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VGG(CNN)-Based Neural Network Optimization for Breast Ultrasound Classification: Benign, Malignant, and Normal


Zihao Han

04/09/2023

Supervised by Hiroyuki Kido; Moderated by Kirill Sidorov

In recent years, the application of Convolutional Neural Networks (CNNs) has garnered considerable attention in the realm of medical imaging, given their innate prowess in image representation. The goal of this project is to find a specific medical image dataset, implement neural network models, and evaluate their performance. The project will also involve a critical review of existing models to motivate the work and justify the proposed models.This study seeks to explore the application and optimization techniques of neural networks, specifically focusing on the VGG model, to classify breast ultrasound images into benign, malignant, or normal breast tissue categories.


Final Report (04/09/2023) [Zip Archive]

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