Navegando por Assunto "Melanoma"

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Uma abordagem para o suporte ao diagnóstico de melanoma por imagem via comitês de autoencoders
(2021-12-17) Silva, Evele Kelle Lemos; Soares, Rodrigo Gabriel Ferreira; http://lattes.cnpq.br/2526739219416964
Skin cancer is the most common type of cancer in Brazil, representing about 33% of cases of malignant neoplasms in the country. Melanoma is a type of skin cancer that represents only 3% of cancer cases in the organ, but it is considered the most offensive due to high possibility of metastasis, which is the spread of cancer to other organs. Although melanoma is considered the main fatal skin disease, the introduction of new drugs combined with the detection of the tumor in early stages have contributed to positive prognosis. Through the ABCDEs rule of melanoma, it is possible to identify melanoma by watching some characteristics present in the lesion, however, the identification of melanoma through observation is often a failure, especially when it comes from an inexperienced doctor. Therefore, this work aims to select and use Machine Learning techniques to propose a model that can help dermatologists to identify skin lesions through dermoscopic images, serving as a second opinion to say if it is or it is not melanoma. The proposed model was compared with techniques widely used in the literature for solving complex problems, with the objective of presenting superior performance. Using Precision and Recall, the proposed model proved to be comparable to the others, although it had access to only 0,1% of the dimensions of the image, which indicates that the model worked well on finding the characteristics that discriminate skin lesions.
Melanoma amelanótico oral em um cão da raça Golden Retriever - Relato de caso
(2022-09-30) Rodrigues, Beto Cherles Coral; Silva, Elayne Cristine Soares da; http://lattes.cnpq.br/2064359935012193; http://lattes.cnpq.br/9837284907050552
The Mandatory Supervised Internship (ESO) is a mandatory subject of the Bachelor's Degree in Veterinary Medicine at the Federal Rural University of Pernambuco (UFRPE), which aims to offer the student practical experience and professional improvement. The experience was held from June 26 to September 9, 2022, at the Clínica Oncologica Veterinaria do Recife (CORe), located at Rua Antônio Viera, 245, Madalena, Recife/PE. This report is divided into two chapters. The first describes the activities followed by the student Beto Cherles Coral Rodrigues during the ESO and the second chapter aimed to report the case of a Golden Retriever dog with oral amelanotic melanoma, also followed during the ESO period. . The choice of this case was due to the importance of immunohistochemistry in the conclusive diagnosis of this melanoma. During this period of ESO, it was possible to know a little about the clinical and surgical routine in veterinary oncology; work multidisciplinary with specialists involved in cancer cases; and gain experience in approaching patients and their tutors.
Técnicas preditivas para auxílio no diagnóstico de melanomas via imagens
(2024-10-02) Silva Júnior, José Carlos Monte; Soares, Rodrigo Gabriel Ferreira; http://lattes.cnpq.br/2526739219416964
Skin cancer is the most common type of cancer worldwide, divided into two main types: melanoma and non-melanoma. Although rarer, melanoma is the most lethal due to its potential to cause metastasis. Non-invasive methods, such as dermoscopy and the ABCDE rule, have been used to avoid unnecessary surgical procedures and have helped in the identification of lesions, contributing to faster diagnoses. With advances in technology, Artificial Intelligence (AI) has gained prominence, proving to be a promising solution for medical data analysis, especially with the use of Convolutional Neural Networks (CNNs), which can recognize patterns in dermoscopic images and help classify lesions as melanoma or non-melanoma in an automated manner. This project proposes an ensemble of classifiers based on Convolutional Neural Networks to classify dermoscopic images as melanoma or non-melanoma, comparing its performance with validated architectures, such as AlexNet and VGG-16, using Transfer Learning techniques The analyses of Precision, Recall, and F1 Score showed that the ensemble of Convolutional Neural Networks outperformed the models using Transfer Learning techniques, with AlexNet showing better performance than VGG-16. The ensemble of Convolutional Neural Networks demonstrated a greater generalization capability, proving to be promising in capturing relevant features from the images, revealing potential for medical applications, although it still needs refinement to meet clinical standards.