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

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.

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"LeukNet" - A Model of Convolutional Neural Network for the Diagnosis of Leukemia

10.5753/sibgrapi.est.2020.12993 ◽

2020 ◽

Author(s):

Luis H. S. Vogado ◽

Rodrigo M. S. Veras ◽

Kelson R. T. Aires

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Cross Validation ◽

Data Augmentation ◽

Fine Tuning ◽

Current State ◽

Leukemia Diagnosis ◽

New Guidelines ◽

Texture Contrast ◽

Tuning Methods

Leukemia is a disorder that affects the bone marrow, causing uncontrolled production of leukocytes, impairing the transport of oxygen and causing blood coagulation problems. In this article, we propose a new computational tool, named LeukNet, a Convolutional Neural Network (CNN) architecture based on the VGG-16 convolutional blocks, to facilitate the leukemia diagnosis from blood smear images. We evaluated different architectures and fine-tuning methods using 18 datasets containing 3536 images with distinct characteristics of color, texture, contrast, and resolution. Additionally, data augmentation operations were applied to increase the training set by up to 20 times. The k-fold cross-validation (k = 5) results achieved 98.28% of accuracy. A cross-dataset validation technique, named LeaveOne-Dataset-Out Cross-Validation (LODOCV), is also proposed to evaluate the developed model’s generalization capability. The accuracy of using LODOCV on the ALL-IDB 1, ALL-IDB 2, and UFG datasets was 97.04%, 82.46%, and 70.24%, respectively, overcoming the current state-of-the-art results and offering new guidelines for image-based computer-aided diagnosis (CAD) systems in this area.

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Deep learning model for Pathogen Classification using Feature fusion and Data augmentation

Current Bioinformatics ◽

10.2174/1574893615999200707143535 ◽

2020 ◽

Vol 15 ◽

Author(s):

Fareed Ahmad ◽

Amjad Farooq ◽

Muhammad Usman Ghani Khan

Keyword(s):

Deep Learning ◽

Cross Validation ◽

Data Augmentation ◽

Feature Vector ◽

Feature Fusion ◽

High Capacity ◽

Automatic Classification ◽

Fine Tuning ◽

Correct Identification ◽

Learning Models

Background: Bacterial pathogens are deadly for animals and humans. The ease of their dissemination, coupled with their high capacity for ailment and death in infected individuals, makes them a threat to society. Objective: Due to high similarity among genera and species of pathogens, it is sometimes difficult for microbiologists to differentiate between them. Their automatic classification using deep-learning models can help in reliable, and accurate outcomes. Method: Deep-learning models, namely; AlexNet, GoogleNet, ResNet101, and InceptionV3 are used with numerous variations including training model from scratch, fine-tuning without pre-trained weights, fine-tuning along with freezing weights of initial layers, fine-tuning along with adjusting weights of all layers and augmenting the dataset by random translation and reflection. Moreover, as the dataset is small, fine-tuning and data augmentation strategies are applied to avoid overfitting and produce a generalized model. A merged feature vector is produced using two best-performing models and accuracy is calculated by xgboost algorithm on the feature vector by applying cross-validation. Results: Fine-tuned models where augmentation is applied produces the best results. Out of these, two-best-performing deep models i.e. (ResNet101, and InceptionV3) selected for feature fusion, produced a similar validation accuracy of 95.83 with a loss of 0.0213 and 0.1066, and a testing accuracy of 97.92 and 93.75, respectively. The proposed model used xgboost to attained a classification accuracy of 98.17% by using 35-folds cross-validation. Conclusion: The automatic classification using these models can help experts in the correct identification of pathogens. Consequently, they can help in controlling epidemics and thereby minimizing the socio-economic impact on the community.

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An Intelligent Computer-Aided Scheme for Classifying Multiple Skin Lesions

Computers ◽

10.3390/computers8030062 ◽

2019 ◽

Vol 8 (3) ◽

pp. 62 ◽

Cited By ~ 8

Author(s):

Nazia Hameed ◽

Fozia Hameed ◽

Antesar Shabut ◽

Sehresh Khan ◽

Silvia Cirstea ◽

...

Keyword(s):

Classification Scheme ◽

Skin Diseases ◽

Research Work ◽

Skin Lesions ◽

Disease Classification ◽

Machine Learning Techniques ◽

Support Vector ◽

Classification Framework ◽

Computer Aided

Skin diseases cases are increasing on a daily basis and are difficult to handle due to the global imbalance between skin disease patients and dermatologists. Skin diseases are among the top 5 leading cause of the worldwide disease burden. To reduce this burden, computer-aided diagnosis systems (CAD) are highly demanded. Single disease classification is the major shortcoming in the existing work. Due to the similar characteristics of skin diseases, classification of multiple skin lesions is very challenging. This research work is an extension of our existing work where a novel classification scheme is proposed for multi-class classification. The proposed classification framework can classify an input skin image into one of the six non-overlapping classes i.e., healthy, acne, eczema, psoriasis, benign and malignant melanoma. The proposed classification framework constitutes four steps, i.e., pre-processing, segmentation, feature extraction and classification. Different image processing and machine learning techniques are used to accomplish each step. 10-fold cross-validation is utilized, and experiments are performed on 1800 images. An accuracy of 94.74% was achieved using Quadratic Support Vector Machine. The proposed classification scheme can help patients in the early classification of skin lesions.

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Maize leaf disease classification using convolutional neural networks and hyperparameter optimization

10.5753/wvc.2020.13489 ◽

2020 ◽

Author(s):

Erik Lucas Da Rocha ◽

Larissa Rodrigues ◽

João Fernando Mari

Keyword(s):

Computer Vision ◽

Data Augmentation ◽

Disease Classification ◽

Fine Tuning ◽

Hyperparameter Optimization ◽

Maize Leaf ◽

Leaf Disease ◽

Agricultural Applications ◽

Validation Procedure ◽

The Impact

Maize is an important food crop in the world, but several diseases affect the quality and quantity of agricultural production. Identifying these diseases is a very subjective and time-consuming task. The use of computer vision techniques allows automatizing this task and is essential in agricultural applications. In this study, we assess the performance of three state-of-the-art convolutional neural network architectures to classify maize leaf diseases. We apply enhancement methods such as Bayesian hyperparameter optimization, data augmentation, and fine-tuning strategies. We evaluate these CNNs on the maize leaf images from PlantVillage dataset, and all experiments were validated using a five-fold cross-validation procedure over the training and test sets. Our findings include the correlation between the maize leaf classes and the impact of data augmentation in pre-trained models. The results show that maize leaf disease classification reached 97% of accuracy for all CNNs models evaluated. Also, our approach provides new perspectives for the identification of leaf diseases based on computer vision strategies.

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

Applied Sciences ◽

10.3390/app11177929 ◽

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.

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A multilayer multimodal detection and prediction model based on explainable artificial intelligence for Alzheimer’s disease

Scientific Reports ◽

10.1038/s41598-021-82098-3 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Shaker El-Sappagh ◽

Jose M. Alonso ◽

S. M. Riazul Islam ◽

Ahmad M. Sultan ◽

Kyung Sup Kwak

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Cross Validation ◽

Disease Risk ◽

Binary Classification ◽

Fuzzy Rule ◽

Large Set ◽

Detection Model ◽

Multi Class Classification ◽

Clinical Measures

AbstractAlzheimer’s disease (AD) is the most common type of dementia. Its diagnosis and progression detection have been intensively studied. Nevertheless, research studies often have little effect on clinical practice mainly due to the following reasons: (1) Most studies depend mainly on a single modality, especially neuroimaging; (2) diagnosis and progression detection are usually studied separately as two independent problems; and (3) current studies concentrate mainly on optimizing the performance of complex machine learning models, while disregarding their explainability. As a result, physicians struggle to interpret these models, and feel it is hard to trust them. In this paper, we carefully develop an accurate and interpretable AD diagnosis and progression detection model. This model provides physicians with accurate decisions along with a set of explanations for every decision. Specifically, the model integrates 11 modalities of 1048 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) real-world dataset: 294 cognitively normal, 254 stable mild cognitive impairment (MCI), 232 progressive MCI, and 268 AD. It is actually a two-layer model with random forest (RF) as classifier algorithm. In the first layer, the model carries out a multi-class classification for the early diagnosis of AD patients. In the second layer, the model applies binary classification to detect possible MCI-to-AD progression within three years from a baseline diagnosis. The performance of the model is optimized with key markers selected from a large set of biological and clinical measures. Regarding explainability, we provide, for each layer, global and instance-based explanations of the RF classifier by using the SHapley Additive exPlanations (SHAP) feature attribution framework. In addition, we implement 22 explainers based on decision trees and fuzzy rule-based systems to provide complementary justifications for every RF decision in each layer. Furthermore, these explanations are represented in natural language form to help physicians understand the predictions. The designed model achieves a cross-validation accuracy of 93.95% and an F1-score of 93.94% in the first layer, while it achieves a cross-validation accuracy of 87.08% and an F1-Score of 87.09% in the second layer. The resulting system is not only accurate, but also trustworthy, accountable, and medically applicable, thanks to the provided explanations which are broadly consistent with each other and with the AD medical literature. The proposed system can help to enhance the clinical understanding of AD diagnosis and progression processes by providing detailed insights into the effect of different modalities on the disease risk.

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Deep Learning-Based Differentiation between Mucinous Cystic Neoplasm and Serous Cystic Neoplasm in the Pancreas Using Endoscopic Ultrasonography

Diagnostics ◽

10.3390/diagnostics11061052 ◽

2021 ◽

Vol 11 (6) ◽

pp. 1052

Author(s):

Leang Sim Nguon ◽

Kangwon Seo ◽

Jung-Hyun Lim ◽

Tae-Jun Song ◽

Sung-Hyun Cho ◽

...

Keyword(s):

Decision Making ◽

Deep Learning ◽

Network Model ◽

Endoscopic Ultrasonography ◽

Data Augmentation ◽

Clinical Information ◽

Training Data ◽

Fine Tuning ◽

Cystic Neoplasm ◽

Cystic Neoplasms

Mucinous cystic neoplasms (MCN) and serous cystic neoplasms (SCN) account for a large portion of solitary pancreatic cystic neoplasms (PCN). In this study we implemented a convolutional neural network (CNN) model using ResNet50 to differentiate between MCN and SCN. The training data were collected retrospectively from 59 MCN and 49 SCN patients from two different hospitals. Data augmentation was used to enhance the size and quality of training datasets. Fine-tuning training approaches were utilized by adopting the pre-trained model from transfer learning while training selected layers. Testing of the network was conducted by varying the endoscopic ultrasonography (EUS) image sizes and positions to evaluate the network performance for differentiation. The proposed network model achieved up to 82.75% accuracy and a 0.88 (95% CI: 0.817–0.930) area under curve (AUC) score. The performance of the implemented deep learning networks in decision-making using only EUS images is comparable to that of traditional manual decision-making using EUS images along with supporting clinical information. Gradient-weighted class activation mapping (Grad-CAM) confirmed that the network model learned the features from the cyst region accurately. This study proves the feasibility of diagnosing MCN and SCN using a deep learning network model. Further improvement using more datasets is needed.

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Adversarial Data Augmentation on Breast MRI Segmentation

Applied Sciences ◽

10.3390/app11104554 ◽

2021 ◽

Vol 11 (10) ◽

pp. 4554

Author(s):

João F. Teixeira ◽

Mariana Dias ◽

Eva Batista ◽

Joana Costa ◽

Luís F. Teixeira ◽

...

Keyword(s):

Data Augmentation ◽

Medical Image Analysis ◽

Synthetic Data ◽

Breast Mri ◽

Semantic Segmentation ◽

Adversarial Networks ◽

Augmentation Strategy ◽

Magnetic Resonance Imaging Mri ◽

Segmentation Task ◽

The Impact

The scarcity of balanced and annotated datasets has been a recurring problem in medical image analysis. Several researchers have tried to fill this gap employing dataset synthesis with adversarial networks (GANs). Breast magnetic resonance imaging (MRI) provides complex, texture-rich medical images, with the same annotation shortage issues, for which, to the best of our knowledge, no previous work tried synthesizing data. Within this context, our work addresses the problem of synthesizing breast MRI images from corresponding annotations and evaluate the impact of this data augmentation strategy on a semantic segmentation task. We explored variations of image-to-image translation using conditional GANs, namely fitting the generator’s architecture with residual blocks and experimenting with cycle consistency approaches. We studied the impact of these changes on visual verisimilarity and how an U-Net segmentation model is affected by the usage of synthetic data. We achieved sufficiently realistic-looking breast MRI images and maintained a stable segmentation score even when completely replacing the dataset with the synthetic set. Our results were promising, especially when concerning to Pix2PixHD and Residual CycleGAN architectures.

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Gomisin M2 Ameliorates Atopic Dermatitis-like Skin Lesions via Inhibition of STAT1 and NF-κB Activation in 2,4-Dinitrochlorobenzene/Dermatophagoides farinae Extract-Induced BALB/c Mice

Molecules ◽

10.3390/molecules26154409 ◽

2021 ◽

Vol 26 (15) ◽

pp. 4409

Author(s):

Jinjoo Kang ◽

Soyoung Lee ◽

Namkyung Kim ◽

Hima Dhakal ◽

Taeg-Kyu Kwon ◽

...

Keyword(s):

Atopic Dermatitis ◽

Oral Administration ◽

Skin Diseases ◽

Nuclear Translocation ◽

Skin Lesions ◽

Biologically Active ◽

Therapeutic Effects ◽

Dermatophagoides Farinae ◽

Tnf Α ◽

Ifn Γ

The extracts of Schisandra chinensis (Turcz.) Baill. (Schisandraceae) have various therapeutic effects, including inflammation and allergy. In this study, gomisin M2 (GM2) was isolated from S. chinensis and its beneficial effects were assessed against atopic dermatitis (AD). We evaluated the therapeutic effects of GM2 on 2,4-dinitrochlorobenzene (DNCB) and Dermatophagoides farinae extract (DFE)-induced AD-like skin lesions with BALB/c mice ears and within the tumor necrosis factor (TNF)-α and interferon (IFN)-γ-stimulated keratinocytes. The oral administration of GM2 resulted in reduced epidermal and dermal thickness, infiltration of tissue eosinophils, mast cells, and helper T cells in AD-like lesions. GM2 suppressed the expression of IL-1β, IL-4, IL-5, IL-6, IL-12a, and TSLP in ear tissue and the expression of IFN-γ, IL-4, and IL-17A in auricular lymph nodes. GM2 also inhibited STAT1 and NF-κB phosphorylation in DNCB/DFE-induced AD-like lesions. The oral administration of GM2 reduced levels of IgE (DFE-specific and total) and IgG2a in the mice sera, as well as protein levels of IL-4, IL-6, and TSLP in ear tissues. In TNF-α/IFN-γ-stimulated keratinocytes, GM2 significantly inhibited IL-1β, IL-6, CXCL8, and CCL22 through the suppression of STAT1 phosphorylation and the nuclear translocation of NF-κB. Taken together, these results indicate that GM2 is a biologically active compound that exhibits inhibitory effects on skin inflammation and suggests that GM2 might serve as a remedy in inflammatory skin diseases, specifically on AD.

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Prevalence and risk of appearence of skin lesions considered to be cutaneous markers of diabetes in a population group in bihor county

Romanian Journal of Diabetes Nutrition and Metabolic Diseases ◽

10.2478/v10255-012-0034-0 ◽

2012 ◽

Vol 19 (3) ◽

pp. 285-290

Author(s):

Denisa Kovacs ◽

Luiza Demian ◽

Aurel Babeş

Keyword(s):

Type 2 Diabetes ◽

Type 1 Diabetes ◽

Skin Lesion ◽

Skin Diseases ◽

Population Group ◽

Skin Lesions ◽

Diabetic Patients ◽

Cutaneous Lesions

Abstract Objectives: The aim of the study was to calculate the prevalence rates and risk ofappearance of cutaneous lesions in diabetic patients with both type-1 and type-2diabetes. Material and Method: 384 patients were analysed, of which 47 had type-1diabetes (T1DM), 140 had type-2 diabetes (T2DM) and 197 were non-diabeticcontrols. Results: The prevalence of the skin lesions considered markers of diabeteswas 57.75% in diabetics, in comparison to 8.12% in non-diabetics (p<0.01). The riskof skin lesion appearance is over 7 times higher in diabetic patients than in nondiabetics.In type-1 diabetes the prevalence of skin lesions was significantly higherthan in type-2 diabetes, and the risk of skin lesion appearance is almost 1.5 timeshigher in type-1 diabetes than type-2 diabetes compared to non-diabetic controls.Conclusions: The diabetic patients are more susceptible than non-diabetics todevelop specific skin diseases. Patients with type-1 diabetes are more affected.

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