Breast cancer accounts for the highest number of female deaths worldwide. Early detection of the disease is essential to increase the chances of treatment and cure of patients. Infrared thermography has emerged as a promising technique for diagnosis of the disease due to its low cost and that it does not emit harmful radiation, and it gives good results when applied in young women. This work uses convolutional neural networks in a database of 440 infrared images of 88 patients, classifying them into two classes: normal and pathology. During the training of the networks, we use transfer learning of the following convolutional neural network architectures: AlexNet, GoogLeNet, ResNet-18, VGG-16, and VGG-19. Our results show the great potential of using deep learning techniques combined with infrared images in the aid of breast cancer diagnosis.

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Automated oscillometric blood pressure monitors are commonly used to measure blood pressure for many patients at home, office, and medical centers, and they have been actively studied recently. These devices usually provide a single blood pressure point and they are not able to indicate the uncertainty of the measured quantity. We propose a new technique using an ensemble-based recursive methodology to measure uncertainty for oscillometric blood pressure measurements. There are three stages we consider: the first stage is pre-learning to initialize good parameters using the bagging technique. In the second stage, we fine-tune the parameters using the ensemble-based recursive methodology that is used to accurately estimate blood pressure and then measure the uncertainty for the systolic blood pressure and diastolic blood pressure in the third stage.

Estimating the correct respiratory rate (RR) is an essential technique for intensive care units, hospitals, geriatric hospital facilities, and home care services. Capnography is a standard methodology used to monitor carbon dioxide concentrations or partial pressures of respiratory gases to provide the most accurate RR measurements. However, it is inconvenient to use and has been primarily used while administering anesthesia and during intensive care. Many researchers now use electrocardiogram signals to estimate RR. Despite the recent developments, the current hospital environments suffer from inaccurate respiratory monitoring. While various machine learning techniques, including deep learning, have recently been applied to the medical processing sector, only a few studies have been conducted in the field of RR estimation. Therefore, using photoplethysmography, machine-learning techniques such as the ensemble gradient boosting algorithm are being employed in RR estimation. Multi-phases are used based on various feature extraction and selection methodology to improve the performance for RR estimation. In this study, the number of ensembles is increased, and the proposed ensemble methodology is effectively learned to estimate the RR. The proposed ensemble-based gradient boosting algorithm are compared with those of ensemble-based long-short memory network, and ensemble-based supported vector regression techniques, 3.30 breaths per min (bpm), 4.82 bpm and 5.83 bpm based on mean absolute errors. The proposed method shows a more accurate estimate of the respiration rate.

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