Faculty Publications

Now showing 1 - 3 of 3

A deep neural network model for content-based medical image retrieval with multi-view classification
(Springer Science and Business Media Deutschland GmbH, 2021) Karthik, K.; Kamath S?, S.S.
In medical applications, retrieving similar images from repositories is most essential for supporting diagnostic imaging-based clinical analysis and decision support systems. However, this is a challenging task, due to the multi-modal and multi-dimensional nature of medical images. In practical scenarios, the availability of large and balanced datasets that can be used for developing intelligent systems for efficient medical image management is quite limited. Traditional models often fail to capture the latent characteristics of images and have achieved limited accuracy when applied to medical images. For addressing these issues, a deep neural network-based approach for view classification and content-based image retrieval is proposed and its application for efficient medical image retrieval is demonstrated. We also designed an approach for body part orientation view classification labels, intending to reduce the variance that occurs in different types of scans. The learned features are used first to predict class labels and later used to model the feature space for similarity computation for the retrieval task. The outcome of this approach is measured in terms of error score. When benchmarked against 12 state-of-the-art works, the model achieved the lowest error score of 132.45, with 9.62–63.14% improvement over other works, thus highlighting its suitability for real-world applications. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.
Swarm optimisation-based bag of visual words model for content-based X-ray scan retrieval
(Inderscience Publishers, 2022) Karthik, K.; Kamath S․, S.
Classification and retrieval of medical images (MedIR) are emerging applications of computer vision for enabling intelligent medical diagnostics. Medical images are multi-dimensional and require specialised processing for the extraction of features from their manifold underlying content. Existing models often fail to consider the inherent characteristics of data and have thus often fallen short when applied to medical images. In this paper, we present a MedIR approach based on the bag of visual words (BoVW) model for content-based medical image retrieval. When it comes to any medical approach models, an imbalance in the dataset is one of the issues. Hence the perspective is also considering a balanced set of categories from an imbalanced dataset. The proposed work on BoVW model extracts features from each image are used to train supervised machine learning classifier for X-ray medical image classification and retrieval. During the experimental validation, the proposed model performed well with the classification accuracy of 89.73% and a good retrieval result using our filter-based approach. © © 2022 Inderscience Enterprises Ltd.
MSDNet: a deep neural ensemble model for abnormality detection and classification of plain radiographs
(Springer Science and Business Media Deutschland GmbH, 2023) Karthik, K.; Kamath S․, S.S.
Modern medical diagnostic techniques facilitate accurate diagnosis and treatment recommendations in healthcare. Such diagnostics procedures are performed daily in large numbers, thus, the clinical interpretation workload of radiologists is very high. Identification of abnormalities is a predominantly manual task that is performed by radiologists before the medical scans are available to the patient’s referring doctor for further recommendations. On the other hand, for a radiologist to delineate the imaging study’s findings/observations as a textual report is also a tedious task. Automated methods for radiographic image examination for identifying abnormalities and generating reliable radiology report are thus a fundamental requirement in clinical workflow management applications. In this work, we present an automated approach for abnormality classification, localization and diagnostic report retrieval for identified abnormalities. We propose MSDNet, an ensemble of Convolutional Neural models for abnormality classification, which combines the features of multiple CNN models to enhance abnormality classification performance. The proposed model also is designed to localize and visualize the detected abnormality on the radiograph image, based on an abnormal region detection algorithm to further optimize the diagnosis quality. Furthermore, the extracted features generated by MSDNet are used to automatically generate the diagnosis text report using an automatic content-based report retrieval algorithm. The upper extremity musculo-skeletal images from the MURA dataset and chest X-ray images from Indiana dataset were used for the experimental evaluation of the proposed approach. The proposed model achieved promising results, with an accuracy of 82.69%, showing its significant impact on alleviating radiologists’ cognitive load, thus improving the overall efficiency. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

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