Conference Papers
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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.