ArticleJournal of medical systems2019
Lung Nodule Detection based on Ensemble of Hand Crafted and Deep Features.
Article in Journal of medical systems, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Lung Cancer Classification Using Effective Fusion Network Integrating Transformers and Controllable Convolutional Encoders-Decoders.Journal of imaging informatics in medicine · 2026Article
- Clinicoradiologic features and evolutionary characteristics of pulmonary focal mucinous adenocarcinomas across different density patterns: a retrospective multi-center study.Quantitative imaging in medicine and surgery · 2026Article
- Optimizing lung cancer diagnosis using improved fungal growth optimizer-based medical image segmentation.Scientific reports · 2026Article
- Multi-scale detection of pulmonary nodules by integrating attention mechanism.Scientific reports · 2023Article
- An Explainable AI-Enabled Framework for Interpreting Pulmonary Diseases from Chest Radiographs.Cancers · 2023Article
- Research on lung nodule recognition algorithm based on deep feature fusion and MKL-SVM-IPSO.Scientific reports · 2022Article
- Radiomics: an overview in lung cancer management-a narrative review.Annals of translational medicine · 2020Review
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Authors and funding
5 authors.
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Abstract
Lung cancer is considered as a deadliest disease worldwide due to which 1.76 million deaths occurred in the year 2018. Keeping in view its dreadful effect on humans, cancer detection at a premature stage is a more significant requirement to reduce the probability of mortality rate. This manuscript depicts an approach of finding lung nodule at an initial stage that comprises of three major phases: (1) lung nodule segmentation using Otsu threshold followed by morphological operation; (2) extraction of geometrical, texture and deep learning features for selecting optimal features; (3) The optimal features are fused serially for classification of lung nodule into two categories that is malignant and benign. The lung image database consortium image database resource initiative (LIDC-IDRI) is used for experimentation. The experimental outcomes show better performance of presented approach as compared with the existing methods.
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