Deep learning algorithms produces state-of-the-art results for different machine learning and computer vision tasks. To perform well on a given task, these algorithms require large dataset for training. However, deep learning algorithms lack generalization and suffer from over-fitting whenever trained on small dataset, especially when one is dealing with medical images. For supervised image analysis in medical imaging, having image data along with their corresponding annotated ground-truths is costly as well as time consuming since annotations of the data is done by medical experts manually. In this paper, we propose a new Generative Adversarial Network for Medical Imaging (MI-GAN). The MI-GAN generates synthetic medical images and their segmented masks, which can then be used for the application of supervised analysis of medical images. Particularly, we present MI-GAN for synthesis of retinal images. The proposed method generates precise segmented images better than the existing techniques. The proposed model achieves a dice coefficient of 0.837 on STARE dataset and 0.832 on DRIVE dataset which is state-of-the-art performance on both the datasets.

Chest X-ray (CXR) is a low-cost medical imaging technique. It is a common procedure for the identification of many respiratory diseases compared to MRI, CT, and PET scans. This paper presents the use of generative adversarial networks (GAN) to perform the task of lung segmentation on a given CXR. GANs are popular to generate realistic data by learning the mapping from one domain to another. In our work, the generator of the GAN is trained to generate a segmented mask of a given input CXR. The discriminator distinguishes between a ground truth and the generated mask, and updates the generator through the adversarial loss measure. The objective is to generate masks for the input CXR, which are as realistic as possible compared to the ground truth masks. The model is trained and evaluated using four different discriminators referred to as D1, D2, D3 and D4 respectively. Experimental results on three different CXR datasets reveal that the proposed model is able to achieve a dice-score of 0.9740, and IOU score of 0.943, which are better than other reported state-of-the art results. INDEX TERMS Deep learning, generative adversarial networks, lung segmentation, medical imaging.

Automatic recognition of Urdu handwritten digits and characters, is a challenging task. It has applications in postal address reading, bank's cheque processing, and digitization and preservation of handwritten manuscripts from old ages. While there exists a significant work for automatic recognition of handwritten English characters and other major languages of the world, the work done for Urdu language is extremely insufficient. This paper has two goals. Firstly, we introduce a pioneer dataset for handwritten digits and characters of Urdu, containing samples from more than 900 individuals. Secondly, we report results for automatic recognition of handwritten digits and characters as achieved by using deep auto-encoder network and convolutional neural network. More specifically, we use a two-layer and a three-layer deep autoencoder network and convolutional neural network and evaluate the two frameworks in terms of recognition accuracy. The proposed framework of deep autoencoder can successfully recognize digits and characters with an accuracy of 97% for digits only, 81% for characters only and 82% for both digits and characters simultaneously. In comparison, the framework of convolutional neural network has accuracy of 96.7% for digits only, 86.5% for characters only and 82.7% for both digits and characters simultaneously. These frameworks can serve as baselines for future research on Urdu handwritten text.

Physiological pressure measurement is one of the most common applications of sensors in healthcare. Particularly, continuous pressure monitoring provides key information for early diagnosis, patient-specific treatment, and preventive healthcare. This paper presents a thin-film flexible wireless pressure sensor for continuous pressure measurement in a wide range of medical applications but mainly focused on interface pressure monitoring during compression therapy to treat venous insufficiency. The sensor is based on a pressure-dependent capacitor (C) and printed inductive coil (L) that form an inductor-capacitor (LC) resonant circuit. A matched reader coil provides an excellent coupling at the fundamental resonance frequency of the sensor. Considering varying requirements of venous ulceration, two versions of the sensor, with different sizes, were finalized after design parameter optimization and fabricated using a cost-effective and simple etching method. A test setup consisting of a glass pressure chamber and a vacuum pump was developed to test and characterize the response of the sensors. Both sensors were tested for a narrow range (0–100 mmHg) and a wide range (0–300 mmHg) to cover most of the physiological pressure measurement applications. Both sensors showed good linearity with high sensitivity in the lower pressure range <100 mmHg, providing a wireless monitoring platform for compression therapy in venous ulceration.

Stress is known as a silent killer that contributes to several life-threatening health conditions such as high blood pressure, heart disease, and diabetes. The current standard for stress evaluation is based on self-reported questionnaires and standardized stress scores. There is no gold standard to independently evaluate stress levels despite the availability of numerous biophysiological stress indicators. With an increasing interest in wearable health monitoring in recent years, several studies have explored the potential of various biophysiological indicators of stress for this purpose. However, there is no clear understanding of the relative sensitivity and specificity of these stress-related biophysiological indicators of stress in the literature. Hence this study aims to perform statistical analysis and classification modelling of biophysiological data gathered from healthy individuals, undergoing various induced emotional states, and to assess the relative sensitivity and specificity of common biophysiological indicators of stress. In this paper, several frequently used key indicators of stress, such as heart rate, respiratory rate, skin conductance, RR interval, heart rate variability in the electrocardiogram, and muscle activation measured by electromyography, are evaluated based on a detailed statistical analysis of the data gathered from an already existing, publicly available WESAD (Wearable Stress and Affect Detection) dataset. Respiratory rate and heart rate were the two best features for distinguishing between stressed and unstressed states.