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Title: Detección temprana de cáncer de piel mediante clasificador de imágenes basado en Inteligencia Artificial
Author: Martín Tinaquero, David
Tutor: Solé-Ribalta, Albert  
Others: Sanchez-Bocanegra, Carlos Luis  
Fernandez-Luque, Luis  
Abstract: Finding skin cancer early often opens up more treatment options. Signs and symptoms do occur in some cases of early-stage disease, but this is not always the case. Currently, the most widespread methods to detect cancer in its early stages are self-examination and examination by a specialist doctor (dermatologist). The first method has the drawback that most citizens do not know how to distinguish a benign skin lesion from a cancerous one. The main drawback of the second alternative is the long waiting times to be seen by a dermatologist, since they are among the three most in-demand specialists, surpassed only by traumatologists and ophthalmologists. The objective of this work is to design and implement an early diagnosis tool that can be used in clinical practice as a support tool for the doctor, acting as a diagnostic assistant to optimize times, and also help people without dermatology knowledge by acting as an advisor for the detection of cancerous tumors during the self-examination of the skin. The work investigates the feasibility of using deep learning, specifically, convolutional and transformer neural networks, for the classification of skin lesions from images.
Keywords: skin cancer
artificial intelligence
deep learning
Document type: info:eu-repo/semantics/masterThesis
Issue Date: 15-Jan-2023
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Appears in Collections:Bachelor thesis, research projects, etc.

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