Browsing by Author "Silva, Giuliana Martins"
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- An Evaluation of Image Preprocessing in Skin Lesions DetectionPublication . Silva, Giuliana Martins; Lazzaretti, André E.; Monteiro, Fernando C.This study aims to evaluate the impact of image preprocessing techniques on the performance of Convolutional Neural Networks (CNNs) in the task of skin lesion classification. The study is made on the ISIC 2017 dataset, a widely used resource in skin cancer diagnosis research. Thirteen popular CNN models were trained using transfer learning. An ensemble strategy was also employed to generate a final diagnosis based on the classifications of different models. The results indicate that image preprocessing can significantly enhance the performance of CNN models in skin lesion classification tasks. Our best model obtained a balanced accuracy of 0.7879.
- Deep learning applied to the classification of skin lesionsPublication . Silva, Giuliana Martins; Monteiro, Fernando C.; Lazzaretti, André E.Skin cancer has been a global health issue and its diagnosis is a challenge in the medical field. Among all the types of skin cancer, melanoma is the worst and can be lethal if not early treated. The use of deep learning techniques, specifically, convolutional neural networks can help to improve the accuracy and speed up the classification of skin lesions. In this work, we aim to employ different image preprocessing techniques, various convolutional neural network models, data augmentation, and ensemble techniques to compare their results and provide an analysis of the data obtained. To achieve that, it was performed several experiments combining different image preprocessing techniques, which, paired with data augmentation strategies, aim to enhance the accuracy and reliability of the classification models. Additionally, three ensemble methods were tested to improve the classification systems’ robustness and reliability by gathering the strengths of each model. Our best result was the ensemble of EfficientNet-B2, EfficientNet-B5, and ResNeSt101 models with the application of data augmentation, and the combination of color constancy and hair removal techniques. This combined approach achieved a balanced accuracy of0.8132. By offering insights into the challenges faced, methodologies employed, and results obtained, this story aims to serve as a guide for researchers and practitioners aiming to advance the field of skin lesion classification using deep learning. Keywords: Deep Learning; Skin Lesion Classification; Image preprocessing.
- Deep learning techniques applied to skin lesion classification: a reviewPublication . Silva, Giuliana Martins; Lazzaretti, Andre E.; Monteiro, Fernando C.Skin cancer is one of the most common cancers in the world. The most dangerous type of skin cancer is melanoma, which can be lethal if not treated early. However, diagnosing skin lesions can be a difficult task. Therefore, deep learning techniques applied to the diagnosis of skin lesions have been explored by researchers, given their effectiveness in extracting features and classifying input data. In this work, we present a review of latest approaches that apply deep learning techniques to skin lesion classification task. In addition, some datasets used for training and validating the models are introduced, informing their characteristics and specificities, as well as popular pre-processing steps and skin lesion segmentation approaches. Finally, we comment the effectiveness of the proposed models.