ArticleScientific reports2019
Effective and Reliable Framework for Lung Nodules Detection from CT Scan Images.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- ROI extraction in corona virus (COVID 19) CT images using intuitionistic fuzzy edge detection.Heliyon · 2024Article
- Diagnostic Accuracy of Machine Learning AI Architectures in Detection and Classification of Lung Cancer: A Systematic Review.Diagnostics (Basel, Switzerland) · 2023Review
- A radiomics approach for lung nodule detection in thoracic CT images based on the dynamic patterns of morphological variation.European radiology · 2022Article
- A Novel Pulmonary Nodule Detection Model Based on Multi-Step Cascaded Networks.Sensors (Basel, Switzerland) · 2020Article
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4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Lung cancer is considered more serious among other prevailing cancer types. One of the reasons for it is that it is usually not diagnosed until it has spread and by that time it becomes very difficult to treat. Early detection of lung cancer can significantly increase the chances of survival of a cancer patient. An effective nodule detection system can play a key role in early detection of lung cancer thus increasing the chances of successful treatment. In this research work, we have proposed a novel classification framework for nodule classification. The framework consists of multiple phases that include image contrast enhancement, segmentation, optimal feature extraction, followed by employment of these features for training and testing of Support Vector Machine. We have empirically tested the efficacy of our technique by utilizing the well-known Lung Image Consortium Database (LIDC) dataset. The empirical results suggest that the technique is highly effective for reducing the false positive rates. We were able to receive an impressive sensitivity rate of 97.45%.
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