proposed model is to classify the severity grades that occurred in the retinal region of the human eye. Initially, gathered retinal images are enhanced and the blood vessel segmentations are done by utilizing the optic disc removal and active contouring model. The abnormalities such as “microaneurysms, hemorrhages, and exudates” are segmented by utilizing Fuzzy C-Means Clustering (FCM) and adaptive thresholding. Then, the segmented images are given to “VGG16 and ResNet”, in which the two different feature sets are acquired. Then, these features are added to obtain the second set of features as F2. Again, the enhanced images act as an input to the “VGG16 and ResNet”, which are attained as the first feature set as F1. In the feature concatenation phase, the resultant of two features is used for feature fusion with the aid of weights parameter that is optimized by Modified Mating Probability-based Water Strider Algorithm (MMP-WSA), where the feature fusion is carried out using the mathematical expression. Finally, the multi-class severity classifications are done by using the Optimized Deep Neuro-Fuzzy Classifier (ODNFC), where the optimization of hyper-parameters is done by the proposed MMP-WSA. Thus, the experimental results of the proposed model have been acquired by the precise segment of the abnormalities and better classification results regarding the grade level.
Publications
SRI LAXMI KUNA (
Scopus ID: 58001488800
RESEARCH OUTPUTS (PATENTS, SOFTWARE, PUBLICATIONS, PRODUCTS)
Patent for Design "Coffee Writing Pen" granted on 30/8/2019.
Patent Published titled "Method and System for Data Security on Capsule Endoscope in a Cloud Computing Environment" published on 30/11/2018
