Browsing by Author "Mayya, V."

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An empirical study of preprocessing techniques with convolutional neural networks for accurate detection of chronic ocular diseases using fundus images
(Springer, 2023) Mayya, V.; Kamath S․, S.K.; Kulkarni, U.; Surya, D.K.; Acharya, U.R.
Chronic Ocular Diseases (COD) such as myopia, diabetic retinopathy, age-related macular degeneration, glaucoma, and cataract can affect the eye and may even lead to severe vision impairment or blindness. According to a recent World Health Organization (WHO) report on vision, at least 2.2 billion individuals worldwide suffer from vision impairment. Often, overt signs indicative of COD do not manifest until the disease has progressed to an advanced stage. However, if COD is detected early, vision impairment can be avoided by early intervention and cost-effective treatment. Ophthalmologists are trained to detect COD by examining certain minute changes in the retina, such as microaneurysms, macular edema, hemorrhages, and alterations in the blood vessels. The range of eye conditions is diverse, and each of these conditions requires a unique patient-specific treatment. Convolutional neural networks (CNNs) have demonstrated significant potential in multi-disciplinary fields, including the detection of a variety of eye diseases. In this study, we combined several preprocessing approaches with convolutional neural networks to accurately detect COD in eye fundus images. To the best of our knowledge, this is the first work that provides a qualitative analysis of preprocessing approaches for COD classification using CNN models. Experimental results demonstrate that CNNs trained on the region of interest segmented images outperform the models trained on the original input images by a substantial margin. Additionally, an ensemble of three preprocessing techniques outperformed other state-of-the-art approaches by 30% and 3%, in terms of Kappa and F1 scores, respectively. The developed prototype has been extensively tested and can be evaluated on more comprehensive COD datasets for deployment in the clinical setup. © 2022, The Author(s).
Applications of Machine Learning in Diabetic Foot Ulcer Diagnosis using Multimodal Images: A Review
(International Association of Engineers, 2023) Mayya, V.; Tummala, V.; Reddy, C.U.; Mishra, P.; Boddu, R.; Olivia, D.; Kamath S․, S.S.
Diabetes related complications such as Diabetic Foot Ulcers (DFU) may necessitate recurrent hospitalisations and expensive treatments. Uncontrolled diabetes can result in severe DFUs, resulting in amputation of lower limbs or feet, prolonged debilitation and diminished quality of life. Early diagnosis and proactive management are reported to significantly enhance the prognosis and reduce the onset of further complications. In this study, research works on developing clinical decision support systems (CDSS) for the identification and segmentation of DFU are systematically reviewed. The techniques employed range from traditional image processing techniques to approaches based on deep learning (DL). A taxonomy of DFU CDSSs is presented, categorised into two groups: RGB-based techniques and thermal imaging-based approaches. To the best of our knowledge, this is the first attempt at a comprehensive study of CDSSs for DFU related investigative tasks, based on different imaging modalities. We also delve into the difficulties experienced in the process of creating efficient, reliable, and accurate models for the early detection of DFU, and highlight the vast potential for further research in this emerging domain. © (2023), (International Association of Engineers). All Rights Reserved.
Automated microaneurysms detection for early diagnosis of diabetic retinopathy: A Comprehensive review
(Elsevier B.V., 2021) Mayya, V.; Kamath S․, S.S.; Kulkarni, U.
Diabetic retinopathy (DR), a chronic disease in which the retina is damaged due to small vessel damage caused by diabetes mellitus, is one of the leading causes of vision impairment in diabetic patients. Detection of the earliest clinical sign of the advent of DR is a critical requirement for intervention and effective treatment. Ophthalmologists are trained to identify DR, based on examining specific minute changes in the eye - microaneurysms, retinal haemorrhages, macular edema and changes in the retinal blood vessels. Segmentation of microaneurysms (MA) is a critical requirement for the early diagnosis of DR and has been the primary focus of the research community over the past few years. In this work, a systematic review of existing literature is carried out to examine the diagnostic use of automated MA detection and segmentation for early DR diagnosis. We mainly focus on existing early DR diagnosis techniques to understand their strengths and weaknesses. Though early diagnosis is performed using colour fundus photography, fluorescein angiography or optical coherence tomography angiography images, our study is limited to colour fundus based techniques. The early DR diagnosis methodologies reviewed in this article can be broadly classified into classical image processing, conventional machine learning (ML), and deep learning (DL) based techniques. Though significant progress has been achieved in these three classes of early DR diagnosis, several challenges and gaps still exist, underscoring a considerable scope for the development of fully automated, user-friendly early DR diagnosis and grading systems. We discuss in detail the challenges that need to be addressed in designing such effective, efficient, and robust algorithms for early DR diagnosis systems and also the ample scope for future research in this area. © 2021
Content-based medical image retrieval system for lung diseases using deep CNNs
(Springer Science and Business Media B.V., 2022) Agrawal, S.; Chowdhary, A.; Agarwala, S.; Mayya, V.; Kamath S․, S.K.
Content-based image retrieval (CBIR) systems are designed to retrieve images that are relevant, based on detailed analysis of latent image characteristics, thus eliminating the dependency of natural language tags, text descriptions, or keywords associated with the images. A CBIR system maintains high-level image visuals in the form of feature vectors, which the retrieval engine leverages for similarity-based matching and ranking for a given query image. In this paper, a CBIR system is proposed for the retrieval of medical images (CBMIR) for enabling the early detection and classification of lung diseases based on lung X-ray images. The proposed CBMIR system is built on the predictive power of deep neural models for the identification and classification of disease-specific features using transfer learning based models trained on standard COVID-19 Chest X-ray image datasets. Experimental evaluation on the standard dataset revealed that the proposed approach achieved an improvement of 49.71% in terms of precision, averaging across various distance metrics. Also, an improvement of 26.55% was observed in the area under precision-recall curve (AUPRC) values across all subclasses. © 2022, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management.
COVIDDX: AI-based clinical decision support system for learning COVID-19 disease representations from multimodal patient data
(SciTePress, 2021) Mayya, V.; Karthik, K.; Kamath Sâ€¤, S.; Karadka, K.; Jeganathan, J.
The COVID-19 pandemic has affected the world on a global scale, infecting nearly 68 million people across the world, with over 1.5 million fatalities as of December 2020. A cost-effective early-screening strategy is crucial to prevent new outbreaks and to curtail the rapid spread. Chest X-ray images have been widely used to diagnose various lung conditions such as pneumonia, emphysema, broken ribs and cancer. In this work, we explore the utility of chest X-ray images and available expert-written diagnosis reports, for training neural network models to learn disease representations for diagnosis of COVID-19. A manually curated dataset consisting of 450 chest X-rays of COVID-19 patients and 2,000 non-COVID cases, along with their diagnosis reports were collected from reputed online sources. Convolutional neural network models were trained on this multimodal dataset, for prediction of COVID-19 induced pneumonia. A comprehensive clinical decision support system powered by ensemble deep learning models (CADNN) is designed and deployed on the web. The system also provides a relevance feedback mechanism through which it learns multimodal COVID-19 representations for supporting clinical decisions. Â© Â© 2021 by SCITEPRESS â€“ Science and Technology Publications, Lda. All rights reserved
Deep Learning based detection of Diabetic Retinopathy from Inexpensive fundus imaging techniques
(Institute of Electrical and Electronics Engineers Inc., 2021) Mukesh, B.R.; Harish, T.; Mayya, V.; Kamath Sâ€¤, S.
Diabetic Retinopathy is the leading cause of blindness across the world as per statistics published by the World Health Organization. Recently, there has been significant research on adopting deep learning methodologies to automate and improve the process of evaluating the advent and progress of chronic eye diseases using eye fundus images. Typically, eye fundus imaging equipment is used by trained specialists for evaluating eye health, however, fundus imaging tends to be expensive, and also the high-end equipment used is typically available in large hospitals and urban areas. This cost barrier leads to an imbalance in care between the developed and developing parts of the world. In this paper, we propose an inexpensive stand-in for such a device and a deep neural model pipeline that is able to analyze these images to determine the need for further evaluation from a trained ophthalmologist. The pipeline is able to achieve an AUC score of 0.9781 in detecting Referable DR. We also benchmark the proposed deep learning pipeline against other pipelines on standard datasets to demonstrate the capability of the network. Â© 2021 IEEE.
DeepOA: Clinical Decision Support System for Early Detection and Severity Grading of Knee Osteoarthritis
(Institute of Electrical and Electronics Engineers Inc., 2021) Dalia, Y.; Bharath, A.; Mayya, V.; Kamath Sâ€¤, S.S.
Knee Osteoarthritis (OA) is a medical condition affecting the knee joint that causes pain due to the cartilage wear-And-Tear. The severity of the impairment is graded by experienced radiologists as per standardized grading systems like the Kellgren-Lawrence(KL) grading scheme. Early detection and classification of knee OA in a patient before it increases in severity can significantly aid in corrective measures and benefit humankind. In this work, we propose a DL model to automatically segment the knee region and predict onset of Knee OA with X-ray scans. A comparative study using an ensemble model consisting of a YOLOv5 object detection algorithm for knee joint segmentation is also proposed. Various classification models such as VGG16, Resnet etc., are experimented with for the KL grade classification. The detailed experiments are conducted to understand the need for the region of interest segmentation step in KL grade classification. The proposed Clinical Decision Support System (CDSS) can help the medical practitioners perform preemptive screening based on X-ray scans for detecting onset earlier and for enabling required treatment. Â© 2021 IEEE.
Detection of bradycardia from electrocardiogram signals using feature extraction and snapshot ensembling
(Springer Science and Business Media B.V., 2022) Sengupta, S.; Mayya, V.; Kamath S․, S.K.
One of the most common diagnostic techniques for detecting certain cardiovascular diseases is using electrocardiogram (ECG) readings. Doctors around the world mostly rely on human insight and processing to determine and interpret these ECG graphs. This process is thus often prone to human error introduced to the increasing cognitive burden of doctors and might introduce delays in diagnosis, which could be fatal. Ongoing research has focused on the design of automated algorithms to accurately diagnose and speed up the process of analyzing and interpreting an ECG signal. In this paper, we present a novel approach that utilizes a neural network pipeline with Snapshot ensembling to enable automated Bradycardia detection from ECG signals. Before the modeling phase, a cross-correlation and segmentation method is used for detecting relevant features in the ECG signals, using which the detection performance is improved. The proposed approach gave good results, with around 95% accuracy and an AUC score of about 0.96, implying an efficient and accurate classification. © 2022, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management.
Detection of Cardiac Arrhythmia Using Machine Learning Approaches
(Institute of Electrical and Electronics Engineers Inc., 2022) Chittoria, J.; Kamath Sâ€¤, S.; Mayya, V.
Arrhythmia is a cardiovascular disease that alters the heart rate, resulting in too fast, too slow, or irregular rhythms. It is a life-threatening disease if left untreated. Traditionally, arrhythmia is diagnosed by a trained doctor, using an electrocardiogram to analyze irregular heartbeats. However, these methods are vulnerable to inadvertent misdiagnosis, especially during the early stages of the disease. In this paper, an approach for cardiac arrhythmia detection is presented, where the subjects or instances are first categorized as diseased or normal and then further graded into normal (non-diseased) or as distinct subtypes of cardiac arrhythmia. The dataset was obtained from the UCI Machine Learning Data Repository, and machine learning methods such as XGBoost, CatBoost, SVM, and Random Forest, were experimented with. Addition-ally, the mutual information-based feature selection approach, minimal redundancy maximum relevance (mRMR), is proposed to improve classification accuracy. Standard evaluation metrics such as accuracy, f1-score, precision, and recall are utilized for comparison of the obtained results. The experimental results demonstrated that accuracy of 81.48% was achieved for multi-class classification, while binary classification achieved up to 84% accuracy. Â© 2022 IEEE.
Ensemble deep neural models for automated abnormality detection and classification in precision care applications
(Elsevier, 2023) Karthik, K.; Mayya, V.; Kamath S․, S.
Radiological imaging is one of the most relied upon modalities in the clinical diagnosis and treatment planning process. Conventional diagnosis involves the manual analysis of radiology images by experienced radiologists, which is often a time-consuming and labor-intensive process. The scarcity of experienced radiologists and necessity of large-scale X-rays image analysis given the huge diagnosis workload at most hospitals stresses the need for automated clinical diagnosis systems capable of fast and accurate identification of abnormalities, disease characteristic identification, disease classification, and others. Such automated methods are thus a fundamental requirement in clinical workflow management applications. In this work, we present an approach for multitask clinical objectives such as disease classification and detection of abnormalities. The proposed model leverages the predictive power of deep neural models for enabling evidence-based diagnosis. During validation experiments, the model achieved an accuracy of 89.58% along with sensitivity and specificity of 85.83% and 90.83%, respectively, with an AUC (area under the ROC curve) of 95.84% for normal/no findings versus COVID-19 chest radiograph classification and an accuracy of 73.19% for upper extremity musculoskeletal images. The performance of the model for the classification and abnormality identification tasks, when benchmarked over multiple standard datasets, emphasizes its suitability and adaptability in real-world clinical settings, with significant improvements in radiology-based diagnosis workflow and patient care. © 2023 Elsevier Inc. All rights reserved.
Ensemble Machine Learning Approaches for Automated Fungal Keratitis Diagnosis Using In Vivo Confocal Microscopy Images
(John Wiley and Sons Inc, 2025) Sowmya Kamath, S.; Reji, S.; Vaibhava Lakshmi, V.; Supreetha, S.; Gawas, P.; Mayya, V.; Hazarika, M.
Fungal keratitis (FK) is a severe ocular infection that can lead to significant vision problems or blindness if not diagnosed and treated promptly. Early and accurate detection of FK is essential for effective management. Traditional diagnostic methods are often time-consuming and require specialized laboratory resources. Recently, advances in artificial intelligence and computer vision have enabled automated diagnosis of FK using slit-lamp images. In this article, a comprehensive evaluation of state-of-the-art techniques adopted for classifying FK using in vivo confocal microscopy (IVCM) images is presented. Detailed experiments and performance evaluation of various machine learning models are systematically performed, with a focus on evaluating the effect of diverse techniques for image processing, data augmentation, hyperparameters and model finetuning to assess each model's strengths and limitations. Experiments revealed that applying green channel preprocessing with a 12-feature set achieved 99% accuracy using Random Forest, highlighting its effectiveness in FK detection, while complex techniques like histogram modelling reduced accuracy to 64%. Robust models like AdaBoost and RUSBoost maintained high F1-scores, demonstrating adaptability to imbalanced medical datasets, and to real-world clinical scenarios. © 2025 The Author(s). Healthcare Technology Letters published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.
Explainable Deep Neural Models for COVID-19 Prediction from Chest X-Rays with Region of Interest Visualization
(Institute of Electrical and Electronics Engineers Inc., 2021) Nedumkunnel, I.M.; Elizabeth George, L.; Kamath Sâ€¤, S.S.; Rosh, N.A.; Mayya, V.
COVID-19 has been designated as a once-in-a-century pandemic, and its impact is still being felt severely in many countries, due to the extensive human and green casualties. While several vaccines are under various stage of development, effective screening procedures that help detect the disease at early stages in a non-invasive and resource-optimized manner are the need of the hour. X-ray imaging is fairly accessible in most healthcare institutions and can prove useful in diagnosing this respiratory disease. Although a chest X-ray scan is a viable method to detect the presence of this disease, the scans must be analyzed by trained experts accurately and quickly if large numbers of tests are to be processed. In this paper, a benchmarking study of different preprocessing techniques and state-of-the-art deep learning models is presented to provide comprehensive insights into both the objective and subjective evaluation of their performance. To analyze and prevent possible sources of bias, we preprocessed the dataset in two ways-first, we segmented the lungs alone, and secondly, we formed a bounding box around the lung and used only this area to train. Among the models chosen to benchmark, which were DenseNet201, EfficientNetB7, and VGG-16, DenseNet201 performed better for all three datasets. Â© 2021 IEEE.
ICU Patients’ Pattern Recognition and Correlation Identification of Vital Parameters Using Optimized Machine Learning Models
(J.J. Strossmayer University of Osijek , Faculty of Electrical Engineering, Computer Science and Information Technology, 2023) Yallabandi, G.; Jeganathan, J.; Mayya, V.; Sowmya Kamath, S.
– Early detection of patient deterioration in the Intensive Care Unit (ICU) can play a crucial role in improving patient outcomes. Conventional severity scales currently used to predict patient deterioration are based on a number of factors, the majority of which consist of multiple investigations. Recent advancements in machine learning (ML) within the healthcare domain offer the potential to alleviate the burden of continuous patient monitoring. In this study, we propose an optimized ML model designed to leverage variations in vital signs observed during the final 24 hours of an ICU stay for outcome predictions. Further, we elucidate the relative contributions of distinct vital parameters to these outcomes The dataset compiled in real-time encompasses six pivotal vital parameters: systolic (0) and diastolic (1) blood pressure, pulse rate (2), respiratory rate (3), oxygen saturation (SpO2) (4), and temperature (5). Of these vital parameters, systolic blood pressure emerges as the most significant predictor associated with mortality prediction. Using a fivefold cross-validation method, several ML classifiers are used to categorize the last 24 hours of time series data after ICU admission into three groups: recovery, death, and intubation. Notably, the optimized Gradient Boosting classifier exhibited the highest performance in detecting mortality, achieving an area under the receiver-operator curve (AUC) of 0.95. Through the integration of electronic health records with this ML software, there is the promise of early notifications regarding adverse outcomes, potentially several hours before the onset of hemodynamic instability. © 2023, J.J. Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology. All rights reserved.
LATA â€“ Label attention transformer architectures for ICD-10 coding of unstructured clinical notes
(Institute of Electrical and Electronics Engineers Inc., 2021) Mayya, V.; Kamath Sâ€¤, S.S.; Sugumaran, V.
Effective code assignment for patient clinical records in a hospital plays a significant role in the process of standardizing medical records, mainly for streamlining clinical care delivery, billing, and managing insurance claims. The current practice employed is manual coding, usually carried out by trained medical coders, making the process subjective, error-prone, inexact, and time-consuming. To alleviate this cost-intensive process, intelligent coding systems built on patientsâ€™ structured electronic medical records are critical. Classification of medical diagnostic codes, like ICD-10, is widely employed to categorize patientsâ€™ clinical conditions and associated diagnoses. In this work, we present a neural model LATA, built on Label Attention Transformer Architectures for automatic assignment of ICD-10 codes. Our work is benchmarked on the CodiEsp dataset, a dataset for automatic clinical coding systems for multilingual medical documents, used in the eHealth CLEF 2020-Multilingual Information Extraction Shared Task. The experimental results reveal that the proposed LATA variants outperform their basic BERT counterparts by 33-49% in terms of standard metrics like precision, recall, F1-score and mean average precision. The label attention mechanism also enables direct extraction of textual evidence in medical documents that map to the clinical ICD-10 diagnostic codes. Â© 2021 IEEE.
Multi-channel, convolutional attention based neural model for automated diagnostic coding of unstructured patient discharge summaries
(Elsevier B.V., 2021) Mayya, V.; Kamath S?, S.S.; S. Krishnan, G.S.; Gangavarapu, T.
Effective coding of patient records in hospitals is an essential requirement for epidemiology, billing, and managing insurance claims. The prevalent practice of manual coding, carried out by trained medical coders, is error-prone and time-consuming. Mitigating this labor-intensive process by developing diagnostic coding systems built on patients’ Electronic Medical Records (EMRs) is vital. However, developing nations with low digitization rates have limited availability of structured EMRs, thereby necessitating a need for systems that leverage unstructured data sources. Despite the rich clinical information available in such unstructured data, modeling them is complex, owing to the variety and sparseness of diagnostic codes, complex structural and temporal nature of summaries, and prolific use of medical jargon. This work proposes a context-attentive network to facilitate automatic diagnostic code assignment as a multi-label classification problem. The proposed model facilitates information aggregation across a patient's discharge summary via multi-channel, variable-sized convolutional filters to extract multi-granular snippets. The attention mechanism enables selecting vital segments in those snippets that map to the clinical codes. The model's superior performance underscores its effectiveness compared to the state-of-the-art on the MIMIC-III database. Additionally, experimental validation using the CodiEsp dataset exhibited the model's interpretability and explainability. © 2021 Elsevier B.V.
Multi-scale convolutional neural network for accurate corneal segmentation in early detection of fungal keratitis
(MDPI, 2021) Mayya, V.; Kamath S?, S.; Kulkarni, U.; Hazarika, M.; Barua, P.D.; Acharya, U.R.
Microbial keratitis is an infection of the cornea of the eye that is commonly caused by prolonged contact lens wear, corneal trauma, pre-existing systemic disorders and other ocular surface disorders. It can result in severe visual impairment if improperly managed. According to the latest World Vision Report, at least 4.2 million people worldwide suffer from corneal opacities caused by infectious agents such as fungi, bacteria, protozoa and viruses. In patients with fungal keratitis (FK), often overt symptoms are not evident, until an advanced stage. Furthermore, it has been reported that clear discrimination between bacterial keratitis and FK is a challenging process even for trained corneal experts and is often misdiagnosed in more than 30% of the cases. However, if diagnosed early, vision impairment can be prevented through early cost-effective interventions. In this work, we propose a multi-scale convolutional neural network (MS-CNN) for accurate segmentation of the corneal region to enable early FK diagnosis. The proposed approach consists of a deep neural pipeline for corneal region segmentation followed by a ResNeXt model to differentiate between FK and non-FK classes. The model trained on the segmented images in the region of interest, achieved a diagnostic accuracy of 88.96%. The features learnt by the model emphasize that it can correctly identify dominant corneal lesions for detecting FK. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
Multi-task deep neural network models for learning COVID-19 disease representations from multimodal data
(Inderscience Publishers, 2023) Mayya, V.; Karthik, K.; Karadka, K.P.; Kamath S․, S.S.
Over the continued course of the COVID-19 pandemic, a significant volume of expert-written diagnosis reports has been accumulated that capture a multitude of symptoms and observations on diagnosed COVID-19 cases, along with expert-validated chest X-ray scans. The utility of rich, latent information embedded in such unstructured expert-written diagnosis reports and its importance as a source of valuable disease-specific information has been explored to a very limited extent. In this work, a convolutional attention-based dense (CAD) neural model for COVID-19 prediction is proposed. The model is trained on the rich disease-specific parameters extracted from chest X-ray images and expert-written diagnostic text reports to support an evidence-based diagnosis. Scalability is ensured by incorporating content based learning models for automatically generating diagnosis reports of identified COVID-19 cases, reducing radiologists' cognitive burden. Experimental evaluation showed that multimodal patient data plays a vital role in diagnosing early-stage cases, thus helping hasten the diagnosis process. © 2023 Inderscience Enterprises Ltd.
Ocular Region Segmentation Model for Diagnosis of Microbial Keratitis Using Slit-Lamp Photography
(Institute of Electrical and Electronics Engineers Inc., 2023) Supreetha, R.; Sowmya Kamath, S.; Mayya, V.
Corneal disease, a prevalent cause of global blindness, can lead to severe complications such as Microbial Keratitis, an inflammatory condition of the cornea often caused by bacterial or fungal infections. Early detection and timely treatment are crucial to prevent vision loss associated with this condition. Slit-lamp photography, a standard tool for ocular examination, is commonly employed for diagnosis. To address the growing demand for ophthalmology specialists, numerous studies have explored the use of Deep Learning (DL) algorithms to achieve precise and accurate segmentation of ocular structures, including the cornea, from slit-lamp photography images. In this study, an ocular region segmentation model trained on heterogeneous slit-lamp image datasets for improving learning performance is presented. Various data augmentation strategies are experimented with, and optimization techniques are incorporated. Experiments revealed that the model outperformed several state-of-the-art works concerning Dice score. Furthermore, the model can also be utilized for the unsupervised learning task of mask generation, as the segmentation findings are on par with the ground truth. Â© 2023 IEEE.
Sketch-Based Image Retrieval Using Convolutional Neural Networks Based on Feature Adaptation and Relevance Feedback
(Springer Science and Business Media Deutschland GmbH, 2022) Kumar, N.; Ahmed, R.; B Honnakasturi, V.; Kamath Sâ€¤, S.; Mayya, V.
Sketch-based Image Retrieval (SBIR) is an approach where natural images are retrieved according to the given input sketch query. SBIR has many applications, for example, searching for a product given the sketch pattern in digital catalogs, searching for missing people given their prominent features from a digital people photo repository etc. The main challenge involved in implementing such a system is the absence of semantic information in the sketch query. In this work, we propose a combination of image prepossessing and deep learning-based methods to tackle this issue. A binary image highlighting the edges in the natural image is obtained using Canny-Edge detection algorithm. The deep features were extracted by an ImageNet based CNN model. Cosine similarity and Euclidean distance measures are adopted to generate the rank list of candidate natural images. Relevance feedback using Rocchioâ€™s method is used to adapt the query of sketch images and feature weights according to relevant images and non-relevant images. During the experimental evaluation, the proposed approach achieved a Mean average precision (MAP) of 71.84%, promising performance in retrieving relevant images for the input query sketch images. Â© 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.