Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item Exploring Convolutional Neural Networks for Image Classification and Object Detection(Institute of Electrical and Electronics Engineers Inc., 2024) Sadhankar, D.S.; Illa, M.; Shetty, P.; Kumar, S.V.; Megha, M.K.; Ambilwade, R.P.Convolutional Neural Networks or CNNs are one of the newest powerful tools in various tasks of computer vision such as image classification or object detection providing the highest accuracy. This paper also aims to evaluate the efficiency of the CNNs in these areas using a real-world dataset from Kaggle. We discuss general issues and ways to address it, such as data augmentation, dropout and choose the best/settled value of hyperparameters for improvement of the model. This paper aimed at analyzing the effectiveness of CNN in learning discriminative features from the images and confirm that CNNs are among the most accurate models for image classification. Moreover, this study also provides suggestions for subsequent research work, including improving the CNN architectures, employing transfer learning, and incorporating interpretation methods to continue enhancing the performance of CNNs in computer vision. © 2024 IEEE.Item Utilizing Machine Learning for Lung Disease Diagnosis(Institute of Electrical and Electronics Engineers Inc., 2024) Markose, G.C.; Sitaraman, S.R.; Kumar, S.V.; Patel, V.; Mohammed, R.J.; Vaghela, C.For lung issues to be really treated and made due, early location and analysis are fundamental. In healthcare, machine learning (ML) strategies have arisen as an expected innovation with quick development, particularly in the field of clinical diagnostics. To analyze lung diseases, this research investigates the utilization of machine learning calculations. It centers around picture examination, patient information understanding, and the reconciliation of numerous information hotspots for an intensive investigation. This research's principal objective is to explore the chance of utilizing machine learning calculations to foresee and analyze a scope of lung conditions, including lung malignant growth, bronchitis, asthma, sensitivities, and persistent obstructive pneumonic disease (COPD). Proactive mediation depends on expecting the probability of lung issues before they manifest. Utilizing an assortment of machine learning techniques for classification and expectation, the examination assembled a heterogeneous dataset fully intent on laying the preparation for protection healthcare measures. © 2024 IEEE.Item Deep Learning Models for Classification of Lung Cancer Types from Histopathology Images(Institute of Electrical and Electronics Engineers Inc., 2025) Kumar, S.V.; Swapna, H.; Raghavendra, B.S.the most deadly and often diagnosed cancer is lung cancer, among other types of cancers all over the world. being diagnosed early can save the patient's life and improve their five-year survival rate. In this context, accurately recognizing the types of lung cancer from histopathology images is essential because it helps doctors decide which cancer types need further therapy. In this paper, to identify types of lung cancer from histopathology images, a deep learning framework based on binary and multi-classification approaches has been proposed. The framework utilizes the concept of transfer learning, and the Weighted average ensemble approach is used for the multi-classification model. The performance of the proposed model is examined using the LC25000 dataset, which is made freely available, and compared with the current approaches for classifying cancer types. It is observed that for the binary classification problem, InceptionV3, EfficicientNetB1, and multi-classification using the Weighted average ensemble approach have provided better results. The figures of merit achieved are recall, average accuracy, precision, and F1-score of 98.88%, 98.77%,98.77%, and 98.77%, respectively © 2025 IEEE.