Banca de QUALIFICAÇÃO: PATRÍCIA ALVES GENUINO

Uma banca de QUALIFICAÇÃO de MESTRADO foi cadastrada pelo programa.
STUDENT : PATRÍCIA ALVES GENUINO
DATE: 20/12/2023
LOCAL: Remota
TITLE:

INTELLIGENT SYSTEM TO SUPPORT THE DIAGNOSIS OF LEPROSY USING DIGITAL IMAGE ANALYSIS AND DEEP ARTIFICIAL NEURAL NETWORKS

KEY WORDS:

Diagnostic support system, Leprosy, Skin cancer, Psoriasis, Psoriatic arthritis, Deep artificial neural networks.

PAGES: 56
BIG AREA: Engenharias
AREA: Engenharia Biomédica
SUMMARY:

The use of artificial intelligence, especially deep learning combined with classification algorithms, has shown significant relevance in the early diagnosis of dermatological diseases. These advanced techniques allow the analysis of large data sets, including photographic images, with the aim of identifying specific patterns and characteristics that can aid in diagnosis in a non-invasive way. As defined by the World Health Organization, neglected diseases are those endemic in underdeveloped regions that do not receive adequate investment due to lack of interest. Leprosy is considered one of these diseases, and Brazil is one of the countries with a high incidence. Early diagnosis of leprosy is essential to interrupt the transmission of the disease and prevent complications. Psoriasis and psoriatic arthritis are related diseases that affect the skin and joints. Early detection of psoriasis in patients with psoriatic arthritis is important for better clinical outcomes and long-term quality of life. Skin cancer is a disease that originates in skin cells and is the most common type of cancer worldwide. world. There are several types of skin cancer, the most prevalent being melanoma, basal cell carcinoma and squamous cell carcinoma. The objective of this work is to contribute to the early detection of leprosy by creating a diagnostic support structure that uses deep artificial neural networks and classification algorithms. The proposal is to develop a system capable of recognizing skin lesions in images that present these characteristic lesions. Advanced artificial intelligence techniques will be applied, such as deep learning, which allows the processing and extraction of complex information from large data sets. Deep artificial neural networks will be trained to identify specific patterns and characteristics present in images of skin lesions related to these diseases. Based on the attributes extracted by the neural networks, a classification algorithm will be used to determine whether the analyzed image indicates the presence of the leprosy, skin cancer, psoriasis or psoriatic arthritis. This diagnostic support structure aims to assist healthcare professionals in early diagnosis, providing more effective treatment and reducing complications. The use of advanced artificial intelligence techniques in the early diagnosis of dermatological diseases offers promising opportunities to improve clinical outcomes.

COMMITTEE MEMBERS:
Interno - 1807632 - WELLINGTON PINHEIRO DOS SANTOS
Externa ao Programa - 2727505 - GISELLE MACHADO MAGALHAES MORENO - UFPEExterna à Instituição - JULIANA CARNEIRO GOMES - UFPE