scholarly journals DEEP LEARNING BASED SKIN LESIONS DIAGNOSIS

2019 ◽  
Vol XLII-2/W12 ◽  
pp. 81-85 ◽  
Author(s):  
D. A. Gavrilov ◽  
N. N. Shchelkunov ◽  
A. V. Melerzanov
Keyword(s):  
Neural Network ◽  
Classification Accuracy ◽  
Intelligent Systems ◽  
State Of The Art ◽  
Skin Lesions ◽  
Deep Convolutional Neural Networks ◽  
Melanoma Diagnosis ◽  
Malignant Skin ◽  
Diagnosis Accuracy ◽  
Visual Diagnosis

<p><strong>Abstract.</strong> Melanoma is one of the most virulent lesions of human’s skin. The visual diagnosis accuracy of melanoma directly depends on the doctor’s qualification and specialization. State-of-the-art solutions in the field of image processing and machine learning allows to create intelligent systems based on artificial convolutional neural network exceeding human’s rates in the field of object classification, including the case of malignant skin lesions. This paper presents an algorithm for the early melanoma diagnosis based on artificial deep convolutional neural networks. The algorithm proposed allows to reach the classification accuracy of melanoma at least 91%.</p>

scholarly journals Skin Cancer Diseases Classification using Deep Convolutional Neural Network with Transfer Learning Model

2021 ◽  
Vol 2128 (1) ◽  
pp. 012013
Author(s):  
Laila Moataz ◽  
Gouda I. Salama ◽  
Mohamed H. Abd Elazeem
Keyword(s):  
Neural Network ◽  
Skin Cancer ◽  
Classification Accuracy ◽  
Skin Lesions ◽  
Fine Tuning ◽  
Deep Convolutional Neural Networks ◽  
Proposed Model ◽  
Prior Models ◽  
Cancer Diseases

Abstract Skin cancer is becoming increasingly common. Fortunately, early discovery can greatly improve the odds of a patient being healed. Many Artificial Intelligence based approaches to classify skin lesions have recently been proposed. but these approaches suffer from limited classification accuracy. Deep convolutional neural networks show potential for better classification of cancer lesions. This paper presents a fine-tuning on Xception pretrained model for classification of skin lesions by adding a group of layers after the basic ones of the Xception model and all model weights are set to be trained. The model is fine-tuned over HAM10,000 dataset seven classes by augmentation approach to mitigate the data imbalance effect and conducted a comparative study with the most up to date approaches. In comparison to prior models, the results indicate that the proposed model is both efficient and reliable.

scholarly journals Multi-Channel Convolutional Neural Networks Architecture Feeding for Effective EEG Mental Tasks Classification

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3451 ◽  
Author(s):  
Sławomir Opałka ◽  
Bartłomiej Stasiak ◽  
Dominik Szajerman ◽  
Adam Wojciechowski
Keyword(s):  
Neural Network ◽  
Frequency Domain ◽  
Classification Accuracy ◽  
State Of The Art ◽  
Parameters Optimization ◽  
Alternative Methods ◽  
Signal Frequency ◽  
Eeg Signal ◽  
Mental Tasks Classification ◽  
Mental Tasks

Mental tasks classification is increasingly recognized as a major challenge in the field of EEG signal processing and analysis. State-of-the-art approaches face the issue of spatially unstable structure of highly noised EEG signals. To address this problem, this paper presents a multi-channel convolutional neural network architecture with adaptively optimized parameters. Our solution outperforms alternative methods in terms of classification accuracy of mental tasks (imagination of hand movements and speech sounds generation) while providing high generalization capability (∼5%). Classification efficiency was obtained by using a frequency-domain multi-channel neural network feeding scheme by EEG signal frequency sub-bands analysis and architecture supporting feature mapping with two subsequent convolutional layers terminated with a fully connected layer. For dataset V from BCI Competition III, the method achieved an average classification accuracy level of nearly 70%, outperforming alternative methods. The solution presented applies a frequency domain for input data processed by a multi-channel architecture that isolates frequency sub-bands in time windows, which enables multi-class signal classification that is highly generalizable and more accurate (∼1.2%) than the existing solutions. Such an approach, combined with an appropriate learning strategy and parameters optimization, adapted to signal characteristics, outperforms reference single- or multi-channel networks, such as AlexNet, VGG-16 and Cecotti’s multi-channel NN. With the classification accuracy improvement of 1.2%, our solution is a clear advance as compared to the top three state-of-the-art methods, which achieved the result of no more than 0.3%.

scholarly journals A Normalized Convolutional Neural Network for Guided Sparse Depth Upsampling

2018 ◽  
Author(s):  
Jiashen Hua ◽  
Xiaojin Gong
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Color Image ◽  
State Of The Art ◽  
Depth Map ◽  
Sparse Data ◽  
Optimal Choice ◽  
Deep Convolutional Neural Networks ◽  
Normalized Convolution ◽  
Indoor And Outdoor

Guided sparse depth upsampling aims to upsample an irregularly sampled sparse depth map when an aligned high-resolution color image is given as guidance. When deep convolutional neural networks (CNNs) become the optimal choice to many applications nowadays, how to deal with irregular and sparse data still remains a non-trivial problem. Inspired by the classical normalized convolution operation, this work proposes a normalized convolutional layer (NCL) implemented in CNNs. Sparse data are therefore explicitly considered in CNNs by the separation of both data and filters into a signal part and a certainty part. Based upon NCLs, we design a normalized convolutional neural network (NCNN) to perform guided sparse depth upsampling. Experiments on both indoor and outdoor datasets show that the proposed NCNN models achieve state-of-the-art upsampling performance. Moreover, the models using NCLs gain a great generalization ability to different sparsity levels.

scholarly journals COP: Customized Deep Model Compression via Regularized Correlation-Based Filter-Level Pruning

2019 ◽  
Author(s):  
Wenxiao Wang ◽  
Cong Fu ◽  
Jishun Guo ◽  
Deng Cai ◽  
Xiaofei He
Keyword(s):  
Neural Network ◽  
State Of The Art ◽  
Computational Cost ◽  
Cross Layer ◽  
Optimal Solutions ◽  
Deep Convolutional Neural Networks ◽  
Model Compression ◽  
Deep Model ◽  
Network Compression ◽  
Novel Algorithm

Neural network compression empowers the effective yet unwieldy deep convolutional neural networks (CNN) to be deployed in resource-constrained scenarios. Most state-of-the-art approaches prune the model in filter-level according to the "importance" of filters. Despite their success, we notice they suffer from at least two of the following problems: 1) The redundancy among filters is not considered because the importance is evaluated independently. 2) Cross-layer filter comparison is unachievable since the importance is defined locally within each layer. Consequently, we must manually specify layer-wise pruning ratios. 3) They are prone to generate sub-optimal solutions because they neglect the inequality between reducing parameters and reducing computational cost. Reducing the same number of parameters in different positions in the network may reduce different computational cost. To address the above problems, we develop a novel algorithm named as COP (correlation-based pruning), which can detect the redundant filters efficiently. We enable the cross-layer filter comparison through global normalization. We add parameter-quantity and computational-cost regularization terms to the importance, which enables the users to customize the compression according to their preference (smaller or faster). Extensive experiments have shown COP outperforms the others significantly. The code is released at https://github.com/ZJULearning/COP.

scholarly journals Training of Deep Convolutional Neural Networks to Identify Critical Liver Alterations in Histopathology Image Samples

2019 ◽  
Vol 10 (1) ◽  
pp. 42 ◽  
Author(s):  
Alexandros Arjmand ◽  
Constantinos T. Angelis ◽  
Vasileios Christou ◽  
Alexandros T. Tzallas ◽  
Markos G. Tsipouras ◽  
...  
Keyword(s):  
Neural Network ◽  
Classification Accuracy ◽  
Diagnostic Methods ◽  
Automated Classification ◽  
Hepatocellular Injury ◽  
Deep Convolutional Neural Networks ◽  
Total Occlusion ◽  
Nonalcoholic Fatty Liver ◽  
Discrimination Capability ◽  
Wide Range

Nonalcoholic fatty liver disease (NAFLD) is responsible for a wide range of pathological disorders. It is characterized by the prevalence of steatosis, which results in excessive accumulation of triglyceride in the liver tissue. At high rates, it can lead to a partial or total occlusion of the organ. In contrast, nonalcoholic steatohepatitis (NASH) is a progressive form of NAFLD, with the inclusion of hepatocellular injury and inflammation histological diseases. Since there is no approved pharmacotherapeutic solution for both conditions, physicians and engineers are constantly in search for fast and accurate diagnostic methods. The proposed work introduces a fully automated classification approach, taking into consideration the high discrimination capability of four histological tissue alterations. The proposed work utilizes a deep supervised learning method, with a convolutional neural network (CNN) architecture achieving a classification accuracy of 95%. The classification capability of the new CNN model is compared with a pre-trained AlexNet model, a visual geometry group (VGG)-16 deep architecture and a conventional multilayer perceptron (MLP) artificial neural network. The results show that the constructed model can achieve better classification accuracy than VGG-16 (94%) and MLP (90.3%), while AlexNet emerges as the most efficient classifier (97%).

scholarly journals Skin Lesions Classification Using Deep Learning Based on Dilated Convolution

2019 ◽  
Author(s):  
Md. Aminur Rab Ratul ◽  
M. Hamed Mozaffari ◽  
Won-Sook Lee ◽  
Enea Parimbelli
Keyword(s):  
Deep Learning ◽  
Classification Accuracy ◽  
Detection System ◽  
Skin Lesions ◽  
Computer Aided Detection ◽  
Dilated Convolution ◽  
High Classification Accuracy ◽  
Malignant Skin ◽  
Improved Accuracy ◽  
Surrounding Skin

AbstractThe prediction of skin lesions is a challenging task even for experienced dermatologists due to a little contrast between surrounding skin and lesions, the visual resemblance between skin lesions, fuddled lesion border, etc. An automated computer-aided detection system with given images can help clinicians to prognosis malignant skin lesions at the earliest time. Recent progress in deep learning includes dilated convolution known to have improved accuracy with the same amount of computational complexities compared to traditional CNN. To implement dilated convolution, we choose the transfer learning with four popular architectures: VGG16, VGG19, MobileNet, and InceptionV3. The HAM10000 dataset was utilized for training, validating, and testing, which contains a total of 10015 dermoscopic images of seven skin lesion classes with huge class imbalances. The top-1 accuracy achieved on dilated versions of VGG16, VGG19, MobileNet, and InceptionV3 is 87.42%, 85.02%, 88.22%, and 89.81%, respectively. Dilated InceptionV3 exhibited the highest classification accuracy, recall, precision, and f-1 score and dilated MobileNet also has high classification accuracy while having the lightest computational complexities. Dilated InceptionV3 achieved better overall and per-class accuracy than any known methods on skin lesions classification to the best of our knowledge while experimenting with a complex open-source dataset with class imbalances.

scholarly journals Logo Recognition with the Use of Deep Convolutional Neural Networks

2020 ◽  
Vol 10 (5) ◽  
pp. 6191-6194
Author(s):  
A. Alsheikhy ◽  
Y. Said ◽  
M. Barr
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Object Recognition ◽  
Neural Network Model ◽  
State Of The Art ◽  
Original Data ◽  
Deep Convolutional Neural Networks ◽  
Convolutional Networks ◽  
Computational Overhead ◽  
Logo Recognition

Automatic logo recognition is gaining importance due to the increasing number of its applications. Unlike other object recognition tasks, logo recognition is more challenging because of the limited amount of the available original data. In this paper, the transfer leaning technique was applied to a Deep Convolutional Neural Network model to guarantee logo recognition using a small computational overhead. The proposed method was based on the Densely Connected Convolutional Networks (DenseNet). The experimental results show that for the FlickrLogos-32 logo recognition dataset, our proposed method performs comparably with state-of-the-art methods while using fewer parameters.

scholarly journals Classification Of Skin Lesions By Topological Data Analysis Alongside With Neural Network

2020 ◽  
Author(s):  
Naiereh Elyasi ◽  
mehdi hosseini moghadam
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Data Analysis ◽  
Skin Diseases ◽  
Persistent Homology ◽  
Skin Lesions ◽  
Topological Data Analysis ◽  
Deep Convolutional Neural Networks ◽  
Classification Result

In this paper we use TDA mapper alongside with deep convolutional neural networks in the classification of 7 major skin diseases. First we apply kepler mapper with neural network as one of its filter steps to classify the dataset HAM10000. Mapper visualizes the classification result by a simplicial complex, where neural network can not do this alone, but as a filter step neural network helps to classify data better. Furthermore we apply TDA mapper and persistent homology to understand the weights of layers of mobilenet network in different training epochs of HAM10000. Also we use persistent diagrams to visualize the results of analysis of layers of mobilenet network.

scholarly journals Garments Texture Design Class Identification Using Deep Convolutional Neural Network

2016 ◽  
Author(s):  
S.M. Sofiqul Islam ◽  
Emon Kumar Dey ◽  
Md. Nurul Ahad Tawhid ◽  
B. M. Mainul Hossain
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Object Recognition ◽  
Online Shopping ◽  
State Of The Art ◽  
Deep Convolutional Neural Networks ◽  
Deep Cnn ◽  
Class Identification ◽  
Multiple Levels ◽  
Texture Design

Automatic garments design class identification for recommending the fashion trends is important nowadays because of the rapid growth of online shopping. By learning the properties of images efficiently, a machine can give better accuracy of classification. Several methods, based on Hand-Engineered feature coding exist for identifying garments design classes. But, most of the time, those methods do not help to achieve better results. Recently, Deep Convolutional Neural Networks (CNNs) have shown better performances for different object recognition. Deep CNN uses multiple levels of representation and abstraction that helps a machine to understand the types of data (images, sound, and text) more accurately. In this paper, we have applied deep CNN for identifying garments design classes. To evaluate the performances, we used two well-known CNN models AlexNet and VGGNet on two different datasets. We also propose a new CNN model based on AlexNet and found better results than existing state-of-the-art by a significant margin.

scholarly journals Intelligent Systems to Predict and Diagnose Benign and Malignant Skin Lesions

2020 ◽  
Author(s):  
Geetha C ◽  
Aparna Darapaneni ◽  
Lakkamaneni Chandana Manaswini
Keyword(s):  
Image Processing ◽  
Health Informatics ◽  
Intelligent Systems ◽  
Skin Lesions ◽  
Improve Quality ◽  
Image Processing Techniques ◽  
Malignant Skin ◽  
Processing Software ◽  
Image Processing Software ◽  
Processing Techniques

The main purpose of Intelligent systems is to reason, calculate and perceive relationships and analogies. These Intelligent systems learn from experience and retrieve information from memory and provide the same to the users based onss their requirement. Currently, there is a trend for the use of intelligent systems in health informatics. The main objective of this is to improve quality, efficiency and availability of health services to people round the clock at a lower cost. Intelligent systems aim to predict and diagnose the skin cancer and abrasions based on their images. It understands the cause and thereby analyses the image based on some of the image processing techniques like patterns, anisotropic diffusion, image editing, independent component analysis and image restoration. We make use of image processing software which captures the image and then converts it to digital form and perform the required manipulations.

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