scholarly journals Melanoma Classification from Dermoscopy Images Using Ensemble of Convolutional Neural Networks

Mathematics ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 26
Author(s):  
Rehan Raza ◽  
Fatima Zulfiqar ◽  
Shehroz Tariq ◽  
Gull Bano Anwar ◽  
Allah Bux Sargano ◽  
...  
Keyword(s):  
Skin Cancer ◽  
Human Skin ◽  
Transfer Learning ◽  
Fine Tuning ◽  
Acral Lentiginous Melanoma ◽  
Augmentation Techniques ◽  
Exposed Part ◽  
Detection And Diagnosis ◽  
Melanoma Classification ◽  
The Impact

Human skin is the most exposed part of the human body that needs constant protection and care from heat, light, dust, and direct exposure to other harmful radiation, such as UV rays. Skin cancer is one of the dangerous diseases found in humans. Melanoma is a form of skin cancer that begins in the cells (melanocytes) that control the pigment in human skin. Early detection and diagnosis of skin cancer, such as melanoma, is necessary to reduce the death rate due to skin cancer. In this paper, the classification of acral lentiginous melanoma, a type of melanoma with benign nevi, is being carried out. The proposed stacked ensemble method for melanoma classification uses different pre-trained models, such as Xception, Inceptionv3, InceptionResNet-V2, DenseNet121, and DenseNet201, by employing the concept of transfer learning and fine-tuning. The selection of pre-trained CNN architectures for transfer learning is based on models having the highest top-1 and top-5 accuracies on ImageNet. A novel stacked ensemble-based framework is presented to improve the generalizability and increase robustness by fusing fine-tuned pre-trained CNN models for acral lentiginous melanoma classification. The performance of the proposed method is evaluated by experimenting on a Figshare benchmark dataset. The impact of applying different augmentation techniques has also been analyzed through extensive experimentations. The results confirm that the proposed method outperforms state-of-the-art techniques and achieves an accuracy of 97.93%.

Assessment of Unconventional Resources Opportunities in the Middle East Tethyan Petroleum System in a Transfer Learning Context

2021 ◽  
Author(s):  
Cyrus Ashayeri ◽  
Birendra Jha
Keyword(s):  
Middle East ◽  
Transfer Learning ◽  
Fine Tuning ◽  
Data Driven ◽  
Learning Context ◽  
Eagle Ford ◽  
Field Development ◽  
Unconventional Resources ◽  
Basin Scale ◽  
The Impact

Abstract Decision making in new fields with little data available relies heavily on physics-based simulation models. However, due to a lack of full understanding of the physical processes governing flow in the unconventional resources, data-driven modeling has emerged as an alternative and complimentary tool to create recovery forecasts that honor the available data. Transfer Learning provides an opportunity to start early-stage analysis of the asset before adequate data becomes available. New challenges in the energy industry as well as shifting dynamics in both domestic and global supply and demand has encouraged some of the petroleum exporting countries in the Middle East to strategize the development of unconventional resources. In this research we have developed a data-driven Transfer Learning framework that allows the basin-wide assessment of new shale gas and tight oil prospects. The proposed Transfer Learning method is developed on real-world data from several thousand horizontal multistage wells in the Eagle Ford super-basin in South Texas. In this method we have integrated reservoir engineering domain expertise in the data pre-processing and feature generation steps. We have also considered the temporal and spatial balancing of the training data to assure that the predictive models honor the real practice of unconventional field development. Our full cycle Transfer Learning workflow consists of dimensionality reduction and unsupervised clustering, supervised learning, and hyperparameter fine-tuning. This workflow enables reservoir engineers to experiment with multiple hypothetical scenarios and observe the impact of additional data in the learning process. We use the developed workflow to examine the performance of a data-driven model of the Eagle Ford Basin on potential plays in the Middle East. Existence of all liquid types of oil, condensate and dry gas in the Eagle Ford has resulted in training a model flexible enough to be tested on various types of assets in a new location. We first present the successful deployment of our model within the Eagle Ford. Next, we use the information from major formations such as Tuwaiq Mountain and Hanifa and show the value of a pre-existing model from a fully-developed shale play on achieving acceptable accuracies with minimal information available in a new field. Our model is developed by data types with relatively low resolution that minimizes overfitting effects and allows generalization to different geologies with basin-wide accuracy. This approach allows conducting accelerated assessment of various sections of a large asset to enhance field development planning processes. This is a first example of such an effort on a basin scale that examines the effectiveness of Transfer Learning on some of the major unconventional plays in the Middle East region. This workflow allows investigating the relationship among geologic and petrophysical variables, drilling and completion parameters, and productivity of a large group of wells in a new asset.

Food Safety Security: A new Concept for Enhancing Food Safety Measures

2012 ◽  
Vol 82 (3) ◽  
pp. 216-222 ◽  
Author(s):  
Venkatesh Iyengar ◽  
Ibrahim Elmadfa
Keyword(s):  
Food Safety ◽  
Hazard Analysis ◽  
Warning System ◽  
Shared Responsibility ◽  
Fine Tuning ◽  
Strategic Action ◽  
Surveillance Network ◽  
Measurement Systems ◽  
Critical Control Points ◽  
The Impact

The food safety security (FSS) concept is perceived as an early warning system for minimizing food safety (FS) breaches, and it functions in conjunction with existing FS measures. Essentially, the function of FS and FSS measures can be visualized in two parts: (i) the FS preventive measures as actions taken at the stem level, and (ii) the FSS interventions as actions taken at the root level, to enhance the impact of the implemented safety steps. In practice, along with FS, FSS also draws its support from (i) legislative directives and regulatory measures for enforcing verifiable, timely, and effective compliance; (ii) measurement systems in place for sustained quality assurance; and (iii) shared responsibility to ensure cohesion among all the stakeholders namely, policy makers, regulators, food producers, processors and distributors, and consumers. However, the functional framework of FSS differs from that of FS by way of: (i) retooling the vulnerable segments of the preventive features of existing FS measures; (ii) fine-tuning response systems to efficiently preempt the FS breaches; (iii) building a long-term nutrient and toxicant surveillance network based on validated measurement systems functioning in real time; (iv) focusing on crisp, clear, and correct communication that resonates among all the stakeholders; and (v) developing inter-disciplinary human resources to meet ever-increasing FS challenges. Important determinants of FSS include: (i) strengthening international dialogue for refining regulatory reforms and addressing emerging risks; (ii) developing innovative and strategic action points for intervention {in addition to Hazard Analysis and Critical Control Points (HACCP) procedures]; and (iii) introducing additional science-based tools such as metrology-based measurement systems.

Enhanced Expression of Fibroblast Growth Factor Receptor 3 in Human Skin Cancer Cells

2009 ◽  
Vol 2 (1) ◽  
pp. 30-36 ◽  
Author(s):  
Motoki Terada ◽  
Chikara Ohnishi ◽  
Nobuhiro Ueno ◽  
Akio Shimizu ◽  
Michiyuki Kanai ◽  
...  
Keyword(s):  
Growth Factor ◽  
Skin Cancer ◽  
Cancer Cells ◽  
Fibroblast Growth Factor ◽  
Human Skin ◽  
Fibroblast Growth Factor Receptor ◽  
Growth Factor Receptor ◽  
Fibroblast Growth ◽  
Enhanced Expression

scholarly journals Deep Learning Techniques for Grape Plant Species Identification in Natural Images

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 4850 ◽  
Author(s):  
Carlos S. Pereira ◽  
Raul Morais ◽  
Manuel J. C. S. Reis
Keyword(s):  
Transfer Learning ◽  
Climatic Conditions ◽  
Fine Tuning ◽  
Variety Identification ◽  
Test Accuracy ◽  
Accuracy Score ◽  
Learning Techniques ◽  
Four Corners ◽  
Integrated Software ◽  
Grape Varieties

Frequently, the vineyards in the Douro Region present multiple grape varieties per parcel and even per row. An automatic algorithm for grape variety identification as an integrated software component was proposed that can be applied, for example, to a robotic harvesting system. However, some issues and constraints in its development were highlighted, namely, the images captured in natural environment, low volume of images, high similarity of the images among different grape varieties, leaf senescence, and significant changes on the grapevine leaf and bunch images in the harvest seasons, mainly due to adverse climatic conditions, diseases, and the presence of pesticides. In this paper, the performance of the transfer learning and fine-tuning techniques based on AlexNet architecture were evaluated when applied to the identification of grape varieties. Two natural vineyard image datasets were captured in different geographical locations and harvest seasons. To generate different datasets for training and classification, some image processing methods, including a proposed four-corners-in-one image warping algorithm, were used. The experimental results, obtained from the application of an AlexNet-based transfer learning scheme and trained on the image dataset pre-processed through the four-corners-in-one method, achieved a test accuracy score of 77.30%. Applying this classifier model, an accuracy of 89.75% on the popular Flavia leaf dataset was reached. The results obtained by the proposed approach are promising and encouraging in helping Douro wine growers in the automatic task of identifying grape varieties.

scholarly journals Flexible Potentiometric Sensor System for Non-Invasive Determination of Antioxidant Activity of Human Skin: Application for Evaluating the Effectiveness of Phytocosmetic Products

Chemosensors ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 76
Author(s):  
Aleksey V. Tarasov ◽  
Ekaterina I. Khamzina ◽  
Maria A. Bukharinova ◽  
Natalia Yu. Stozhko
Keyword(s):  
Antioxidant Activity ◽  
Human Skin ◽  
Skin Diseases ◽  
Silver Chloride ◽  
Potentiometric Sensor ◽  
Sensor System ◽  
Non Invasive ◽  
Pet Film ◽  
The Impact

In contemporary bioanalysis, monitoring the antioxidant activity (AOA) of the human skin is used to assess stresses, nutrition, cosmetics, and certain skin diseases. Non-invasive methods for skin AOA monitoring have certain advantages over invasive methods, namely cost-effectiveness, lower labor intensity, reduced risk of infection, and obtaining results in the real-time mode. This study presents a new flexible potentiometric sensor system (FPSS) for non-invasive determination of the human skin AOA, which is based on flexible film electrodes (FFEs) and membrane containing a mediator ([Fe(CN)6]3–/4–). Low-cost available materials and scalable technologies were used for FFEs manufacturing. The indicator FFE was fabricated based on polyethylene terephthalate (PET) film and carbon veil (CV) by single-sided hot lamination. The reference FFE was fabricated based on PET film and silver paint by using screen printing, which was followed by the electrodeposition of precipitate containing a mixture of silver chloride and silver ferricyanide (SCSF). The three-electrode configuration of the FPSS, including two indicator FFEs (CV/PET) and one reference FFE (SCSF/Ag/PET), has been successfully used for measuring the skin AOA and evaluating the impact of phytocosmetic products. FPSS provides reproducible (RSD ≤ 7%) and accurate (recovery of antioxidants is almost 100%) results, which allows forecasting its broad applicability in human skin AOA monitoring as well as for evaluating the effectiveness of topically and orally applied antioxidants.

Listen2Cough

2021 ◽  
Vol 5 (1) ◽  
pp. 1-22
Author(s):  
Xuhai Xu ◽  
Ebrahim Nemati ◽  
Korosh Vatanparvar ◽  
Viswam Nathan ◽  
Tousif Ahmed ◽  
...  
Keyword(s):  
Health Assessment ◽  
Ground Truth ◽  
Health Condition ◽  
Fine Tuning ◽  
Detection Model ◽  
Assessment Tasks ◽  
Augmentation Techniques ◽  
In The Wild ◽  
The Rich ◽  
Lung Health

The prevalence of ubiquitous computing enables new opportunities for lung health monitoring and assessment. In the past few years, there have been extensive studies on cough detection using passively sensed audio signals. However, the generalizability of a cough detection model when applied to external datasets, especially in real-world implementation, is questionable and not explored adequately. Beyond detecting coughs, researchers have looked into how cough sounds can be used in assessing lung health. However, due to the challenges in collecting both cough sounds and lung health condition ground truth, previous studies have been hindered by the limited datasets. In this paper, we propose Listen2Cough to address these gaps. We first build an end-to-end deep learning architecture using public cough sound datasets to detect coughs within raw audio recordings. We employ a pre-trained MobileNet and integrate a number of augmentation techniques to improve the generalizability of our model. Without additional fine-tuning, our model is able to achieve an F1 score of 0.948 when tested against a new clean dataset, and 0.884 on another in-the-wild noisy dataset, leading to an advantage of 5.8% and 8.4% on average over the best baseline model, respectively. Then, to mitigate the issue of limited lung health data, we propose to transform the cough detection task to lung health assessment tasks so that the rich cough data can be leveraged. Our hypothesis is that these tasks extract and utilize similar effective representation from cough sounds. We embed the cough detection model into a multi-instance learning framework with the attention mechanism and further tune the model for lung health assessment tasks. Our final model achieves an F1-score of 0.912 on healthy v.s. unhealthy, 0.870 on obstructive v.s. non-obstructive, and 0.813 on COPD v.s. asthma classification, outperforming the baseline by 10.7%, 6.3%, and 3.7%, respectively. Moreover, the weight value in the attention layer can be used to identify important coughs highly correlated with lung health, which can potentially provide interpretability for expert diagnosis in the future.

scholarly journals Screening for Melanoma and Other Skin Cancer Shows a Higher Early Melanoma Incidence: Social Educational Program “Life Fear-Free”

2021 ◽  
Vol 8 (1) ◽  
pp. 54-68
Author(s):  
Lev Demidov ◽  
Igor Samoylenko ◽  
Nina Vand ◽  
Igor Utyashev ◽  
Irina Shubina ◽  
...  
Keyword(s):  
Skin Cancer ◽  
Screening Program ◽  
Excisional Biopsy ◽  
Skin Lesions ◽  
Detection Methods ◽  
Non Melanoma Skin Cancer ◽  
The Social ◽  
Educational Campaign ◽  
Skin Cancer Detection ◽  
The Impact

Background: The screening program Life Fear-Free (LFF) aimed at early diagnosis of cutaneous melanoma (CM) was introduced in Samara, Chelyabinsk, Yekaterinburg, and Krasnodar (Russia) in 2019. Objectives: To analyze the impact of the program on early CM and non-melanoma skin cancer (NMSC) detection. Methods: According to the social educational campaign, people were informed about CM risk factors and symptoms and were invited for skin examination. The program planned to involve 3200 participants in total. Participants with suspicious lesions were invited for excisional biopsy. Results: 3143 participants, including 75.4% women, were examined for skin lesions. The average age of the participants was 43.7 years. Mostly skin phototypes II and III were registered (48.2% and 41.0%, respectively); 3 patients had CM, 15 had basal cell carcinoma, and 1 had Bowen’s disease, which were confirmed histologically. All detected melanomas had Breslow’s thickness of 1 mm. Conclusion: The participants showed high interest in early skin cancer detection programs. The incidence rate of CM and NMSCs among the program participants was higher than in general public. The early disease grade was proven for the detected CMs and NMSCs. The study has shown that it is important to continue such programs.

scholarly journals New polyp image classification technique using transfer learning of network-in-network structure in endoscopic images

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Young Jae Kim ◽  
Jang Pyo Bae ◽  
Jun-Won Chung ◽  
Dong Kyun Park ◽  
Kwang Gi Kim ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Transfer Learning ◽  
Test Data ◽  
State Of The Art ◽  
Early Stage ◽  
Statistical Significance ◽  
Recall Rate ◽  
Training Data ◽  
Fine Tuning ◽  
Accuracy Evaluation

AbstractWhile colorectal cancer is known to occur in the gastrointestinal tract. It is the third most common form of cancer of 27 major types of cancer in South Korea and worldwide. Colorectal polyps are known to increase the potential of developing colorectal cancer. Detected polyps need to be resected to reduce the risk of developing cancer. This research improved the performance of polyp classification through the fine-tuning of Network-in-Network (NIN) after applying a pre-trained model of the ImageNet database. Random shuffling is performed 20 times on 1000 colonoscopy images. Each set of data are divided into 800 images of training data and 200 images of test data. An accuracy evaluation is performed on 200 images of test data in 20 experiments. Three compared methods were constructed from AlexNet by transferring the weights trained by three different state-of-the-art databases. A normal AlexNet based method without transfer learning was also compared. The accuracy of the proposed method was higher in statistical significance than the accuracy of four other state-of-the-art methods, and showed an 18.9% improvement over the normal AlexNet based method. The area under the curve was approximately 0.930 ± 0.020, and the recall rate was 0.929 ± 0.029. An automatic algorithm can assist endoscopists in identifying polyps that are adenomatous by considering a high recall rate and accuracy. This system can enable the timely resection of polyps at an early stage.

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