Lung cancer is a leading cause of death worldwide; it refers to the uncontrolled growth of abnormal cells in the lung. A computed tomography (CT) scan of the thorax is the most sensitive method for detecting cancerous lung nodules. A lung nodule is a round lesion which can be either non-cancerous or cancerous. In the CT, the lung cancer is observed as round white shadow nodules. The possibility to obtain a manually accurate interpretation from CT scans demands a big effort by the radiologist and might be a fatiguing process. Therefore, the design of a computer-aided diagnosis (CADx) system would be helpful as a second opinion tool. The stages of the proposed CADx are: a supervised extraction of the region of interest to eliminate the shape differences among CT images. The Daubechies db1, db2, and db4 wavelet transforms are computed with one and two levels of decomposition. After that, 19 features are computed from each wavelet sub-band. Then, the sub-band and attribute selection is performed. As a result, 11 features are selected and combined in pairs as inputs to the support vector machine (SVM), which is used to distinguish CT images containing cancerous nodules from those not containing nodules. The clinical data set used for experiments consists of 45 CT scans from ELCAP and LIDC. For the training stage 61 CT images were used (36 with cancerous lung nodules and 25 without lung nodules). The system performance was tested with 45 CT scans (23 CT scans with lung nodules and 22 without nodules), different from that used for training. The results obtained show that the methodology successfully classifies cancerous nodules with a diameter from 2 mm to 30 mm. The total preciseness obtained was 82%; the sensitivity was 90.90%, whereas the specificity was 73.91%. The CADx system presented is competitive with other literature systems in terms of sensitivity. The system reduces the complexity of classification by not performing the typical segmentation stage of most CADx systems. Additionally, the novelty of the algorithm is the use of a wavelet feature descriptor.

Technological innovations in the field of disease prevention and maintenance of patient health have enabled the evolution of fields such as monitoring systems. One of the main advances is the development of real-time monitors that use intelligent and wireless communication technology. In this paper, a system is presented for the remote monitoring of the body temperature and heart rate of a patient by means of a wireless sensor network (WSN) and mobile augmented reality (MAR). The combination of a WSN and MAR provides a novel alternative to remotely measure body temperature and heart rate in real time during patient care. The system is composed of (1) hardware such as Arduino microcontrollers (in the patient nodes), personal computers (for the nurse server), smartphones (for the mobile nurse monitor and the virtual patient file) and sensors (to measure body temperature and heart rate), (2) a network layer using WiFly technology, and (3) software such as LabView, Android SDK, and DroidAR. The results obtained from tests show that the system can perform effectively within a range of 20 m and requires ten minutes to stabilize the temperature sensor to detect hyperthermia, hypothermia or normal body temperature conditions. Additionally, the heart rate sensor can detect conditions of tachycardia and bradycardia.

Recent studies have reported that the inclusion of new technological elements such as augmented reality (AR), for educational purposes, increases the learning interest and motivation of students. However, developing AR applications, especially with mobile content, is still a rather technical subject; thus the dissemination of the technology in the classroom has been rather limited. This paper presents a new software architecture for AR application development based on freely available components; it provides a detailed view of the subsystems and tasks that encompass the creation of a mobile AR application. The typical task of plotting a quadratic equation was selected as a case study to obtain feasibility insights on how AR could support the teaching-learning process and to observe the student’s reaction to the technology and the particular application. The pilot study was conducted with 59 students at a Mexican undergraduate school. A questionnaire was created in order to obtain information about the students’ experience using the AR application and the analysis of the results obtained is presented. The comments expressed by the users after the AR experience are positive, supporting the premise that AR can be, in the future, a valuable complimentary teaching tool for topics that benefit from contextual learning experience and multipoint visualization, such as the quadratic equation.

In this paper a computational alternative to classify lung nodules using computed tomography (CT) thorax images is presented. The novelty of the method is the elimination of the segmentation stage. The contribution consist of several steps. After image acquisition, eight texture features were extracted from the histogram and the gray level coocurrence matrix (with four different angles) for each CT image. The features were used to train a non-parametric classifier called support vector machine (SVM), used to classify lung tissues into two classes: with lung nodules and without lung nodules. A total of 128 public clinical data set (ELCAP, NBIA) with different number of slices and diagnoses were used to train and evaluate the performance of the methodology presented. After the tests stage, five false negative (FN) and seven false positive (FP) results were obtained. The results obtained were validated by a radiologist to finally obtain a reliability index of 84%.