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| Titre: | A Comparative Analysis of Deep Learning and Traditional Machine Learning Approaches for Lung Cancer Detection |
| Auteur(s): | Amarouche, Yacine
Kouachi, Ishak |
| Mots-clés: | Lung cancer
Histopathology
Machine Learning
Deep Learning
Convolutional Neural Networks |
| Date de publication: | 2025 |
| Résumé: | In this work, a hybrid method is proposed for lung cancer detection using
histopathological images from the LC25000 dataset. This method adopts
MobileNetV3Small for efficient deep feature extraction, Principal Component
Analysis (PCA) for dimensionality reduction, and a Support Vector
Machine (SVM) for classification. The model achieved a high accuracy of
99.87%, along with excellent precision, recall, and F1-score values. Due
to its low execution time, the proposed method is particularly well suited
for real-world clinical applications. This research contributes to the field of
medical diagnosis supported by advanced artificial intelligence technologies
by offering a powerful tool for accurate lung cancer detection. |
| URI/URL: | http://dspace.univ-setif.dz:8888/jspui/handle/123456789/5583 |
| Collection(s) : | Mémoires de master
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