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Development and Validation of a Deep Learning Model for Histopathological Slide Analysis in Lung Cancer Diagnosis.
- Source :
- Cancers; Apr2024, Vol. 16 Issue 8, p1506, 11p
- Publication Year :
- 2024
-
Abstract
- Simple Summary: A prolonged diagnosis of lung cancer can hinder effective treatment processes for cancer patients. Artificial intelligence-based models significantly impact the healthcare system; deep-learning algorithms in the diagnostic process can save time and money and provide high-accuracy results that accelerate and improve the treatment journey. Lung cancer is the leading cause of cancer-related deaths worldwide. Two of the crucial factors contributing to these fatalities are delayed diagnosis and suboptimal prognosis. The rapid advancement of deep learning (DL) approaches provides a significant opportunity for medical imaging techniques to play a pivotal role in the early detection of lung tumors and subsequent monitoring during treatment. This study presents a DL-based model for efficient lung cancer detection using whole-slide images. Our methodology combines convolutional neural networks (CNNs) and separable CNNs with residual blocks, thereby improving classification performance. Our model improves accuracy (96% to 98%) and robustness in distinguishing between cancerous and non-cancerous lung cell images in less than 10 s. Moreover, the model's overall performance surpassed that of active pathologists, with an accuracy of 100% vs. 79%. There was a significant linear correlation between pathologists' accuracy and years of experience (r Pearson = 0.71, 95% CI 0.14 to 0.93, p = 0.022). We conclude that this model enhances the accuracy of cancer detection and can be used to train junior pathologists. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 20726694
- Volume :
- 16
- Issue :
- 8
- Database :
- Complementary Index
- Journal :
- Cancers
- Publication Type :
- Academic Journal
- Accession number :
- 176876922
- Full Text :
- https://doi.org/10.3390/cancers16081506