Classification of scleroderma patterns using deep learning
DOI:
https://doi.org/10.59681/2175-4411.v16.iEspecial.2024.1300Keywords:
Scleroderma, Classification, Deep LearningAbstract
Objective: Scleroderma is a disease whose cause is unknown and which results in the stiffening of the skin and internal organs. This study aims to develop deep learning models to help doctors assess the progression and mortality of the disease. Method: PIU and PINE pattern classification models were developed with the MobileNetV2, VGG16, ResNet50 and EfficientNet architecture for computerized tomography images of patients with scleroderma. Results: All the models achieved 100% accuracy in the training, validation and test sets and, therefore, it was possible to differentiate the patterns presented in the computerized tomography images of patients who were at the Pedro Ernesto Hospital between 2017 and 2022. Conclusion: Among the models evaluated, MobileNetV2 is the best because it has the fewest parameters of all the architectures evaluated in this study.
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