Excerpt
Table of Contents
Acknowledgments
Abstract (English)
Abstract (Résumé en Frangais)
XLIM Laboratory and Internship Presentations
1. Introduction
2. Related Work
3. Background
3.1 Deep Learning in Medical Diagnosis
3.2 Deep Learning architectures
3.3 Convolutional Neural Networks (CNNs)
3.3.1 Convolution Layer (C1, C3, and C5)
3.3.2 Pooling Layer (S2 and S4)
3.3.3 FCL (F6)
3.4 Transfer Learning (TL)
3.5 ISIC Dataset
3.5.1 Remarks of detecting a melanoma:
4. Approach
4.1 Pre-processing
4.1.1 Image Resizing
4.1.2 Data augmentation
4.1.2 Data split
4.2 Algorithm
4.2.1 VGG16 LL (Last layer fine-tuning)
4.2.2 VGG16 FL (First layer fine-tuning)
4.2.3 VGG16 ML (Middle layers fine-tuning)
4.2.4 VGG16 FU (Full layers fine-tuning)
4.3 Evaluation Metrics
1) Accuracy (A)
2) Log Loss (LL)
3) Confusion matrix (CM)
5. Experiments and Results
6. Conclusion
6.1 Future Works
6.2 Challenges
References
- Quote paper
- Marwan Al Omari (Author), 2021, Use of CNNs for the Classification of Medical Images, Munich, GRIN Verlag, https://www.grin.com/document/1215084
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