Browsing by Author "Mahesh, M.A."

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A Novel Artificial Intelligence-Based Lung Nodule Segmentation and Classification System on CT Scans
(Springer Science and Business Media Deutschland GmbH, 2022) Dodia, S.; Annappa, A.; Mahesh, M.A.
Major innovations in deep neural networks have helped optimize the functionality of tasks such as detection, classification, segmentation, etc., in medical imaging. Although Computer-Aided Diagnosis (CAD) systems created using classic deep architectures have significantly improved performance, the pipeline operation remains unclear. In this work, in comparison to the state-of-the-art deep learning architectures, we developed a novel pipeline for performing lung nodule detection and classification, resulting in fewer parameters, better analysis, and improved performance. Histogram equalization, an image enhancement technique, is used as an initial preprocessing step to improve the contrast of the lung CT scans. A novel Elagha initialization-based Fuzzy C-Means clustering (EFCM) is introduced in this work to perform nodule segmentation from the preprocessed CT scan. Following this, Convolutional Neural Network (CNN) is used for feature extraction to perform nodule classification instead of customary classification. Another set of features considered in this work is Bag-of-Visual-Words (BoVW). These features are encoded representations of the detected nodule images. This work also examines a blend of intermediate features extracted from CNN and BoVW characteristics, which resulted in higher performance than individual feature representation. A Support Vector Machine (SVM) is used to distinguish detected nodules into benign and malignant nodules. Achieved results clearly show improvement in the nodule detection and classification task performance compared to the state-of-the-art architectures. The model is evaluated on the popular publicly available LUNA16 dataset and verified by an expert pulmonologist. Â© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
A Novel Bi-level Lung Cancer Classification System onÂ CT Scans
(Springer Science and Business Media Deutschland GmbH, 2022) Dodia, S.; Annappa, A.; Mahesh, M.A.
Purpose: Lung cancer is a life-threatening disease that affects both men and women. Accurate identification of lung cancer has been a challenging task for decades. The aim of this work is to perform a bi-level classification of lung cancer nodules. In Level-1, candidates are classified into nodules and non-nodules, and in Level-2, the detected nodules are further classified into benign and malignant. Methods: A new preprocessing method, named, Boosted Bilateral Histogram Equalization (BBHE) is applied to the input scans prior to feeding the input to the neural networks. A novel Cauchy Black Widow Optimization-based Convolutional Neural Network (CBWO-CNN) is introduced for Level-1 classification. The weight updation in the CBWO-CNN is performed using Cauchy mutation, and the error rate is minimized, which in turn improved the accuracy with less computation time. A novel hybrid Convolutional Neural Network (CNN) model with shared parameters is introduced for performing Level-2 classification. The second model proposed in this work is a fusion of Squeeze-and-Excitation Network (SE-Net) and Xception, abbreviated as â€œSE-Xceptionâ€ . The weight parameters are shared for the SE-Xception model trained from CBWO-CNN, i.e., a knowledge transfer approach is adapted. Results: The recognition accuracy obtained from CBWO-CNN for Level-1 classification is 96.37% with a reduced False Positive Rate (FPR) of 0.033. SE-Xception model achieved a sensitivity of 96.14%, an accuracy of 94.75%, and a specificity of 92.83%, respectively, for Level-2 classification. Conclusion: The proposed methodâ€™s performance is better than existing deep learning architectures and outperformed individual SE-Net and Xception with fewer parameters. Â© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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
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. Â© 2023 IEEE.
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. Â© 2023 IEEE.