Faculty Publications
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Item A novel feature extraction technique for pulmonary sound analysis based on EMD(Elsevier Ireland Ltd, 2018) Mondal, A.; Banerjee, P.; Tang, H.Background and objective: The stethoscope based auscultation technique is a primary diagnostic tool for chest sound analysis. However, the performance of this method is limited due to its dependency on physicians experience, knowledge and also clarity of the signal. To overcome this problem we need an automated computer-aided diagnostic system that will be competent in noisy environment. In this paper, a novel feature extraction technique is introduced for discriminating various pulmonary dysfunctions in an automated way based on pattern recognition algorithms. Method: In this work, the disease correlated relevant characteristics of lung sounds signals are identified in terms of statistical distribution parameters: mean, variance, skewness, and kurtosis. These features are extracted from selective morphological components of the mapped signal in the empirical mode decomposition domain. The feature set is fed to the classifier model to differentiate their corresponding classes. Results: The significance of features developed are validated by conducting several experiments using supervised and unsupervised classifiers. Furthermore, the discriminating power of the proposed features is compared with three types of baseline features. The experimental result is evaluated by statistical analysis and also validated with physicians inference. Conclusions: It is found that the proposed features extraction technique is superior to the baseline methods in terms of classification accuracy, sensitivity and specificity. The developed method gives better results compared to baseline methods in any circumstance. The proposed method gives a higher accuracy of 94.16, sensitivity of 100 and specificity of 93.75 for an artificial neural network classifier. © 2018 Elsevier B.V.Item A fully-automated system for identification and classification of subsolid nodules in lung computed tomographic scans(Elsevier Ltd, 2019) Savitha, G.; Padikkal, P.A fully-automated computer-aided detection (CAD) system is being proposed in this paper for identification and classification of subsolid lung nodules present in Computed Tomography(CT) scans. The system consists of two stages. The first stage aims at detecting locations of the nodules, while the second one classifies the same into the solid and subsolid category. The system performs segmentation of the region of interest (ROI) and extraction of relevant features from the segmented ROI. Graylevel covariance matrix (GLCM) is being used to extract the Feature vectors. Principle component analysis (PCA) algorithm is used to select significant features in the feature space formed by the vectors. The nodule localization is performed using support vector machine, fuzzy C-means, and random forest classification algorithms. The identified nodules are further grouped into solid and subsolid nodules by extracting histogram of gradient (HoG) features adopting K-means and support vector machine (SVM) classifiers. A large number of annotated images from the widely available benchmark database is tested to validate the results. Efficiency and reliability of the system are evaluated visually and numerically using the relevant quantitative measures. The developed CAD system is found to identify subsolid nodules with a high percentage of accuracy. © 2019 Elsevier LtdItem Poly(N,N-diethyl acrylamide)/functionalized graphene quantum dots hydrogels loaded with doxorubicin as a nano-drug carrier for metastatic lung cancer in mice(Elsevier Ltd, 2019) Havanur, S.; Batish, I.; Cheruku, S.P.; Gourishetti, K.; JagadeeshBabu, J.; Kumar, N.Cancer has emanated as a daunting menace to human-kind even though medicine, science, and technology has reached its zenith. Subsequent scarcity in the revelation of new drugs, the exigency of salvaging formerly discovered toxic drugs such as doxorubicin has emerged. The invention of drug carrier has made drug delivery imminent which is ascribable to its characteristic traits of specific targeting, effective response to stimuli and biocompatibility. In this paper, the nanoscale polymeric drug carrier poly(N,N-diethyl acrylamide) nanohydrogel has been synthesized by inverse emulsion polymerization. Lower critical solution temperature of the polymeric carrier has been modified using graphene quantum. The particle size of pure nanohydrogel was in the range of 47 to 59.5 nm, and graphene quantum dots incorporated nanohydrogels was in the range of 68.1 to 87.5 nm. Doxorubicin (hydroxyl derivative of anthracycline) release behavior as a function of time and temperature was analyzed, and the Lower critical solution temperature of the synthesized nanohydrogels has been found to be in the range of 28–42 °C. Doxorubicin release characteristics have improved significantly as the surrounding temperature of the release media was increased near to physiological temperature. Further, the cumulative release profile was fitted in the different kinetic model and found to follow a Fickian diffusion release mechanism. The hydrogel was assessed for its cytotoxicity in B16F10 cells by MTT assay. In-vivo studies were done to study the lung metastasis by melanoma cancer and the results showed a rational favorable prognosis which was confirmed by evaluating hematological parameters and the non-immunogenic nature of nanohydrogel by cytokine assay. Comprehensively, the results suggested that poly(N,N-diethyl acrylamide) nanohydrogels have potential application as an intelligent drug carrier for melanoma cancer. © 2019 Elsevier B.V.Item A holistic deep learning approach for identification and classification of sub-solid lung nodules in computed tomographic scans(Elsevier Ltd, 2020) Savitha, G.; Padikkal, P.Prompt detection of malignant lung nodules significantly improves the chance of survivability of the affected patients. The lung nodules in their early stages appear as subsolid or part-solid nodules whose identification remains a challenging task. Many of the present lung nodule detection systems fail to identify the nodules in their early stages. Limitations in the feature extraction process lead to significant false-positive rates, which eventually diminish the accuracy aspects of the system. In this study, a sophisticated deep learning approach is employed for feature extraction which improves the nodule localization or identification stage of the system. Further, the false positives sneaking out of the system are drastically reduced by adopting a Conditional Random Framework in the model. The quantitative demonstrations prove the efficiency of the model to detect sub-solid nodules in CT images. Thus the employability of the model for early detection of the nodules is tested and verified. © 2020 Elsevier LtdItem Particulate matter (PM10) enhances RNA virus infection through modulation of innate immune responses(Elsevier Ltd, 2020) Mishra, R.; Krishnamoorthy, P.; Gangamma, S.; Raut, A.A.; Kumar, H.Particulate matter (PM10) enhances severity of influenza virus infection through skewing innate immunity via modulation of metabolic pathways-related genes.; Sensing of pathogens by specialized receptors is the hallmark of the innate immunity. Innate immune response also mounts a defense response against various allergens and pollutants including particulate matter present in the atmosphere. Air pollution has been included as the top threat to global health declared by WHO which aims to cover more than three billion people against health emergencies from 2019 to 2023. Particulate matter (PM), one of the major components of air pollution, is a significant risk factor for many human diseases and its adverse effects include morbidity and premature deaths throughout the world. Several clinical and epidemiological studies have identified a key link between the PM existence and the prevalence of respiratory and inflammatory disorders. However, the underlying molecular mechanism is not well understood. Here, we investigated the influence of air pollutant, PM10 (particles with aerodynamic diameter less than 10 ?m) during RNA virus infections using Highly Pathogenic Avian Influenza (HPAI) – H5N1 virus. We thus characterized the transcriptomic profile of lung epithelial cell line, A549 treated with PM10 prior to H5N1infection, which is known to cause severe lung damage and respiratory disease. We found that PM10 enhances vulnerability (by cellular damage) and regulates virus infectivity to enhance overall pathogenic burden in the lung cells. Additionally, the transcriptomic profile highlights the connection of host factors related to various metabolic pathways and immune responses which were dysregulated during virus infection. Collectively, our findings suggest a strong link between the prevalence of respiratory illness and its association with the air quality. © 2020 Elsevier Ltd; © 2020 Elsevier LtdItem A novel receptive field-regularized V-net and nodule classification network for lung nodule detection(John Wiley and Sons Inc, 2022) Dodia, S.; Annappa, B.; Mahesh, M.Recent advancements in deep learning have achieved great success in building a reliable computer-aided diagnosis (CAD) system. In this work, a novel deep-learning architecture, named receptive field regularized V-net (RFR V-Net), is proposed for detecting lung cancer nodules with reduced false positives (FP). The method uses a receptive regularization on the encoder block's convolution and deconvolution layer of the decoder block in the V-Net model. Further, nodule classification is performed using a new combination of SqueezeNet and ResNet, named nodule classification network (NCNet). Postprocessing image enhancement is performed on the 2D slice by increasing the image's intensity by adding pseudo-color or fluorescence contrast. The proposed RFR V-Net resulted in dice similarity coefficient of 95.01% and intersection over union of 0.83, respectively. The proposed NCNet achieved the sensitivity of 98.38% and FPs/Scan of 2.3 for 3D representations. The proposed NCNet resulted in considerable improvements over existing CAD systems. © 2021 Wiley Periodicals LLC.Item Transfer Learning-Hierarchical Segmentation on COVID CT Scans(Springer, 2024) Singh, S.; Pais, A.R.; Crasta, L.J.COVID-19—A pandemic declared by WHO in 2019 has spread worldwide, leading to many infections and deaths. The disease is fatal, and the patient develops symptoms within 14 days of the window. Diagnosis based on CT scans involves rapid and accurate detection of symptoms, and much work has already been done on segmenting infections in CT scans. However, the existing work on infection segmentation must be more efficient to segment the infection area. Therefore, this work proposes an automatic Deep Learning based model using Transfer Learning and Hierarchical techniques to segment COVID-19 infections. The proposed architecture, Transfer Learning with Hierarchical Segmentation Network (TLH-Net), comprises two encoder–decoder architectures connected in series. The encoder–decoder architecture is similar to the U-Net except for the modified 2D convolutional block, attention block and spectral pooling. In TLH-Net, the first part segments the lung contour from the CT scan slices, and the second part generates the infection mask from the lung contour maps. The model trains with the loss function TV_bin, penalizing False-Negative and False-Positive predictions. The model achieves a Dice Coefficient of 98.87% for Lung Segmentation and 86% for Infection Segmentation. The model was also tested with the unseen dataset and has achieved a 56% Dice value. © The Author(s), under exclusive licence to The Japanese Society for Artificial Intelligence and Springer Nature Japan KK, part of Springer Nature 2024.Item KAC SegNet: A Novel Kernel-Based Active Contour Method for Lung Nodule Segmentation and Classification Using Dense AlexNet Framework(World Scientific, 2024) Dodia, S.; Annappa, B.; Mahesh, P.A.Lung cancer is known to be one of the leading causes of death worldwide. There is a chance of increasing the survival rate of the patients if detected at an early stage. Computed Tomography (CT) scans are prominently used to detect and classify lung cancer nodules/tumors in the thoracic region. There is a need to develop an efficient and reliable computer-aided diagnosis model to detect lung cancer nodules accurately from CT scans. This work proposes a novel kernel-based active-contour (KAC) SegNet deep learning model to perform lung cancer nodule detection from CT scans. The active contour uses a snake method to detect internal and external boundaries of the curves, which is used to extract the Region Of Interest (ROI) from the CT scan. From the extracted ROI, the nodules are further classified into benign and malignant using a Dense AlexNet deep learning model. The key contributions of this work are the fusion of an edge detection method with a deep learning segmentation method which provides enhanced lung nodule segmentation performance, and an ensemble of state-of-the-art deep learning classifiers, which encashes the advantages of both DenseNet and AlexNet to learn better discriminative information from the detected lung nodules. The experimental outcome shows that the proposed segmentation approach achieves a Dice Score Coefficient of 97.8% and an Intersection-over-Union of 92.96%. The classification performance resulted in an accuracy of 95.65%, a False Positive Rate, and False Negative Rate values of 0.0572 and 0.0289. The proposed model is robust compared to the existing state-of-the-art methods. © 2024 World Scientific Publishing Company.Item Feasibility study of a 4-DOF cable-driven exosuit for elbow and wrist rehabilitation(Springer Science and Business Media B.V., 2025) Alapati, S.; Seth, D.; Nakka, S.; Aoustin, Y.Cable-driven exosuits are emerging as an effective solution for upper limb rehabilitation, offering lightweight, flexible, and customizable assistance. While prior research for upper limb rehabilitation has primarily focused on single-joint actuation or systems with up to 2 degrees of freedom (DOF), the combined actuation of the elbow and wrist, including forearm pronation-supination, within an exosuit remains largely unexplored. Addressing this gap, this study investigates the feasibility of a 4-DOF cable-driven exosuit designed to assist these joints, which are essential for rehabilitation exercises and activities of daily living (ADLs) such as eating and object manipulation. A torque-tension model is developed to establish the relationship between joint torques and cable tensions. An optimization framework is implemented to determine optimal tension values. Motion capture data from six healthy subjects performing rehabilitation and daily tasks are used to analyze tension variations and validate the model. The results confirm that the proposed exosuit can provide effective assistance for the elbow and wrist while maintaining cable tensions optimized between 5 N to 50 N for ADLs. Furthermore, workspace analysis confirms that the exosuit maintains 100% feasibility across the rehabilitation range of motion. These insights lay the groundwork for an adaptive, personalized exosuit capable of assisting multi-DOF upper limb movements. © The Author(s), under exclusive licence to Springer Nature B.V. 2025.Item Development, optimization, and prototyping of a simplified sit-stand mechanism for lower limb impairments(Springer Science and Business Media Deutschland GmbH, 2025) George, S.P.; Thomas, M.J.; Mathew, M.; Gangadharan, N.; Varghese, A.K.A sit-stand device for rehabilitation should be simple in its design, easy to manufacture, and convenient for individuals with mobility impairments to use. This paper proposes a design framework and prototyping process for developing an assisted sit-to-stand mechanism tailored to the specific limitations faced by individuals with lower limb impairments. The study incorporates a functional kinematic and kinetic design to ensure the mechanism’s usability across a diverse range of individuals. Recognizing the critical challenges faced by individuals with spinal cord injuries (SCI) and subsequent paralysis, the design philosophy integrates considerations specifically aimed at this population. A simplified circular design trajectory is presented for individuals with muscle paralysis, focusing on the synthesis of an electrically actuated mechanism. A four-bar linkage is modeled to represent the mechanism in the sagittal plane. The functional attributes of the device are determined, and kinematic synthesis is performed to ensure comfort during the sit-to-stand motion. This is achieved by minimizing the actuator’s travel distance during the lift. The velocity and acceleration profiles of the linear actuator are determined after applying boundary conditions. An optimal configuration is selected based on minimizing the displacement of the electric actuator. A human body model based on a 50th percentile male was developed to simulate a motion study of the sit-stand and validate the trajectory using the motion study module in SOLIDWORKS™. An optimum sit-to-stand linkage design was synthesized, and the corresponding prototype was fabricated. The independent anthropometric dimensions on which the design depends are the thigh length and the weight. The sagittal linkages for lifting were calculated and tested through simulation with a human body model to replicate the sit-to-stand movement. The prototype was evaluated on an able-bodied individual. A key design feature was the repositioning of support from the armpit to the hip, thereby reducing user discomfort and improving ergonomics. The motion study revealed that the trajectory of the hip joint (H-point) followed a nearly circular curvature. Stability analysis using a mannequin confirmed a static stability margin of 1 and showed that the device would tip forward only if the deceleration exceeded 35.8 m/s2, which is significantly higher than typical human-induced accelerations—indicating safe operation during use. The prototype fabricated demonstrated the intended sit-to-stand functionality and validated the design approach. The motion analysis confirmed ergonomic hip support and smooth joint trajectories. While the initial testing was successful on an able-bodied subject, further evaluation involving individuals with spinal cord injuries is recommended for final adjustments. This work presents a cost-effective and customizable framework for manufacturing sit-to-stand assistive devices, scalable for variations in body weight and thigh length. © International Federation for Medical and Biological Engineering 2025.