Diagnosing and categorizing of pulmonary diseases using Deep learning conventional Neural network

N. Sudhir Reddy; V. Khanaa

doi:10.52756/10.52756/ijerr.2023.v31spl.002

N. Sudhir Reddy Department of Computer Science and Engineering, Bharath Institute of Higher Education and Research (BIHER), Chennai, Tamil Nadu, India https://orcid.org/0009-0004-1132-6863
V. Khanaa Department of Computer Science and Engineering, Bharath Institute of Higher Education and Research (BIHER), Chennai, Tamil Nadu, India https://orcid.org/0000-0002-2509-1549

DOI: https://doi.org/10.52756/10.52756/ijerr.2023.v31spl.002

Keywords: Hybrid fuzzy morphological, region of interest, NLTF, laplacian pyramid decomposition

Abstract

Lung cancer is one of the major illnesses that contribute to millions of fatalities worldwide. Numerous deaths could be saved through the early identification and categorization of lung cancers. However, with traditional approaches, classification accuracy cannot be produced. To detect and classify lung diseases, a deep learning convolutional neural network model has been developed. LDDC, the customized local trilateral filter, is used for pre-processing the lung images from computing tomography for non-local trilateral filters. The region of interest for lung cancer was successfully restricted throughout the segmentation of the disease using hybrid fuzzy morphological procedures. To extract the deep seismic features, the Laplacian pyramid decomposition method was utilized for the segmented image. This paper covers an overall analysis of non-local trilateral filter Processing, hybrid fuzzy morphological techniques and analysis of patient and disease characteristics of LIDR- IDRI and FDA data of Group A (no co-AGA), P-value, Multi-mut Patient, Group B (with a co-AGA).

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