Advanced Deep Learning Techniques for Accurate Lung Cancer Detection and Classification
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866908482441052160 |
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| author | Abumohsen, Mobarak Costa-Montenegro, Enrique García-Méndez, Silvia Owda, Amani Yousef Owda, Majdi |
| author_facet | Abumohsen, Mobarak Costa-Montenegro, Enrique García-Méndez, Silvia Owda, Amani Yousef Owda, Majdi |
| contents | Lung cancer (LC) ranks among the most frequently diagnosed cancers and is one of the most common causes of death for men and women worldwide. Computed Tomography (CT) images are the most preferred diagnosis method because of their low cost and their faster processing times. Many researchers have proposed various ways of identifying lung cancer using CT images. However, such techniques suffer from significant false positives, leading to low accuracy. The fundamental reason results from employing a small and imbalanced dataset. This paper introduces an innovative approach for LC detection and classification from CT images based on the DenseNet201 model. Our approach comprises several advanced methods such as Focal Loss, data augmentation, and regularization to overcome the imbalanced data issue and overfitting challenge. The findings show the appropriateness of the proposal, attaining a promising performance of 98.95% accuracy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_06287 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Advanced Deep Learning Techniques for Accurate Lung Cancer Detection and Classification Abumohsen, Mobarak Costa-Montenegro, Enrique García-Méndez, Silvia Owda, Amani Yousef Owda, Majdi Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition Lung cancer (LC) ranks among the most frequently diagnosed cancers and is one of the most common causes of death for men and women worldwide. Computed Tomography (CT) images are the most preferred diagnosis method because of their low cost and their faster processing times. Many researchers have proposed various ways of identifying lung cancer using CT images. However, such techniques suffer from significant false positives, leading to low accuracy. The fundamental reason results from employing a small and imbalanced dataset. This paper introduces an innovative approach for LC detection and classification from CT images based on the DenseNet201 model. Our approach comprises several advanced methods such as Focal Loss, data augmentation, and regularization to overcome the imbalanced data issue and overfitting challenge. The findings show the appropriateness of the proposal, attaining a promising performance of 98.95% accuracy. |
| title | Advanced Deep Learning Techniques for Accurate Lung Cancer Detection and Classification |
| topic | Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2508.06287 |