Pneumonia Detection through Deep Learning: A Comparative Exploration of Classification and Segmentation Strategies
Journal Title: International Journal of Experimental Research and Review - Year 2024, Vol 40, Issue 4
Abstract
The Convolution Neural Network (CNN) algorithm is one of the most widely used methods for identifying and categorizing lung cancer. This paper covers the most suitable architecture and CNN algorithms for lung cancer and pneumonia deduction and classification. The main contributions to the diagnosis and classification of lung cancer with four steps are Nonlinear transfer learning framework (NLTF), Hierarchical Feature Mapping (HFM), Lifelong Partial Dissection (LPD), and Deep Lifelong Convolutional Neural Network (DLCNN). The application of non-local total fuzzy (NLTF) filtering removes various categories of noise after lung CT imageries and enhances cancer areas. The application of Hybrid Fuzzy Morphology (HFM) constructed segmentation to minimize the region of interest (ROI) for cancer using morphology opening and closing processes. Extraction of traits unique to each disease employing Lung Parenchyma Division (LPD) and extraction of deep seismic features using the Geometric Optimal Algorithm (GOA). Training and testing the proposed Deep Learning Convolutional Neural Network (DLCNN) model using the extracted features to classify benign, malignant lung cancers and Recent advancements in deep learning methods have shown accurate results in the investigation and diagnosis of medical image data, including the detection of pneumonia.
Authors and Affiliations
Vishnu Kumar Mishra, Megha Mishra, Ashish Kumar Tamrakar, Thurimella Srikanth, Talaisila Ram Kumar and Anoop Kumar
Keywords
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- EP ID EP739922
- DOI 10.52756/ijerr.2024.v40spl.004
- Views 78
- Downloads 1
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