This research project structures different enhanced architectures and models of CNNs using in particular the VGG16 model, for its featured simplicity and efficiency along with its pre-trained wights on ImageNet. The VGG16 models are well trained using transfer learning mechanism in fine-tuning the architecture on the ISIC2018 Task3 dataset. Then, the models are projected for skin cancer image classification in highlighting the state-of-the-art performance.
Deep learning models have showed great capabilities in data modelling on the various applications of image processing, including segmentation, classification, tagging, and many others. In particular, convolutional neural network (CNNs) has proved to be effective in capturing deep features on unstructured data that are well sited in the state-of-the-art. It is well competitive in comparison to the traditional algorithms of machine learning.
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
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