Segmentation and classification of multimodal medical images based on generative adversarial learning and convolutional neural networks

Identificador:  TDX:3013

Handle: https://hdl.handle.net/20.500.11797/TDX3013

Autores:  Singh, Vivek Kumar

Resumen:
The main aim of this thesis is to create an advanced CAD system for any type of medical image modality with high sensitivity and specificity rates based on deep learning techniques. More specifically, we want to improve the automatic method of detection of Regions of Interest (ROI), which are areas of the image that contain possible ill tissues, as well as segmentation of the findings (delimitation with a boundary), and ultimately, a prediction of a most suitable diagnose (classification). In this thesis, we focus on several topics including mammograms and ultrasound images to diagnose breast cancer, skin lesions analysis in dermoscopic images and retinal fundus images examination to avoid diabetic retinopathy.