Faculty Publications
Permanent URI for this communityhttps://idr.nitk.ac.in/handle/123456789/18736
Publications by NITK Faculty
Browse
19 results
Search Results
Item 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.Item COVIDDX: AI-based clinical decision support system for learning COVID-19 disease representations from multimodal patient data(SciTePress, 2021) Mayya, V.; Karthik, K.; Kamath S․, 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. © © 2021 by SCITEPRESS – Science and Technology Publications, Lda. All rights reservedItem 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 S․, 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. © 2021 IEEE.Item LATA – Label attention transformer architectures for ICD-10 coding of unstructured clinical notes(Institute of Electrical and Electronics Engineers Inc., 2021) Mayya, V.; Kamath S․, 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 patients’ structured electronic medical records are critical. Classification of medical diagnostic codes, like ICD-10, is widely employed to categorize patients’ 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. © 2021 IEEE.Item 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 S․, 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. © 2021 IEEE.Item 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 S․, 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. © 2021 IEEE.Item 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 S․, 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 Rocchio’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. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item Detection of Cardiac Arrhythmia Using Machine Learning Approaches(Institute of Electrical and Electronics Engineers Inc., 2022) Chittoria, J.; Kamath S․, 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. © 2022 IEEE.Item 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. © 2023 IEEE.Item 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