scholarly journals The Role in Teledermoscopy of an Inexpensive and Easy-to-Use Smartphone Device for the Classification of Three Types of Skin Lesions Using Convolutional Neural Networks

Diagnostics ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 451
Author(s):  
Federica Veronese ◽  
Francesco Branciforti ◽  
Elisa Zavattaro ◽  
Vanessa Tarantino ◽  
Valentina Romano ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Low Cost ◽  
Skin Lesions ◽  
Melanocytic Nevus ◽  
Ease Of Use ◽  
Small Data ◽  
Seborrheic Keratosis ◽  
Data Set ◽  
Face To Face

Background. The use of teledermatology has spread over the last years, especially during the recent SARS-Cov-2 pandemic. Teledermoscopy, an extension of teledermatology, consists of consulting dermoscopic images, also transmitted through smartphones, to remotely diagnose skin tumors or other dermatological diseases. The purpose of this work was to verify the diagnostic validity of images acquired with an inexpensive smartphone microscope (NurugoTM), employing convolutional neural networks (CNN) to classify malignant melanoma (MM), melanocytic nevus (MN), and seborrheic keratosis (SK). Methods. The CNN, trained with 600 dermatoscopic images from the ISIC (International Skin Imaging Collaboration) archive, was tested on three test sets: ISIC images, images acquired with the NurugoTM, and images acquired with a conventional dermatoscope. Results. The results obtained, although with some limitations due to the smartphone device and small data set, were encouraging, showing comparable results to the clinical dermatoscope and up to 80% accuracy (out of 10 images, two were misclassified) using the NurugoTM demonstrating how an amateur device can be used with reasonable levels of diagnostic accuracy. Conclusion. Considering the low cost and the ease of use, the NurugoTM device could be a useful tool for general practitioners (GPs) to perform the first triage of skin lesions, aiding the selection of lesions that require a face-to-face consultation with dermatologists.

scholarly journals Robustness of convolutional neural networks in recognition of pigmented skin lesions

2021 ◽  
Vol 145 ◽  
pp. 81-91
Author(s):  
Roman C. Maron ◽  
Sarah Haggenmüller ◽  
Christof von Kalle ◽  
Jochen S. Utikal ◽  
Friedegund Meier ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Skin Lesions ◽  
Pigmented Skin Lesions

scholarly journals Convolutional Neural Networks Model in Premature Detection of Melanoma

2019 ◽  
Vol 8 (12S2) ◽  
pp. 6-11 ◽  
Keyword(s):  
Neural Networks ◽  
Skin Cancer ◽  
Convolutional Neural Networks ◽  
Additional Data ◽  
Data Set ◽  
Non Invasive ◽  
Convolution Filter ◽  
The University ◽  
Improved Accuracy

The objective of this research is provide to the specialists in skin cancer, a premature, rapid and non-invasive diagnosis of melanoma identification, using an image of the lesion, to apply to the treatment of a patient, the method used is the architecture contrast of Convolutional neural networks proposed by Laura Kocobinski of the University of Boston, against our architecture, which reduce the depth of the convolution filter of the last two convolutional layers to obtain maps of more significant characteristics. The performance of the model was reflected in the accuracy during the validation, considering the best result obtained, which is confirmed with the additional data set. The findings found with the application of this base architecture were improved accuracy from 0.79 to 0.83, with 30 epochs, compared to Kocobinski's AlexNet architecture, it was not possible to improve the accuracy of 0.90, however, the complexity of the network played an important role in the results we obtained, which was able to balance and obtain better results without increasing the epochs, the application of our research is very helpful for doctors, since it will allow them to quickly identify if an injury is melanoma or not and consequently treat it efficiently.

scholarly journals Multiple-Oriented and Small Object Detection with Convolutional Neural Networks for Aerial Image

2019 ◽  
Vol 11 (18) ◽  
pp. 2176 ◽  
Author(s):  
Chen ◽  
Zhong ◽  
Tan
Keyword(s):  
Neural Networks ◽  
Object Detection ◽  
Convolutional Neural Networks ◽  
Aerial Images ◽  
Superior Performance ◽  
Aerial Image ◽  
Detection Accuracy ◽  
Small Object ◽  
Data Set ◽  
Orientation Information

Detecting objects in aerial images is a challenging task due to multiple orientations and relatively small size of the objects. Although many traditional detection models have demonstrated an acceptable performance by using the imagery pyramid and multiple templates in a sliding-window manner, such techniques are inefficient and costly. Recently, convolutional neural networks (CNNs) have successfully been used for object detection, and they have demonstrated considerably superior performance than that of traditional detection methods; however, this success has not been expanded to aerial images. To overcome such problems, we propose a detection model based on two CNNs. One of the CNNs is designed to propose many object-like regions that are generated from the feature maps of multi scales and hierarchies with the orientation information. Based on such a design, the positioning of small size objects becomes more accurate, and the generated regions with orientation information are more suitable for the objects arranged with arbitrary orientations. Furthermore, another CNN is designed for object recognition; it first extracts the features of each generated region and subsequently makes the final decisions. The results of the extensive experiments performed on the vehicle detection in aerial imagery (VEDAI) and overhead imagery research data set (OIRDS) datasets indicate that the proposed model performs well in terms of not only the detection accuracy but also the detection speed.

Mobile Educational Technology

2011 ◽  
pp. 1424-1431
Author(s):  
Chris Houser ◽  
Patricia Thornton
Keyword(s):  
Mobile Devices ◽  
Learning Experience ◽  
Low Cost ◽  
Ease Of Use ◽  
Collaborative Problem Solving ◽  
Personal Digital Assistants ◽  
Web Pages ◽  
Face To Face ◽  
Web Interfaces ◽  
E Mail

Mobile devices such as laptop computers, PDAs (personal digital assistants), and cell phones offer many features useful for learning both inside and outside classrooms. These devices offer access to Web pages and e-mail, and provide other functions such as textual noting and video cameras. They promise a single, easily learned device that can be useful in a variety of educational settings. When used appropriately, these devices can enrich the learning experience by connecting learners with each other, their environment, and with information providers. They can enable collaborative problem solving by providing easy face-to-face sharing of data through IR (infrared) beaming or distance sharing through e-mail and Web interfaces. For learners who require repetitive practice for skills development, mobile devices offer a personal tool that can be used anytime, anywhere for quick review. Because of their low cost and ease of use, mobile devices have the potential to bring the power of a computer to every learner.

scholarly journals Evaluation of convolutional neural networks for the classification of falls from heterogeneous thermal vision sensors

2020 ◽  
Vol 16 (5) ◽  
pp. 155014772092048
Author(s):  
Miguel Ángel López-Medina ◽  
Macarena Espinilla ◽  
Chris Nugent ◽  
Javier Medina Quero
Keyword(s):  
Neural Networks ◽  
High Resolution ◽  
Convolutional Neural Networks ◽  
Viewing Angle ◽  
Vision Sensor ◽  
Low Resolution ◽  
Data Set ◽  
Wide Viewing Angle ◽  
Vision Sensors ◽  
Thermal Vision

The automatic detection of falls within environments where sensors are deployed has attracted considerable research interest due to the prevalence and impact of falling people, especially the elderly. In this work, we analyze the capabilities of non-invasive thermal vision sensors to detect falls using several architectures of convolutional neural networks. First, we integrate two thermal vision sensors with different capabilities: (1) low resolution with a wide viewing angle and (2) high resolution with a central viewing angle. Second, we include fuzzy representation of thermal information. Third, we enable the generation of a large data set from a set of few images using ad hoc data augmentation, which increases the original data set size, generating new synthetic images. Fourth, we define three types of convolutional neural networks which are adapted for each thermal vision sensor in order to evaluate the impact of the architecture on fall detection performance. The results show encouraging performance in single-occupancy contexts. In multiple occupancy, the low-resolution thermal vision sensor with a wide viewing angle obtains better performance and reduction of learning time, in comparison with the high-resolution thermal vision sensors with a central viewing angle.

scholarly journals Convolutional Neural Networks enable efficient, accurate and fine-grained segmentation of plant species and communities from high-resolution UAV imagery

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Teja Kattenborn ◽  
Jana Eichel ◽  
Fabian Ewald Fassnacht
Keyword(s):  
Neural Networks ◽  
High Resolution ◽  
Convolutional Neural Networks ◽  
Low Cost ◽  
Training Data ◽  
Observation Data ◽  
Visual Interpretation ◽  
Fine Grained ◽  
Wide Range ◽  
Vegetation Species

AbstractRecent technological advances in remote sensing sensors and platforms, such as high-resolution satellite imagers or unmanned aerial vehicles (UAV), facilitate the availability of fine-grained earth observation data. Such data reveal vegetation canopies in high spatial detail. Efficient methods are needed to fully harness this unpreceded source of information for vegetation mapping. Deep learning algorithms such as Convolutional Neural Networks (CNN) are currently paving new avenues in the field of image analysis and computer vision. Using multiple datasets, we test a CNN-based segmentation approach (U-net) in combination with training data directly derived from visual interpretation of UAV-based high-resolution RGB imagery for fine-grained mapping of vegetation species and communities. We demonstrate that this approach indeed accurately segments and maps vegetation species and communities (at least 84% accuracy). The fact that we only used RGB imagery suggests that plant identification at very high spatial resolutions is facilitated through spatial patterns rather than spectral information. Accordingly, the presented approach is compatible with low-cost UAV systems that are easy to operate and thus applicable to a wide range of users.

scholarly journals Automatic Gully Detection: Neural Networks and Computer Vision

2020 ◽  
Vol 12 (11) ◽  
pp. 1743
Author(s):  
Artur M. Gafurov ◽  
Oleg P. Yermolayev
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Large Scale ◽  
Satellite Images ◽  
Gully Erosion ◽  
Training Data ◽  
Russian Plain ◽  
Data Set ◽  
High Resolution Satellite Images

Transition from manual (visual) interpretation to fully automated gully detection is an important task for quantitative assessment of modern gully erosion, especially when it comes to large mapping areas. Existing approaches to semi-automated gully detection are based on either object-oriented selection based on multispectral images or gully selection based on a probabilistic model obtained using digital elevation models (DEMs). These approaches cannot be used for the assessment of gully erosion on the territory of the European part of Russia most affected by gully erosion due to the lack of national large-scale DEM and limited resolution of open source multispectral satellite images. An approach based on the use of convolutional neural networks for automated gully detection on the RGB-synthesis of ultra-high resolution satellite images publicly available for the test region of the east of the Russian Plain with intensive basin erosion has been proposed and developed. The Keras library and U-Net architecture of convolutional neural networks were used for training. Preliminary results of application of the trained gully erosion convolutional neural network (GECNN) allow asserting that the algorithm performs well in detecting active gullies, well differentiates gullies from other linear forms of slope erosion — rills and balkas, but so far has errors in detecting complex gully systems. Also, GECNN does not identify a gully in 10% of cases and in another 10% of cases it identifies not a gully. To solve these problems, it is necessary to additionally train the neural network on the enlarged training data set.

Skin Cancer Classification with Deep Convolutional Neural Networks

2020 ◽  
Vol 10 (7) ◽  
pp. 1707-1713 ◽  
Author(s):  
Mingang Chen ◽  
Wenjie Chen ◽  
Wei Chen ◽  
Lizhi Cai ◽  
Gang Chai
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Skin Diseases ◽  
Survival Rates ◽  
Skin Lesions ◽  
Classification Model ◽  
Deep Convolutional Neural Networks ◽  
Image Preprocessing ◽  
Skin Cancers

Skin cancers are one of the most common cancers in the world. Early detections and treatments of skin cancers can greatly improve the survival rates of patients. In this paper, a skin lesions classification system is developed with deep convolutional neural networks of ResNet50, which may help dermatologists to recognize skin cancers earlier. We utilize the ResNet50 as a pre-trained model. Then, by transfer learning, it is trained on our skin lesions dataset. Image preprocessing and dataset balancing methods are used to increase the accuracy of the classification model. In classification of skin diseases, our model achieves an overall accuracy of 83.74% on nine-class skin lesions. The experimental results show an impressive effect of the ResNet50 model in finegrained skin lesions classification and skin cancers recognition.

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