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.

Convolutional Neural Networks (CNNs) have proven to be an effective approach for solving image classification problems. The output, the accuracy and the computational efficiency of a CNN are determined mainly by the architecture, the convolutional filters, and the activation functions. Based on the importance of an activation function, in this paper, nine new activation functions based on combinations of classical functions such as ReLU and sigmoid are presented. Also, a study about the effects caused by the activation functions in the performance of a CNN is presented. First, every new function is described, also, their graphs, analytic forms and derivatives are presented. Then, a traditional CNN model with each new activation function is used to classify three 10-class databases: MNIST, Fashion MNIST and a handwritten digit database created by us. Experimental results illustrate that some of the proposed activation functions lead to better performances on classifying than classical activation functions. Moreover, our study demonstrated that the accuracy of a CNN could be increased by 1.18% with the new proposed activation functions.

The bandelet transform take advantage of the geometrical regularity of the structure of an image and is appropriate for the analysis of edges and textures of the images. Denoising is one of the most interesting and widely investigated topics in image processing area. The main problem in denosing is the tradeoff between the noise suppression and oversmoothing of image details. In order to solve that problem, in this paper we exploit the geometrical advantages offered by the bandelet transform to solve the problem of image denoising. We present the results obtained with the bandelet transform for denoising process with additive white Gaussian noise and salt and pepper noise. A comparison is made with those results obtained with wavelets and contourlets. We show that bandelets can outperform the wavelets and contourlets in terms of subjective and objective measures.