Thermography is a useful tool since it provides information that may help in the diagnostic of several diseases in a noninvasive and fast way. Particularly, thermography has been applied in the study of the diabetic foot. However, most of these studies report only qualitative information making it difficult to measure significant parameters such as temperature variations. These variations are important in the analysis of the diabetic foot since they could bring knowledge, for instance, regarding ulceration risks. The early detection of ulceration risks is considered an important research topic in the medicine field, as its objective is to avoid major complications that might lead to a limb amputation. The absence of symptoms in the early phase of the ulceration is conceived as the main disadvantage to provide an opportune diagnostic in subjects with neuropathy. Since the relation between temperature and ulceration risks is well established in the literature, a methodology that obtains quantitative temperature differences in the plantar area of the diabetic foot to detect ulceration risks is proposed in this work. Such methodology is based on the angiosome concept and image processing.

Food waste is a global concern and is the focus of this research. Currently, no method in the state of the art classifies multiple fruits and vegetables and their level of ripening. The objective of the study is to design and develop an intelligent system based on deep learning techniques to classify between types of fruits and vegetables, and also to evaluate the level of ripeness of some of them. The system consists of two models using the MobileNet V2 architecture. One algorithm is for the classification of 32 classes of fruits and vegetables, and another is for the determination of the ripeness of 6 classes of them. The overall intelligent system is the union of the two models, predicting first the class of fruit or vegetable and then its ripeness. The fruits and vegetables classification model achieved 97.86% accuracy, 98% precision, 98% recall, and 98% F1-score, while the ripeness assessment model achieved 100% accuracy, 98% precision, 99% recall, and 99% F1-score. According to the results, the proposed system is able to classify between types of fruits and vegetables and evaluate their ripeness. To achieve the best performance indicators, it is necessary to obtain the appropriate hyperparameters for the artificial intelligence models, in addition to having an extensive database with well-defined classes.

Breast cancer is the leading disease in incidence and mortality among women in developing countries. The opportune diagnosis of this disease strengthens the survival index. Mammography application is limited by age and periodicity. Temperature is a physical magnitude that can be measured by using multiple sensing techniques. IR (infrared) thermography using commercial cameras is gaining relevance in industrial and medical applications because it is a non-invasive and non-intrusive technology. Asymmetrical temperature in certain human body zones is associated with cancer. In this paper, an IR thermographic sensor is applied for breast cancer detection. This work includes an automatic breast segmentation methodology, to spot the hottest regions in thermograms using the morphological watershed operator to help the experts locate the tumor. A protocol for thermogram acquisition considering the required time to achieve a thermal stabilization is also proposed. Breast thermograms are evaluated as thermal matrices, instead of gray scale or false color images, increasing the certainty of the provided diagnosis. The proposed tool was validated using the Database for Mastology Research and tested in a voluntary group of 454 women of different ages and cancer stages with good results, leading to the possibility of being used as a supportive tool to detect breast cancer and angiogenesis cases.