Browsing by Author "Kamath S․, S.K."

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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).
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.
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.
Efficient parameter tuning of neural foundation models for drug perspective prediction from unstructured socio-medical data
(Elsevier Ltd, 2023) Reshma, R.; Kamath S․, S.K.; V.s, A.
The phenomenal popularity of social media platforms over the past decade has accelerated the development of intelligent applications that leverage social media data for informed decision-making in diverse domains like finance, education, public policy and healthcare management practices. While understanding the colloquial language of users on social media remains a challenging problem, access to users’ medical perspectives that conversationally divulge healthcare-related experiences and insights can help reshape healthcare ecosystems like chronic disease management, pandemics, public health, pharmacovigilance and more. Most existing models are constrained to a particular dataset while neglecting model adaptability across data sources and domains. Model generalization across variable data sizes also has received very little research attention. Conventional foundation models can be fine-tuned by adding additional model heads or by appending contributing network layers, however, there has been very little focus on effective parameter calibration for adapting neural foundation models to a specific task. In this study, an Adaptive Learning mechanism for Socio-Medical data (AL4SM) built on generic foundation neural models with efficient parameter learning is proposed, to categorize users’ perspectives on prescription drug-related experiences and adapt to diverse socio-medical data sources of variable sizes. AL4SM aims to lighten the over-parameterized mechanisms adopted by existing foundational techniques by efficiently learning latent medical information based on optimized parameter calibration and weight reinitialization techniques. Comprehensive cross-domain and cross-data analyses are undertaken to explore specific user perspectives related to prescription effectiveness and side effects. Validation experiments conducted on standard datasets obtained from Drugs.com and Druglib.com revealed that the proposed AL4SM outperformed state-of-the-art models, achieving an improvement of 6.06% in accuracy and 7.62% in F1-score for 3-class and 2% in F1-score for 10-class drug perspective categorization. The cross-data experiments further emphasized the superiority of the proposed model, with improved accuracy of 17% on Drugs.com and 9% on Druglib.com datasets, respectively. © 2023 Elsevier Ltd