Skin cancer is one of the most severe diseases, and medical imaging is among the main tools for cancer diagnosis. The images provide information on the evolutionary stage, size, and location of tumor lesions. This paper focuses on the classification of skin lesion images considering a framework of four experiments to analyze the classification performance of Convolutional Neural Networks (CNNs) in distinguishing different skin lesions. The CNNs are based on transfer learning, taking advantage of ImageNet weights. Accordingly, in each experiment, different workflow stages are tested, including data augmentation and fine-tuning optimization. Three CNN models based on DenseNet-201, Inception-ResNet-V2, and Inception-V3 are proposed and compared using the HAM10000 dataset. The results obtained by the three models demonstrate accuracies of 98%, 97%, and 96%, respectively. Finally, the best model is tested on the ISIC 2019 dataset showing an accuracy of 93%. The proposed methodology using CNN represents a helpful tool to accurately diagnose skin cancer disease.

2019 under the Hermes Project through Construcción del Prototipo de un Sistema de Visión Multiespectral Basado en Iluminación Light Emitting Diode (LED) under Grant 48996.

The emergence of COVID-19 as a global pandemic forced researchers worldwide in various disciplines to investigate and propose efficient strategies and/or technologies to prevent COVID-19 from further spreading. One of the main challenges to be overcome is the fast and efficient detection of COVID-19 using deep learning approaches and medical images such as Chest Computed Tomography (CT) and Chest X-ray images. In order to contribute to this challenge, a new dataset was collected in collaboration with “S.E.S Hospital Universitario de Caldas” (https://hospitaldecaldas.com/) from Colombia and organized following the Medical Imaging Data Structure (MIDS) format. The dataset contains 7,307 chest X-ray images divided into 3,077 and 4,230 COVID-19 positive and negative images. Images were subjected to a selection and anonymization process to allow the scientific community to use them freely. Finally, different convolutional neural networks were used to perform technical validation. This dataset contributes to the scientific community by tackling significant limitations regarding data quality and availability for the detection of COVID-19.

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COVID-19 caused by the SARS-CoV-2 virus has affected healthcare and people's lifestyles worldwide since 2019. Among the available diagnostic tools, reverse transcription-polymerase chain reaction has proven highly accurate. However, the need for a specialized laboratory makes these tests expensive and time-consuming between sample collection and results. Currently, there are initial steps for the diagnosis of COVID-19 through chest x-ray images. Additionally, artificial intelligence techniques like deep learning (DL) help identify abnormalities. Inspired by the reported success of DL, this chapter presents an introduction to state-of-the-art DL-based approaches applied to the detection of COVID-19 in chest x-ray images, which currently allows assessing disease severity. The results presented are obtained using well-known models and some novel networks designed for this task. In addition, the models were evaluated using the most used public datasets, applying preprocessing techniques to improve detection results. Finally, this chapter shows some possible future research directions.