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Author: search.filters.author.Annappa, A.Subject: search.filters.subject.Nodule classification

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    Machine learning-based detection and classification of lung cancer
    (Elsevier, 2022) Dodia, S.; Annappa, A.
    Cancer is termed to be one of the life-threatening diseases in the world. Among various types of cancer, the highest mortality and morbidity rate recorded is from lung cancer. Computer-aided diagnosis (CAD) systems are used to identify lung cancer nodules. The development of reliable automated algorithms is important to provide doctors with a second opinion. A lung cancer diagnosis is performed in two steps: lung cancer nodule detection and classification. In nodule detection, from a given computed tomography (CT) scan, the nodules and nonnodules are identified. Once the nodules and nonnodules are identified, the next step is to classify the detected nodules as cancerous and noncancerous. This work explores various machine learning classifiers for lung cancer classification. A majority voting scheme is used to classify nodules. An in-depth analysis of different machine learning algorithms’ performance is presented in this work. © 2023 Elsevier Inc. All rights reserved.
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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. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.