Abstract
Melanoma, one of the most dangerous types of skin cancer, results in a very high mortality rate. Early detection and resection are two key points for a successful cure. Recent researches ...have used artificial intelligence to classify melanoma and nevus and to compare the assessment of these algorithms to that of dermatologists. However, training neural networks on an imbalanced dataset leads to imbalanced performance, the specificity is very high but the sensitivity is very low. This study proposes a method for improving melanoma prediction on an imbalanced dataset by reconstructed appropriate CNN architecture and optimized algorithms. The contributions involve three key features as custom loss function, custom mini-batch logic, and reformed fully connected layers. In the experiment, the training dataset is kept up to date including 17,302 images of melanoma and nevus which is the largest dataset by far. The model performance is compared to that of 157 dermatologists from 12 university hospitals in Germany based on the same dataset. The experimental results prove that our proposed approach outperforms all 157 dermatologists and achieves higher performance than the state-of-the-art approach with area under the curve of 94.4%, sensitivity of 85.0%, and specificity of 95.0%. Moreover, using the best threshold shows the most balanced measure compare to other researches, and is promisingly application to medical diagnosis, with sensitivity of 90.0% and specificity of 93.8%. To foster further research and allow for replicability, we made the source code and data splits of all our experiments publicly available.
Radiological hazard assessment is an important task. The natural radionuclides in 88 surface soil samples in/surrounding high-level natural radiation areas were measured using a high-purity germanium ...gamma-ray detector. The
226
Ra,
238
U,
232
Th, and
40
K activities varied from 60.4 to 655, 59.3 to 643, 71.2 to 886, and 252 to 745 Bq/kg respectively. The highest radionuclides concentration was found in rare earth element mines, followed by uranium and metallic mines. There was an equilibrium between
238
U and
226
Ra with
238
U/
226
Ra ratios equal to 1.01. The radiation hazard indices in the study soil samples were far higher than the world average values.
Recently, there have been several studies on vision-based motion estimation under a supposition that planar motion follows a nonholonomic constraint. This allows reducing computational time. However, ...the vehicle motion in an outdoor environment does not accept this assumption. This paper presents a method for estimating the vision-based 3-D motion of a vehicle with several parts as follows. First, the Ackermann steering model is applied to reduce constraint parameters of the 3-D motion. In difference to the previous contribution, the proposed approach requires only two corresponding points of consecutive images to estimate the vehicle motion. Second, motion parameters are extracted based on a closed-form solution on geometric constraints. Third, the estimation approach applies the bundle adjustment-based quasiconvex optimization. This task aims to take into account advantage of omnidirectional vision-based features for reducing errors. The omnidirectional vision supports for landmarks tracking in long travel and large rotation, which is appropriate for a bundle adjustment technique. Evaluated results show that the proposed method is applicable in the practical condition of outdoor environments.
This paper presents a pedestrian detection method from a moving vehicle using optical flows and histogram of oriented gradients (HOG). A moving object is extracted from the relative motion by ...segmenting the region representing the same optical flows after compensating the egomotion of the camera. To obtain the optical flow, two consecutive images are divided into grid cells 14×14 pixels; then each cell is tracked in the current frame to find corresponding cell in the next frame. Using at least three corresponding cells, affine transformation is performed according to each corresponding cell in the consecutive images, so that conformed optical flows are extracted. The regions of moving object are detected as transformed objects, which are different from the previously registered background. Morphological process is applied to get the candidate human regions. In order to recognize the object, the HOG features are extracted on the candidate region and classified using linear support vector machine (SVM). The HOG feature vectors are used as input of linear SVM to classify the given input into pedestrian/nonpedestrian. The proposed method was tested in a moving vehicle and also confirmed through experiments using pedestrian dataset. It shows a significant improvement compared with original HOG using ETHZ pedestrian dataset.
The impact of direct-acting antivirals (DAA) therapy on lipid and glucose metabolism and kidney function in patients with hepatitis C virus (HCV) infection, along with its side effects on blood ...cells, remains controversial. Therefore, we conducted a study that enrolled 280 patients with HCV infection who achieved sustained virologic response after treatment with DAA therapy without ribavirin to evaluate the metabolic changes, renal function, and anemia risk based on real-world data. This study was an observational prospective study with a follow-up period of 12 weeks after the initiation of DAA therapy. Data on biochemical tests, renal function, blood counts, viral load, and host genomics were recorded before treatment and after 12 weeks of treatment with DAAs. DAA therapy reduced fibrosis-4 scores and improved liver function, with significant reductions in aspartate transaminase, alanine aminotransferase, and total bilirubin levels. However, DAA therapy slightly increased uric acid, cholesterol, and low-density lipoprotein cholesterol levels. It significantly reduced fasting blood glucose levels and hemoglobin A1C index (HbA1C) in the study group, while hemoglobin (Hb) and hematocrit (HCT) concentrations decreased significantly (4.78 ± 21.79 g/L and 0.09% ± 0.11%, respectively). The estimated glomerular filtration rate (eGFR) decreased by 12.89 ± 39.04 mL/min/1.73m.sup.2 . Most variations were not related to the genotype, except for Hb, HCT, and HbA1C. Anemia incidence increased from 23.58% before treatment to 30.72% after treatment. Patients with HCV-1 genotype had a higher rate of anemia than did patients with genotype 6 (36.23% vs. 24.62%). Multivariate analysis showed that the risk of anemia was related to female sex, cirrhosis status, fibrosis-4 score, pretreatment eGFR, and pretreatment Hb level. The results of our study can provide helpful information to clinicians for the prognosis and treatment of HCV infection.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Skin cancer is one of the most common cancers in the world. However, the disease is curable if detected in the beginning stage. Early detection of malignant lesions through accurate techniques and ...innovative technologies has a significant impact on reducing skin cancer mortality rates. Recently, artificial intelligence has come to the forefront to facilitate skin cancer diagnosis based on medical images. Many deep learning models have been studied and developed, but the imbalance of performance among classes in the multi-class classification is still a challenging problem. This study proposes a hybrid method for handling class imbalance of skin-disease classification. This method combines the data level method of balanced mini-batch logic followed by real-time image augmentation with the algorithm level method of designing new loss function. The training dataset includes 24,530 dermoscopic images of seven skin disease categories, which is by far the largest dataset of skin cancer. The performance metrics of six proposed methods are evaluated on a test dataset of 2,453 images. Our proposed EfficientNetB4-CLF model achieves the highest accuracy of 89.97% and also the highest mean recall of 86.13% with the smallest recalls' standard deviations of 7.60%. Compared to the original methods, our proposed solution not only surpasses 4.65% (86.13% vs 81.48%) of mean recalls but also reduces 4.24% of the recalls' standard deviations (from ±11.84% to ±7.60%). This result indicates that our hybrid method is highly effective in training the Deep CNN network on the skin-disease imbalanced dataset. It addresses the problem of slow learning of the minority classes in the networks by combining the data level method of balanced mini-batch logic followed by the real-time image augmentation with the algorithm level method of the newly designed loss function.
This paper contributes two issues for enhancing the accuracy and speed of a pedestrian detection system. First, it introduces a feature description using variant-scale block based Histograms of ...Oriented Gradients (HOG) features. By non-restricted block sizes, an extensive feature space that allows high-discriminated features to be selected for classification can be obtained. Second, a classification method based on a hybrid cascade boosting technique and a Support vector machine (SVM) is described. The SVM is known as one of the most efficient learning models for classification. On the other hand, one advantage of cascade boosting structure is to quickly reject most negative examples in the early layers, while retains almost all positive examples for speed up of the system. Because the performance of boosting depends on the kernel of weak classifier, the hybrid algorithms using the proposed feature descriptor is helpful for constructing an efficient classification with low computational time. In addition, an “integral image” method is utilized to support fast computation of the feature. The experimental results showed that performance of the proposed method is higher than the SVM using standard HOG features about 5% and the AdaBoost using variant-scale based HOG features about 4% detection rates, at 1% false alarm rates. The speed of classification using a cascade boosting approach is doubled comparing to that of the non-cascade one.
This paper proposes a solution based on
Adaptive learning
using the CNN model. The proposed method automatically updates the recognition model according to online training dataset accumulated ...directly from the system and retraining recognition model. The data updating task focuses on data samples that are less similar to previous trained ones. The purpose of this solution is to upgrade the model to a new one more adaptive, expecting to reach higher accuracy. In the adaptive learning approach, the recognition system is capable of self-learning and complementing data, without experts needed for data labeling or training. The proposed solution includes 5 main phases: (1) Detect and recognize low confident objects; (2) Track objects in
n
frames in future progress to make sure whether they are interesting objects or not. (3) In case of objects that are recognized with high confidence: labeling (same class of object) for the corresponding data samples to be recognized with low confidence scores which were tracked in the previous process. In case of objects determined not to be of interesting objects, the samples are labeled as Negative for all previous samples, which were tracked in n previous frames; (4) Initialize a training dataset based on a selective combination of previously trained data and the new data. (5) Retrain and update the model if it results in higher accuracy. We have conducted experiments to compare results of the proposed model—PDnet and some state of the art methods such as AlexNet and Vgg. The experimental results demonstrate that the proposed method provides the higher accuracy when the model are self-learned over time. On the other hand, our adaptive learning is applicable to the traditional recognition models such as AlexNet and Vgg model for improving accuracy.
In the field of dermatological diseases, especially for skin cancer, machine learning (ML) methods are used to classify melanoma and nevus using skin images. ML techniques result in high accuracy of ...diagnostic tasks since they are trained on balanced datasets. However, MLs working with imbalanced datasets produce erroneous results on precision, sensitivity, and specificity measured criteria. To deal with this problem, an augmentation approach combined with a category seesaw is used for the compensation factor. It increases the penalty for misclassified instances, thereby reducing the occurrence of false positives within the less common categories. This paper presents an approach to improve the efficiency of DCNN for classifying multi-class medical images on imbalanced datasets. The solution consists of three major contributions: (1) feature extraction based on some backbone models with customizing fully connected layers for classifier layers, (2) optimizing loss function (LF) and training parameters, (3) solving the problem of imbalanced samples using optimizing domination of weights between asymmetric classes with majority and minority categories. The method was evaluated and analyzed using the ISIC2018 benchmark and Chest X-ray dataset. Some well-known backbones were used for this study, e.g., EfficientNets, MobileNets, and DenseNets. The use of these backbones is to demonstrate that our methods are more efficient and stable in both light and heavy DCNN architectures. We also provide comparisons with existing methods that deal with the imbalance problem, e.g., data augmentation (AU), downsamples, customizing LF, and focal loss method (FL) for focusing on hard samples. Experimental results showed that these methods achieve good performance. However, there are several problems caused by generating new samples, and weighting samples, such as data overloading to train classifier models, a corrupt problem when applied to imbalanced data. Moreover, the FL method produced insufficient results on various DCNN backbones. Differently, our approach solves the imbalanced dataset based on boosting the sample weights of the minority and reducing the impact ratio of samples in majority categories. This strategy results in high precision and stable performance with various DCNN models without augmenting the dataset. Experiment results on ISIC2018 dataset demonstrated that our approach achieves more efficiency than other methods in some specific evaluation criteria as follows: higher than the FL method with 2.73% recall, 2.63% precision, 2.81% specificity, and 3.09% F1 using EfficientNet backbones; higher than AU method with 5.16% recall, 5.97% precision, 8.93% specificity, 6.16% F1 using DenseNet backbones.
PurposeThis research study aims to identify and rank the most substantial barriers to implementing green supply chain management (GSCM) in the Vietnamese agriculture ...industry.Design/methodology/approachThe Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) techniques have been employed for this work to rank the critical GSCM barriers. The rankings were determined based on the expertise and input of ten experts from Vietnamese agriculture firms who participated as respondents.FindingsThis study has identified seven clusters of barriers, which encompass a total of 19 sub-barriers. Among these obstacles, the categories of financial costs and external stakeholders have emerged as the top priority barriers that require immediate attention and resolution. Meanwhile, the technology and strategic management clusters have a relatively weaker impact on GSCM implementation.Practical implicationsThese findings provide valuable guidelines for the top managers in this sector to consider before systematically deciding on the GSCM implementation problems to improve performance and competitive advantage.Originality/valueThis work focuses on considering GSCM barriers for the Vietnamese agriculture industry; hence, it enriches the GSCM literature by offering perspectives from a transitional market, which results in variations in the barriers, categorization and importance ranking.
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