Faculty Publications
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Item Assessment of Asthma BAL Cytokines using Machine Learning Techniques(Institute of Electrical and Electronics Engineers Inc., 2023) Naik, P.P.; Mahesh, M.A.; Rajan, J.Asthma is a chronic respiratory disorder characterised by airway inflammation and constriction, leading to difficulty in breathing and recurrent attacks of wheezing, coughing, and shortness of breath. In asthma, various cytokines, including interleukins (IL-4, IL-5, and IL-13) and tumor necrosis factoralpha (TNF-alpha), have been found to be increased in the airways of individuals. These cytokines are involved in the recruitment and activation of immune cells, such as eosinophils and T-lymphocytes, which contribute to the inflammation and airway hyperresponsiveness. Dysregulation of cytokine production and signaling has been implicated in the pathogenesis of asthma and may be targeted by therapies to alleviate symptoms and improve outcomes in individuals with this disease. We propose a predictive binary and multi-class machine learning model analysis that efficiently classify the asthma and healthy control patients by detecting cytokines in bronchoalveolar lavage (BAL) fluid which achieved better F1-score than existing approaches. © 2023 IEEE.Item An Efficient Deep Transfer Learning Approach for Classification of Skin Cancer Images(Springer Science and Business Media Deutschland GmbH, 2023) Naik, P.P.; Annappa, B.; Dodia, S.Prolonged exposure to the sun for an extended period can likely cause skin cancer, which is an abnormal proliferation of skin cells. The early detection of this illness necessitates the classification of der-matoscopic images, making it an enticing study problem. Deep learning is playing a crucial role in efficient dermoscopic analysis. Modified version of MobileNetV2 is proposed for the classification of skin cancer images in seven classes. The proposed deep learning model employs transfer learning and various data augmentation techniques to more accurately classify skin lesions compared to existing models. To improve the per¬formance of the classifier, data augmentation techniques are performed on “HAM10000" (Human Against Machine) dataset to classify seven dif¬ferent kinds of skin cancer. The proposed model obtained a training accuracy of 96.56% and testing accuracy of 93.11%. Also, it has a lower number of parameters in comparison to existing methods. The aim of the study is to aid dermatologists in the clinic to make more accurate diagnoses of skin lesions and in the early detection of skin cancer. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.Item Attention-Based CRNN Models for Identification of Respiratory Diseases from Lung Sounds(Institute of Electrical and Electronics Engineers Inc., 2023) Sanjana, J.; Naik, P.P.; Mahesh, M.A.; Koolagudi, S.G.; Rajan, J.Respiratory diseases are a major global health concern, with millions of people suffering from disorders such as asthma, bronchitis, chronic obstructive pulmonary disease (COPD), and pneumonia. In recent years, machine learning and other forms of Artificial Intelligence have proven to be useful resources for resolving issues in the medical field. In this study, we examine the diagnostic utility of Convolutional Recurrent Neural Network (CRNN) models for identifying respiratory diseases using digitally recorded lung sounds. We developed two deep learning models to diagnose and classify lung diseases: a binary classification to classify COPD and non-COPD, and a multi-class classification model to classify five lung disorders (COPD, URTI-upper respiratory tract infection, Pneumonia, Bronchiectasis and Bronchiolitis) and healthy conditions. The ICBHI 2017 challenge dataset [1] was used to develop the models. The accuracy of the binary and multiclass classification models was 98.6% and 97.6%, respectively, with ICBHI Scores of 0.9866 and 0.9723. © 2023 IEEE.Item Spectrophotometric determination of platinum(IV) in alloys, complexes, environmental, and pharmaceutical samples using 4-[N,N-(diethyl)amino] benzaldehyde thiosemicarbazone(2010) Naik, P.P.; Karthikeyan, J.; Nityananda Shetty, A.N.4-[N,N-(Diethyl)amino] benzaldehyde thiosemicarbazone (DEABT) is proposed as an analytical reagent for the spectrophotometric determination of platinum(IV). The DEABT forms 1:2 yellow complex with Pt(IV), which is sparingly soluble in water and completely soluble in water-ethanol-DMF medium. The Pt(IV)-DEABT complex shows maximum absorbance at 405 nm. Beer's law is valid up to 7.80 ?g cm-3, and optimum concentration range for the determination of platinum(IV) is 0.48-7.02 ?g cm-3. The molar absorptivity and Sandell's sensitivity of the method are found to be 1.755 × 104 dm3 mol-1 cm-1 and 0.0012 ?g cm-2, respectively. The relative error and coefficient of variation (n=6) for the method does not exceed ±0.43% and 0.35%, respectively. Since the method tolerates a number of metal ions commonly associated with platinum, it can be employed for the determination of platinum in environmental samples, pharmaceutical samples, alloys, catalysts, and complexes. The method is rapid as the Pt(IV)-DEABT complex is soluble in water-ethanol-DMF medium and not requiring any time consuming extraction method for the complex. © 2010 Springer Science+Business Media B.V.Item A rapid extractive spectrophotometric determination of copper(II) in environmental samples, alloys, complexes and pharmaceutical samples using 4-N,N(dimethyl)amino]benzaldehyde thiosemicarbazone(2011) Karthikeyan, J.; Naik, P.P.; Nityananda Shetty, A.N.4-[N,N-(Dimethyl)amino]benzaldehyde thiosemicarbazone (DMABT) is proposed as an analytical reagent for the extractive spectrophotometric determination of copper(II). DMABT forms yellow colored complex with copper(II) in the pH range 4.4-5.4. Beer's law is obeyed in the concentration range up to 4.7 ?g mL -1. The optimum concentration range for minimum photometric error as determined by Ringbom plot method is 1.2-3.8 ?g mL-1. The yellowish Cu(II)-DMABT complex shows a maximum absorbance at 420 nm, with molar absorptivity of 1.72 × 104dm3 mol-1 cm-1 and Sandell's sensitivity of the complex obtained from Beer's data is 0.0036 ?g cm-2. The composition of the Cu(II)-DMABT complex is found to be 1:2 (M/L). The interference of various cations and anions in the method were studied. Thus the method can be employed for the determination of trace amount of copper(II) in water, alloys and other natural samples of significant importance. © 2010 Springer Science+Business Media B.V.Item An automated deep learning pipeline for detecting user errors in spirometry test(Elsevier Ltd, 2024) Bonthada, S.; Pariserum Perumal, S.P.; Naik, P.P.; Mahesh, M.A.; Rajan, J.Spirometer is used as a major diagnostic tool for obstructive airway diseases and a monitoring tool for therapy response and disease staging over time. It is a sophisticated medical device employed to quantify flow and volume of air exhaled by a subject during a specific testing period. The essential metrics obtained from the spirometry test, play a crucial role in enabling healthcare professionals to thoroughly evaluate the respiratory health and condition of the individual under examination. Several spirometer measurements including Forced Vital Capacity (FVC) and Forced Expiratory Volume (FEV) serve as guidelines for diagnosis and prognosis of Chronic Obstructive Pulmonary Diseases (COPD) and asthma. However, user errors caused by different reasons, including improper handling of the equipment and poor performance during the maneuvers of the expiratory airflow, end up in incorrect treatment directions. To ensure accurate results, spirometry tests traditionally require the presence of a skilled professional to identify and address these errors promptly. A novel machine learning approach is proposed in this paper to automatically identify four such user errors based on Volume-Time and Flow-Volume graphs. By detecting specific errors and providing immediate feedback to patients, reliability and accuracy of spirometry results will be improved and the need for trained professionals will be reduced. The implementation facilitates the widespread adoption of spirometry, particularly in low-resource telemedicine settings. This work implements a binary classification model distinguishing between normal and error test samples, achieving a prediction accuracy of 93%. Additionally, a 4-way classification model is presented for identifying individual error sub-types, demonstrating a prediction accuracy of 94%. © 2023 Elsevier Ltd