The exponential growth of deep learning networks has allowed us to tackle complex tasks, even in fields as complicated as medicine. However, using these models requires a large corpus of data for the networks to be highly generalizable and with high performance. In this sense, data augmentation methods are widely used strategies to train networks with small data sets, being vital in medicine due to the limited access to data. A clear example of this is magnetic resonance imaging in pathology scans associated with cancer. In this vein, we compare the effect of several conventional data augmentation schemes on the ResNet50 network for brain tumor detection. In addition, we included our strategy based on principal component analysis. The training was performed with the network trained from zeros and transfer-learning, obtained from the ImageNet dataset. The investigation allowed us to achieve an F1 detection score of 92.34%. The score was achieved with the ResNet50 network through the proposed method and implementing the learning transfer. In addition, it was also concluded that the proposed method is different from the other conventional methods with a significance level of 0.05 through the Kruskal Wallis test statistic.

Brain tumors are usually fatal diseases with low life expectancies due to the organs they affect, even if the tumors are benign. Diagnosis and treatment of these tumors are challenging tasks, even for experienced physicians and experts, due to the heterogeneity of tumor cells. In recent years, advances in deep learning (DL) methods have been integrated to aid in the diagnosis, detection, and segmentation of brain neoplasms. However, segmentation is a computationally expensive process, typically based on convolutional neural networks (CNNs) in the UNet framework. While UNet has shown promising results, new models and developments can be incorporated into the conventional architecture to improve performance. In this research, we propose three new, computationally inexpensive, segmentation networks inspired by Transformers. These networks are designed in a 4-stage deep encoder-decoder structure and implement our new cross-attention model, along with separable convolution layers, to avoid the loss of dimensionality of the activation maps and reduce the computational cost of the models while maintaining high segmentation performance. The new attention model is integrated in different configurations by modifying the transition layers, encoder, and decoder blocks. The proposed networks are evaluated against the classical UNet network, showing that our networks have differences of up to an order of magnitude in the number of training parameters. Additionally, one of the models outperforms UNet, achieving training in significantly less time and with a Dice Similarity Coefficient (DSC) of up to 94%, ensuring high effectiveness in brain tumor segmentation.

Diabetes mellitus (DM) is a metabolic disorder characterized by increased blood glucose. The pathology can manifest itself with different conditions, including neuropathy, the main consequence of diabetic disease. Statistics show worrying figures worldwide, diagnosed an estimated 1.6 million people with DM by 2025. In this sense, alternative and automated methods are necessary to detect DM, allowing it to take the pertinent measures in its treatment and avoid critical complications, such as the diabetic foot. On the other hand, foot thermography is a promising tool that allows visualization of thermal patterns, patterns that are altered as a consequence of shear and friction associated with lower limb neuropathy. Based on these considerations, we explored different strategies to detect patients with DM from foot thermography in this research. Initially, the study focused on finding a classification index like Thermal Change Index (TCI). Subsequently, we used the deep convolutional neural networks paradigm, implementing 12 different data augmentation methods, of which four are conventional, and 8 are newly proposed methods. The results showed that the proposed and the conventional methods increased the network’s performance, where a 100% detection was achieved by weighting the DM probability percentages for both images of the feet. Finally, it was also possible to demonstrate the importance of transfer learning, which does not depend on the type of database, but on the data corpus with which the transfer was trained.

Nowadays, a consequence of data overload is that world's technology capacity to collect, communicate, and store large volumes of data is increasing faster than human analysis skills. Such an issue has motivated the development of graphic ways to visually represent and analyze high-dimensional data. Particularly, in this work, we propose a graphical interface that allow the combination of dimensionality reduction (DR) methods using a chromatic model to make data visualization more intelligible for humans. This interface is designed for an easy and interactive use, so that input parameters are given by the user via the selection of RGB values inside a given surface. Proposed interface enables (even non-expert) users to intuitively either select a concrete DR method or carry out a mixture of methods. Experimental results proves the usability of our interface making the selection or configuration of a DR-based visualization an intuitive and interactive task for the user.

An increase in plantar pressure and skin temperature is commonly associated with an increased risk of diabetic foot ulcers. However, the effect of insoles in reducing plantar temperature has not been commonly studied. The aim was to assess the effect of walking in insoles with different features on plantar temperature. Twenty-six (F/M:18/8) participants—13 with diabetes and 13 healthy, aged 55.67 ± 9.58 years—participated in this study. Skin temperature at seven plantar regions was measured using a thermal camera and reported as the difference between the temperature after walking with an insole for 20 m versus the baseline temperature. The mixed analyses of variance indicated substantial main effects for the Insole Condition, for both the right [Wilks’ Lambda = 0.790, F(14, 492) = 4.393, p < 0.01, partial eta squared = 0.111] and left feet [Wilks’ Lambda = 0.890, F(14, 492) = 2.103, p < 0.011, partial eta squared = 0.056]. The 2.5 mm-tall dimple insole was shown to be significantly more effective at reducing the temperature in the hallux and third met head regions compared to the 4 mm-tall dimple insole. The insoles showed to be significantly more effective in the diabetes group versus the healthy group, with large effect size for the right [Wilks’ Lambda = 0.662, F(14, 492) = 8.037, p < 0.000, Partial eta-squared = 0.186] and left feet [Wilks’ Lambda = 0.739, F(14, 492) = 5.727, p < 0.000, Partial eta-squared = 0.140]. This can have important practical implications for designing insoles with a view to decrease foot complications in people with diabetes.