A Review on Deep Learning Method for Lung Cancer Stage Classification Using PET‐CT

Das, Kaushik; Chandra, Joydeep; M, Dr Nachamai

doi:10.1002/9781119743187.ch2

Cited by 1 publication

(1 citation statement)

References 15 publications

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“…However, these reviews are very specific and focus on single diseases like pneumonia (Khan et al. 2021 ), lung cancer (Pratim and Nachamai 2022 ), tuberculosis (Oloko-Oba and Viriri 2022 ), pulmonary nodule detection (Li et al. 2022 ), and COVID19 detection (Chen et al.…”

Section: Introductionmentioning

confidence: 99%

Review on chest pathogies detection systems using deep learning techniques

et al. 2023

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Chest radiography is the standard and most affordable way to diagnose, analyze, and examine different thoracic and chest diseases. Typically, the radiograph is examined by an expert radiologist or physician to decide about a particular anomaly, if exists. Moreover, computer-aided methods are used to assist radiologists and make the analysis process accurate, fast, and more automated. A tremendous improvement in automatic chest pathologies detection and analysis can be observed with the emergence of deep learning. The survey aims to review, technically evaluate, and synthesize the different computer-aided chest pathologies detection systems. The state-of-the-art of single and multi-pathologies detection systems, which are published in the last five years, are thoroughly discussed. The taxonomy of image acquisition, dataset preprocessing, feature extraction, and deep learning models are presented. The mathematical concepts related to feature extraction model architectures are discussed. Moreover, the different articles are compared based on their contributions, datasets, methods used, and the results achieved. The article ends with the main findings, current trends, challenges, and future recommendations.

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