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Melanomas are the most aggressive form of skin cancer. Due to observer bias, computerized analysis of dermoscopy images has become an important research area. One of the most important steps in dermoscopy image analysis is the automated detection of lesion areas in the dermoscopy images. In this paper, we present a deep learning method for automatic skin lesion segmentation. We use a subset of the...
Deep learning methods for image analysis have shown impressive performance in recent years. In this paper, we present deep learning based approaches to solve two problems in skin lesion analysis using a dermoscopic image containing skin tumor. In the first problem, we use a fully convolutional-deconvolutional architecture to automatically segment skin tumor from the surrounding skin. In the second...
Skin segmentation, which involves detecting human skin areas in an image, is an important process for skin disease analysis. The aim of this paper is to identify the skin regions in a newly collected set of psoriasis images. For this purpose, we present a committee of machine learning (ML) classifiers. A psoriasis training set is first collected by using pixel values in five different color spaces...
The hand segmentation is the critical pre-processing of the gesture recognition application. Nowadays, to achieve a robust hand segmentation under cluttered background is still challenging. Advanced research in model-driven approach based on the depth information has obtained impressive performance. However, it is unable to deal with the hand very close to the body part. Also, a large number of marked...
This paper proposes a novel methodology to predict thermal comfort states of occupants with k-means approach. The approach is embedded into an optimization problem, which is used to locate optimal operating conditions via Augmented Firefly Algorithm (AFA), for improving energy efficiency of buildings and maintaining satisfactory indoor thermal comfort states in the meantime. The neural networks models...
Automated, efficient and accurate classification of skin diseases using digital images of skin is very important for bio-medical image analysis. Various techniques have already been developed by many researchers. In this work, a technique based on meta-heuristic supported artificial neural network has been proposed to classify images. Here 3 common skin diseases have been considered namely angioma,...
Physical interactions between human and machine are essential in facilitating effective physical therapy training programs. Nowadays, physical training largely involves robotic assistive devices or wearable haptics. In this study, we propose a lightweight wearable sensory augmentation device using skin stretch feedback to provide individuals with additional sensory cues during balance training. The...
Development of local fruit industry in Indonesia is very high, but less competitive than imported fruits. Produced fruit kinds in this country is very diverse, but the use of technology to support the production and distribution is still not widely applied. This makes the local fruit market less competitive than imported fruits that has largely been applying technology production support. In this...
Selfies have become commonplace. More and more people take pictures of themselves, and enjoy enhancing these pictures using a variety of image processing techniques. One specific functionality of interest is automatic skin and hair segmentation, as this allows for processing one's skin and hair separately. Traditional approaches require user input in the form of fully specified trimaps, or at least...
Face editing has a variety of applications, especially with the increasing popularity of photography using mobile devices. In this work, we argue that the performance of face image editing can be further improved by using semantic segmentation which marks each pixel with a label that indicates its corresponding facial part. To this end, we propose a deep learning based method for automatic pixel-level...
This pilot study was conducted in the framework of SmokeFreeBrain project and it aimed at assessing the subjective emotional impact of skin temperature training and neurofeedback training on smokers by means of the AffectLecture application. The current paper constitutes a proof-of-concept, exploring the case of a single participant. The intervention consists of 5 sessions of biofeedback followed...
Skin cancer is one of the most common malignancies in fair skin population. It can be divided in two main classes: melanoma and non-melanoma skin cancer. This means that pigmented and, also, non-pigmented skin lesions might raise an important risk. Due to the fact that melanoma is more aggressive, pigmented skin lesions gained more attention in terms of automatic diagnosis. One of the most important...
Stress causes many physiological changes in the body and has significant effects on physiology. Various types of acute stress include social, competition, emotional and mental stress. Several studies and experiments have been conducted to investigate stress detection and measurement with physiological signals. We designed social and competition stress experiments to test our algorithms to discriminate...
Segmentation of skin lesions is considered as an important step in computer aided diagnosis (CAD) for melanoma diagnosis. There have many attempts to segment skin lesions in a semi- or fully-automated manner. Existing methods, however, have problems with over- or under-segmentation and do not perform well with challenging skin lesions such as when a lesion is partially connected to the background...
Similarity in appearance between various skin diseases, often makes it challenging for clinicians to identify the type of skin condition, and the accuracy is highly reliant on the level of expertise. There is also a great degree of subjectivity and inter/intra observer variability found in the clinical practices. In this paper, we propose a method for automatic skin diseases recognition that combines...
Neural networks are powerful tools for medical image classification and segmentation. However, existing network structures and training procedures assume that the output classes are mutually exclusive and equally important. Many datasets of medical images do not satisfy these conditions. For example, some skin disease datasets have images labelled as coarse-grained class (such as Benign) in addition...
Accurate skin lesion segmentation is an important yet challenging problem for medical image analysis. The skin lesion segmentation is subject to variety of challenges such as the significant pattern and colour diversity found within the lesions, presence of various artifacts, etc. In this paper, we present two fully convolutional networks with several side outputs to take advantage of discriminative...
Photoplethysmography (PPG) is one of the simple and non-invasive heart rate (HR) sensing methods, but when applying it to a person during exercise, the output is contaminated with motion artifact (MA). Furthermore, when the pressure to stabilize the sensor on the skin surface is lower, extremely large values referred to as "outliers" are often observed in the sensed heart rate. To cancel...
Inadequate sleep affects health in multiple ways. Unobtrusive ambulatory methods to monitor long-term sleep patterns in large populations could be useful for health and policy decisions. This paper presents an algorithm that uses multimodal data from smartphones and wearable technologies to detect sleep/wake state and sleep episode on/offset. We collected 5580 days of multimodal data and applied recurrent...
In this study, we investigate what a practically useful approach is in order to achieve robust skin disease diagnosis. A direct approach is to target the ground truth diagnosis labels, while an alternative approach instead focuses on determining skin lesion characteristics that are more visually consistent and discernible. We argue that, for computer aided skin disease diagnosis, it is both more realistic...
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