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Automated Mobile Image Acquisition of Macroscopic Dermatological Lesions
... With ongoing advancements, these devices have seen increases in both versatility and performance, alongside enhancements in the number of their embedded sensors. These improvements open new avenues for utilizing mobile devices in specialized image processing tasks in various contexts, including industrial applications [3], agriculture [4] and healthcare [5]. ...
... This approach can significantly enhance image quality and improve object detection accuracy in mobile applications. [5], [15]. ...
... A method for automated image focus assessment is proposed in [5] for segmenting dermatological lesions using a mobile application. The results demonstrated that the method not only effectively distinguishes focused from non-focused images but also enables real-time processing and provides user feedback. ...
Identifying damages in Printed Circuit Boards is a critical task for quality assurance and repair inspection workflows. Image processing mobile applications, with embedded deep learning, assist technicians in detecting damages in this task, increasing accuracy and agility. However, the performance of such applications is highly dependent on the ability of the user in taking adequate photos. We propose an automatic capture method named PCBShot, that assists users of mobile applications of PCB damage detection to take better photos, enhancing the detection performance. Our method uses classical image processing algorithms to detect if a target PCB is inside a virtual guideline, ensuring that the position and distance are appropriate. Then, a photo is automatically captured, the background is cropped and the image is sliced into four quadrants for resolution preservation. The damage detection is performed in the slices. We evaluate our method through a real-life mobile application used in repair centers of an electronics manufacturer, comparing the detection performance with the manual image acquisition, without further assistance. Our results show that our method largely surpasses the manual acquisition, as it allows the capture of higher-quality images due to framing assistance with image processing methods, eliminating noisy backgrounds and preserving resolution.
... It is used to evaluate skin structures within the epidermis and dermis by reducing surface reflectance [20]. Dermatoscopic images can be acquired by a smartphone [35]. It is a high-resolution skin imaging technique that allows visualization of deeper skin structures. ...
Skin cancer is a highly relevant health problem around the world. The World Health Organization (WHO) reports that one-third of the diagnosed cancers are skin cancer. It is well known that early detection of skin cancer significantly increases the prognosis of patients. In many cases, however, the absence of clinical devices and qualified experts makes this task very difficult. To overcome this problem, in the last years advanced deep learning techniques have been proposed to recognize skin cancer automatically showing promising results. In this work, we evaluate 50 deep learning approaches on the well-known HAM10000 dataset of dermatoscopic images from seven different skin lesion categories. The approaches have been evaluated using the same experimental protocol. In our experiments, the performance of each approach in terms of accuracy and computational time are measured. In addition, the number of trainable parameters and the number of features of the last layer of the deep learning architecture are given. Thus, comparison between the approaches can be easily established. The results showed that the best performance has been achieved by a deep learning approach based on visual transformers with 84.29% of accuracy on the testing subset. We know that these results may vary significantly on other datasets. For this reason, rather than establish which method is the best, the main contribution of this work is to make the 50 deep learning approaches available in a simple way for future research in the area. We believe that this methodology, i.e., training, testing and evaluating many deep learning methods, can be very helpful to establish which is the best architecture and what is the highest performance that can be achieved on new datasets.KeywordsDeep learningImage classificationMedical imagingSkin lesion recognition
... The present work integrates a larger project, DermAI, that aims to improve the existing Teledermatology processes between Primary Care Units (PCU) and Dermatology Services in the Portuguese National Health Service (NHS) for skin lesion referral. Through the usage of Artificial Intelligence (AI) and Computer Vision, we envision two major goals: (a) to support doctors in Primary Care Units through the development of a mobile application that fosters image acquisition standardization [9] and (b) to assist dermatologists in the referral process for booking specialist consultations in the hospital through the adequate prioritization of cases. Improving dermatology consultations' prioritization is particularly relevant in the Portuguese scenario due to the lack of specialists in the NHS and the long waiting lists for this type of consultation. ...
Teledermatology has developed rapidly in recent years and is nowadays an essential tool for early diagnosis. In this work, we aim to improve existing Teledermatology processes for skin lesion diagnosis by developing a deep learning approach for risk prioritization with a dataset of retrospective data from referral requests of the Portuguese National Health System. Given the high complexity of this task, we propose a new prioritization pipeline guided and inspired by domain knowledge. We explored automatic lesion segmentation and tested different learning schemes, namely hierarchical classification and curriculum learning approaches, optionally including additional patient metadata. The final priority level prediction can then be obtained by combining predicted diagnosis and a baseline priority level accounting for explicit expert knowledge. In both the differential diagnosis and prioritization branches, lesion segmentation with 30% tolerance for contextual information was shown to improve classification when compared with a flat baseline model trained on original images; furthermore, the addition of patient information was not beneficial for most experiments. Curriculum learning delivered better results than a flat or hierarchical approach. The combination of diagnosis information and a knowledge map, created in collaboration with dermatologists, together with the priority achieved interesting results (best macro F1 of 43.93% for a validated test set), paving the way for new data-centric and knowledge-driven approaches.
... Digital cameras and cameras embedded in mobile devices increase the quality and resolution of the images captured [1,2], allowing for powerful solutions for different areas of human life [3,4]. Currently, the emergence of studies related to healthcare and technology is increasing, and it is participating in the new era of medicine related to patient empowerment [5]. ...
Healthcare treatments might benefit from advances in artificial intelligence and technological equipment such as smartphones and smartwatches. The presence of cameras in these devices with increasingly robust and precise pattern recognition techniques can facilitate the estimation of the wound area and other telemedicine measurements. Currently, telemedicine is vital to the maintenance of the quality of the treatments remotely. This study proposes a method for measuring the wound area with mobile devices. The proposed approach relies on a multi-step process consisting of image capture, conversion to grayscale, blurring, application of a threshold with segmentation, identification of the wound part, dilation and erosion of the detected wound section, identification of accurate data related to the image, and measurement of the wound area. The proposed method was implemented with the OpenCV framework. Thus, it is a solution for healthcare systems by which to investigate and treat people with skin-related diseases. The proof-of-concept was performed with a static dataset of camera images on a desktop computer. After we validated the approach’s feasibility, we implemented the method in a mobile application that allows for communication between patients, caregivers, and healthcare professionals.
... In summary, the detailed control of image quality and adequacy should be considered an extremely important factor during the design of a mobile application intended for trapbased insect monitoring. In fact, promising results have been recently reported for different healthcare solutions [22][23][24], by embedding AI to effectively support the user in the handheld image acquisition process. However, the use of similar approaches in viticulture is non-existent. ...
The increasing alarming impacts of climate change are already apparent in viticulture, with unexpected pest outbreaks as one of the most concerning consequences. The monitoring of pests is currently done by deploying chromotropic and delta traps, which attracts insects present in the production environment, and then allows human operators to identify and count them. While the monitoring of these traps is still mostly done through visual inspection by the winegrowers, smartphone image acquisition of those traps is starting to play a key role in assessing the pests’ evolution, as well as enabling the remote monitoring by taxonomy specialists in better assessing the onset outbreaks. This paper presents a new methodology that embeds artificial intelligence into mobile devices to establish the use of hand-held image capture of insect traps for pest detection deployed in vineyards. Our methodology combines different computer vision approaches that improve several aspects of image capture quality and adequacy, namely: (i) image focus validation; (ii) shadows and reflections validation; (iii) trap type detection; (iv) trap segmentation; and (v) perspective correction. A total of 516 images were collected, divided into three different datasets and manually annotated, in order to support the development and validation of the different functionalities. By following this approach, we achieved an accuracy of 84% for focus detection, an accuracy of 80% and 96% for shadows/reflections detection (for delta and chromotropic traps, respectively), as well as mean Jaccard index of 97% for the trap’s segmentation.
Every year, the number of skin cancer cases has been increasing which, consequently, increases the strain on the health care systems around the globe. With the growth of processing power and camera quality on smartphones, the investment in telemedicine and the development of mobile teledermatology applications can, not only contribute to the standardization of image acquisitions but also, facilitate early diagnosis. This paper presents a new process for real-time automated image acquisition of macroscopic skin images with the merging of an automated focus assessment feature-based machine learning algorithm with conventional computer vision techniques to segment dermatological images. Three datasets were used to develop and evaluate the proposed methodology. One comprised of 3428 images acquired with a mobile phone for this purpose and 1380 from the other two datasets which are publicly available. The best focus assessment model achieved an accuracy of 88.3% and an F1-Score of 86.8%. The segmentation algorithm obtained a Jaccard index of 85.81% for the SMARTSKINS dataset and 68.59% for the Dermofit dataset. The algorithms were deployed to a mobile application, available in Android and iOS, without causing any performance hindrances. The application was tested in a real environment, being used in a 10-month pilot study with six General and Family Medicine doctors and one Dermatologist. The easiness to acquire dermatological images, image quality, and standardization were referred to as the main advantages of the application.
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