scholarly journals A Study on Multiple Factors Affecting the Accuracy of Multiclass Skin Disease Classification

2021 ◽  
Vol 11 (17) ◽  
pp. 7929
Author(s):  
Jiayi Fan ◽  
Jongwook Kim ◽  
Insu Jung ◽  
Yongkeun Lee
Keyword(s):  
Transfer Learning ◽  
Skin Disease ◽  
Classification Accuracy ◽  
Data Augmentation ◽  
Skin Diseases ◽  
Disease Classification ◽  
Batch Size ◽  
Deep Convolutional Neural Networks ◽  
Multiple Factors ◽  
Augmentation Techniques

Diagnosis of skin diseases by human experts is a laborious task prone to subjective judgment. Aided by computer technology and machine learning, it is possible to improve the efficiency and robustness of skin disease classification. Deep transfer learning using off-the-shelf deep convolutional neural networks (CNNs) has huge potential in the automation of skin disease classification tasks. However, complicated architectures seem to be too heavy for the classification of only a few skin disease classes. In this paper, in order to study potential ways to improve the classification accuracy of skin diseases, multiple factors are investigated. First, two different off-the-shelf architectures, namely AlexNet and ResNet50, are evaluated. Then, approaches using either transfer learning or trained from scratch are compared. In order to reduce the complexity of the network, the effects of shortening the depths of deep CNNs are investigated. Furthermore, different data augmentation techniques based on basic image manipulation are compared. Finally, the choice of mini-batch size is studied. Experiments were carried out on the HAM10000 skin disease dataset. The results show that the ResNet50-based model is more accurate than the AlexNet-based model. The transferred knowledge from the ImageNet database helps to improve the accuracy of the model. The reduction in stages of the ResNet50-based model can reduce complexity while maintaining good accuracy. Additionally, the use of different types of data augmentation techniques and the choice of mini-batch size can also affect the classification accuracy of skin diseases.

scholarly journals Multi skin lesions classification using fine-tuning and data-augmentation applying NASNet

2021 ◽  
Vol 7 ◽  
pp. e371
Author(s):  
Elia Cano ◽  
José Mendoza-Avilés ◽  
Mariana Areiza ◽  
Noemi Guerra ◽  
José Longino Mendoza-Valdés ◽  
...  
Keyword(s):  
Cross Validation ◽  
Data Augmentation ◽  
Skin Diseases ◽  
Medical Image Analysis ◽  
Skin Lesions ◽  
Disease Classification ◽  
Fine Tuning ◽  
Skin Cancer Prevention ◽  
Skin Imaging ◽  
Multi Class Classification

Skin lesions are one of the typical symptoms of many diseases in humans and indicative of many types of cancer worldwide. Increased risks caused by the effects of climate change and a high cost of treatment, highlight the importance of skin cancer prevention efforts like this. The methods used to detect these diseases vary from a visual inspection performed by dermatologists to computational methods, and the latter has widely used automatic image classification applying Convolutional Neural Networks (CNNs) in medical image analysis in the last few years. This article presents an approach that uses CNNs with a NASNet architecture to recognize in a more accurate way, without segmentation, eight skin diseases. The model was trained end-to-end on Keras with augmented skin diseases images from the International Skin Imaging Collaboration (ISIC). The CNN architectures were initialized with weight from ImageNet, fine-tuned in order to discriminate well among the different types of skin lesions, and then 10-fold cross-validation was applied. Finally, some evaluation metrics are calculated as accuracy, sensitivity, and specificity and compare with other CNN trained architectures. This comparison shows that the proposed system offers higher accuracy results, with a significant reduction on the training paraments. To the best of our knowledge and based in the state-of-art recompiling in this work, the application of the NASNet architecture training with skin image lesion from ISIC archive for multi-class classification and evaluated by cross-validation, represents a novel skin disease classification system.

scholarly journals Improving Image-Based Plant Disease Classification With Generative Adversarial Network Under Limited Training Set

2020 ◽  
Vol 11 ◽  
Author(s):  
Luning Bi ◽  
Guiping Hu
Keyword(s):  
Classification Accuracy ◽  
Plant Disease ◽  
Data Augmentation ◽  
Plant Diseases ◽  
Disease Classification ◽  
Training Data ◽  
Training Dataset ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Overfitting Problem

Traditionally, plant disease recognition has mainly been done visually by human. It is often biased, time-consuming, and laborious. Machine learning methods based on plant leave images have been proposed to improve the disease recognition process. Convolutional neural networks (CNNs) have been adopted and proven to be very effective. Despite the good classification accuracy achieved by CNNs, the issue of limited training data remains. In most cases, the training dataset is often small due to significant effort in data collection and annotation. In this case, CNN methods tend to have the overfitting problem. In this paper, Wasserstein generative adversarial network with gradient penalty (WGAN-GP) is combined with label smoothing regularization (LSR) to improve the prediction accuracy and address the overfitting problem under limited training data. Experiments show that the proposed WGAN-GP enhanced classification method can improve the overall classification accuracy of plant diseases by 24.4% as compared to 20.2% using classic data augmentation and 22% using synthetic samples without LSR.

scholarly journals A Review of Skin Disease Classification Techniques based on Machine Learning

2021 ◽  
Vol 9 (8) ◽  
pp. 1097-1101
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Skin Cancer ◽  
Basal Cell Carcinoma ◽  
Actinic Keratosis ◽  
Skin Disease ◽  
Skin Diseases ◽  
Skin Condition ◽  
Disease Classification ◽  
Vascular Lesions

Dermatology is one of the most unpredictable and difficult field to diagnose. In this field, more tests are needed to be carried out so as to decide the skin condition the patient may be facing. The time to diagnose may vary according to the different dermatologist. Machine learning and image processing can be used to efficiently detect the skin diseases. There are seven different categories of skin cancer- melanocytic nevi, melanoma, benign keratosis, Basal cell carcinoma, actinic keratosis, vascular lesions and dermatofibroma. The purpose of this review is to outline types, diagnosis, methodology and treatment of skin cancer.

Effective Data Augmentation and Training Techniques for Improving Deep Learning in Plant Leaf Disease Recognition

2021 ◽  
Author(s):  
Prem Enkvetchakul ◽  
Olarik Surinta
Keyword(s):  
Plant Disease ◽  
Data Augmentation ◽  
Recognition System ◽  
Deep Convolutional Neural Networks ◽  
Plant Leaf ◽  
Training Techniques ◽  
Leaf Disease ◽  
Training Technique ◽  
Augmentation Techniques ◽  
And Training

Plant disease is the most common problem in agriculture. Usually, the symptoms appear on leaves of the plants which allow farmers to diagnose and prevent the disease from spreading to other areas. An accurate and consistent plant disease recognition system can help prevent the spread of diseases and save maintenance costs. In this research, we present a plant leaf disease recognition system using two deep convolutional neural networks (CNNs); MobileNetV2 and NasNetMobile. These CNN architectures are designed to be suitable for smartphones due to the models being small. We have experimented on training techniques; online, offline, and mixed training techniques on two plant leaf diseases. As for data augmentation techniques, we found that the combination of rotation, shift, and zoom techniques significantly increases the performance of the CNN architectures. The experimental results show that the most accurate algorithm for plant leaf disease recognition is NASNetMobile architecture using transfer learning. Additionally, the most accurate result is obtained when combining the offline training technique with data augmentation techniques.

scholarly journals Efficient-CapsNet: capsule network with self-attention routing

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Vittorio Mazzia ◽  
Francesco Salvetti ◽  
Marcello Chiaberge
Keyword(s):  
Architectural Design ◽  
Data Augmentation ◽  
Routing Algorithm ◽  
Affine Transformations ◽  
Design Strategies ◽  
Feature Maps ◽  
Deep Convolutional Neural Networks ◽  
Convolutional Networks ◽  
Use Of Data ◽  
Augmentation Techniques

AbstractDeep convolutional neural networks, assisted by architectural design strategies, make extensive use of data augmentation techniques and layers with a high number of feature maps to embed object transformations. That is highly inefficient and for large datasets implies a massive redundancy of features detectors. Even though capsules networks are still in their infancy, they constitute a promising solution to extend current convolutional networks and endow artificial visual perception with a process to encode more efficiently all feature affine transformations. Indeed, a properly working capsule network should theoretically achieve higher results with a considerably lower number of parameters count due to intrinsic capability to generalize to novel viewpoints. Nevertheless, little attention has been given to this relevant aspect. In this paper, we investigate the efficiency of capsule networks and, pushing their capacity to the limits with an extreme architecture with barely 160 K parameters, we prove that the proposed architecture is still able to achieve state-of-the-art results on three different datasets with only 2% of the original CapsNet parameters. Moreover, we replace dynamic routing with a novel non-iterative, highly parallelizable routing algorithm that can easily cope with a reduced number of capsules. Extensive experimentation with other capsule implementations has proved the effectiveness of our methodology and the capability of capsule networks to efficiently embed visual representations more prone to generalization.

scholarly journals Diagnostic Approach for Accurate Diagnosis of COVID-19 Employing Deep Learning and Transfer Learning Techniques through Chest X-ray Images Clinical Data in E-Healthcare

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8219
Author(s):  
Amin Ul Haq ◽  
Jian Ping Li ◽  
Sultan Ahmad ◽  
Shakir Khan ◽  
Mohammed Ali Alshara ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Classification Accuracy ◽  
High Performance ◽  
Data Augmentation ◽  
Early Stage ◽  
Accurate Diagnosis ◽  
Fine Tuning ◽  
Data Set ◽  
X Ray ◽  
Chest X Ray

COVID-19 is a transferable disease that is also a leading cause of death for a large number of people worldwide. This disease, caused by SARS-CoV-2, spreads very rapidly and quickly affects the respiratory system of the human being. Therefore, it is necessary to diagnosis this disease at the early stage for proper treatment, recovery, and controlling the spread. The automatic diagnosis system is significantly necessary for COVID-19 detection. To diagnose COVID-19 from chest X-ray images, employing artificial intelligence techniques based methods are more effective and could correctly diagnosis it. The existing diagnosis methods of COVID-19 have the problem of lack of accuracy to diagnosis. To handle this problem we have proposed an efficient and accurate diagnosis model for COVID-19. In the proposed method, a two-dimensional Convolutional Neural Network (2DCNN) is designed for COVID-19 recognition employing chest X-ray images. Transfer learning (TL) pre-trained ResNet-50 model weight is transferred to the 2DCNN model to enhanced the training process of the 2DCNN model and fine-tuning with chest X-ray images data for final multi-classification to diagnose COVID-19. In addition, the data augmentation technique transformation (rotation) is used to increase the data set size for effective training of the R2DCNNMC model. The experimental results demonstrated that the proposed (R2DCNNMC) model obtained high accuracy and obtained 98.12% classification accuracy on CRD data set, and 99.45% classification accuracy on CXI data set as compared to baseline methods. This approach has a high performance and could be used for COVID-19 diagnosis in E-Healthcare systems.

scholarly journals ROI-based features for classification of skin diseases using a multi-layer neural network

2021 ◽  
Vol 23 (1) ◽  
pp. 216
Author(s):  
Thanh-Hai Nguyen ◽  
Ba-Viet Ngo
Keyword(s):  
Skin Disease ◽  
Classification Accuracy ◽  
Skin Diseases ◽  
Human Life ◽  
Disease Diagnosis ◽  
Classification Performance ◽  
High Classification Accuracy ◽  
Separate Region ◽  
Occurrence Matrix

<p>Skin diseases have a serious impact on human life and health. This article aims to represent the classification accuracy of skin diseases for supporting the physicians’ correct decision on patients for early treatment. In particular, 100 images in each type of five skin diseases from ISIC database are used for balanced datasets related to the classification accuracy. In addition, this paper focuses on processing images for extracting six optimal types of eleven features of skin disease image for higher classification performance and also this takes less time for training. Therefore, skin disease images are filtered and segmented for separating region of interests (ROIs) before extracting optimal features. First, the skin disease images are processed by normalizing sizes, removing noises, segmenting to separate region of interests (ROIs) showing skin disease signs. Next, a gray-level co-occurrence matrix (GLCM) method is applied for texture analysis to extract eleven features. With the optimal six features chosen, the high classification accuracy of skin diseases is about 92% evaluated using a matrix confusion. The result showed to illustrate the effectiveness of the proposed method. Furthermore, this method can be developed for other medical datasets for supporting in disease diagnosis.</p>

scholarly journals Augmentasi Data Pengenalan Citra Mobil Menggunakan Pendekatan Random Crop, Rotate, dan Mixup

2020 ◽  
Vol 6 (2) ◽  
Author(s):  
Joseph Sanjaya ◽  
Mewati Ayub
Keyword(s):  
Data Augmentation ◽  
Learning System ◽  
Training Data ◽  
Two Dimensional ◽  
Deep Convolutional Neural Networks ◽  
Image Detection ◽  
Geometric Transformations ◽  
Car Model ◽  
Augmentation Techniques ◽  
Significant Generalization

Deep convolutional neural networks (CNNs) have achieved remarkable results in two-dimensional (2D) image detection tasks. However, their high expression ability risks overfitting. Consequently, data augmentation techniques have been proposed to prevent overfitting while enriching datasets. In this paper, a Deep Learning system for accurate car model detection is proposed using the ResNet-152 network with a fully convolutional architecture. It is demonstrated that significant generalization gains in the learning process are attained by randomly generating augmented training data using several geometric transformations and pixel-wise changes, such as image cropping and image rotation. We evaluated data augmentation techniques by comparison with competitive data augmentation techniques such as mixup. Data augmented ResNet models achieve better results for accuracy metrics than baseline ResNet models with accuracy 82.6714% on Stanford Cars Dataset.

scholarly journals A Deep Transfer Learning Model with Classical Data Augmentation and CGAN to Detect COVID-19 from Chest CT Radiography Digital Images

2020 ◽  
Author(s):  
Mohamed Loey ◽  
Florentin Smarandache ◽  
Nour Eldeen M. Khalifa
Keyword(s):  
Transfer Learning ◽  
Data Augmentation ◽  
Digital Images ◽  
Main Idea ◽  
Learning Model ◽  
Chest Ct ◽  
Benchmark Datasets ◽  
Augmentation Techniques ◽  
Testing Accuracy ◽  
Chest Ct Scan

The coronavirus disease 2019 (COVID-19) is the fastest transmittable virus caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The detection of COVID-19 using artificial intelligence techniques and especially deep learning will help to detect this virus in early stages which will reflect in increasing the opportunities of fast recovery of patients worldwide. This will lead to release the pressure off the healthcare system around the world. In this research, classical data augmentation techniques along with CGAN based on a deep transfer learning model for COVID-19 detection in chest CT scan images will be presented. The limited benchmark datasets for covid-19 especially in chest CT images is the main motivation of this research. The main idea is to collect all the possible images for covid-19 that exists until the very writing of this research and use the classical data augmentations along with CGAN to generate more images to help in the detection of the COVID-19. In this study, five different deep convolutional neural network-based models (AlexNet, VGGNet16, VGGNet19, GoogleNet, and ResNet50) have been selected for the investigation to detect the coronavirus infected patient using chest CT radiographs digital images. The classical data augmentations along with CGAN improve the performance of classification in all selected deep transfer models. The Outcomes show that ResNet50 is the most appropriate classifier to detect the COVID-19 from chest CT dataset using the classical data augmentation and CGAN with testing accuracy of 82.91%.

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