scholarly journals Classification Models for Skin Tumor Detection Using Texture Analysis in Medical Images

2020 ◽  
Vol 6 (6) ◽  
pp. 51
Author(s):  
Marcos A. M. Almeida ◽  
Iury A. X. Santos
Keyword(s):  
Early Diagnosis ◽  
Medical Images ◽  
Skin Tumor ◽  
Classification Models ◽  
Color Channel ◽  
Second Order Statistics ◽  
New Strategy ◽  
Channel Information ◽  
Occurrence Matrix

Medical images have made a great contribution to early diagnosis. In this study, a new strategy is presented for analyzing medical images of skin with melanoma and nevus to model, classify and identify lesions on the skin. Machine learning applied to the data generated by first and second order statistics features, Gray Level Co-occurrence Matrix (GLCM), keypoints and color channel information—Red, Green, Blue and grayscale images of the skin were used to characterize decisive information for the classification of the images. This work proposes a strategy for the analysis of skin images, aiming to choose the best mathematical classifier model, for the identification of melanoma, with the objective of assisting the dermatologist in the identification of melanomas, especially towards an early diagnosis.

Effectiveness of Supervised Classification Models for Hate Speech on Twitter

2020 ◽  
Author(s):  
Kunal Srivastava ◽  
Ryan Tabrizi ◽  
Ayaan Rahim ◽  
Lauryn Nakamitsu
Keyword(s):  
Supervised Classification ◽  
Hate Speech ◽  
The Internet ◽  
Classification Models ◽  
Personal Attack ◽  
Political Beliefs ◽  
The Many ◽  
Primary Medium ◽  
Offensive Speech

<div> <div> <div> <p>Abstract </p> <p>The ceaseless connectivity imposed by the internet has made many vulnerable to offensive comments, be it their physical appearance, political beliefs, or religion. Some define hate speech as any kind of personal attack on one’s identity or beliefs. Of the many sites that grant the ability to spread such offensive speech, Twitter has arguably become the primary medium for individuals and groups to spread these hurtful comments. Such comments typically fail to be detected by Twitter’s anti-hate system and can linger online for hours before finally being taken down. Through sentiment analysis, this algorithm is able to distinguish hate speech effectively through the classification of sentiment. </p> </div> </div> </div>

scholarly journals A Phantom Study to Investigate Robustness and Reproducibility of Grey Level Co-Occurrence Matrix (GLCM)-Based Radiomics Features for PET

2021 ◽  
Vol 11 (2) ◽  
pp. 535
Author(s):  
Mahbubunnabi Tamal
Keyword(s):  
Textural Features ◽  
Strongly Correlated ◽  
Segmentation Method ◽  
Diagnosis And Prognosis ◽  
Radiotracer Uptake ◽  
Positron Emission ◽  
Occurrence Matrix ◽  
The Impact ◽  
Robust To Noise

Quantification and classification of heterogeneous radiotracer uptake in Positron Emission Tomography (PET) using textural features (termed as radiomics) and artificial intelligence (AI) has the potential to be used as a biomarker of diagnosis and prognosis. However, textural features have been predicted to be strongly correlated with volume, segmentation and quantization, while the impact of image contrast and noise has not been assessed systematically. Further continuous investigations are required to update the existing standardization initiatives. This study aimed to investigate the relationships between textural features and these factors with 18F filled torso NEMA phantom to yield different contrasts and reconstructed with different durations to represent varying levels of noise. The phantom was also scanned with heterogeneous spherical inserts fabricated with 3D printing technology. All spheres were delineated using: (1) the exact boundaries based on their known diameters; (2) 40% fixed; and (3) adaptive threshold. Six textural features were derived from the gray level co-occurrence matrix (GLCM) using different quantization levels. The results indicate that homogeneity and dissimilarity are the most suitable for measuring PET tumor heterogeneity with quantization 64 provided that the segmentation method is robust to noise and contrast variations. To use these textural features as prognostic biomarkers, changes in textural features between baseline and treatment scans should always be reported along with the changes in volumes.

scholarly journals Preventing corneal blindness caused by keratitis using artificial intelligence

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Zhongwen Li ◽  
Jiewei Jiang ◽  
Kuan Chen ◽  
Qianqian Chen ◽  
Qinxiang Zheng ◽  
...  
Keyword(s):  
Early Diagnosis ◽  
Vision Loss ◽  
Learning System ◽  
Automated Classification ◽  
Slit Lamp ◽  
Resource Limited ◽  
The World ◽  
Different Types ◽  
Corneal Blindness

AbstractKeratitis is the main cause of corneal blindness worldwide. Most vision loss caused by keratitis can be avoidable via early detection and treatment. The diagnosis of keratitis often requires skilled ophthalmologists. However, the world is short of ophthalmologists, especially in resource-limited settings, making the early diagnosis of keratitis challenging. Here, we develop a deep learning system for the automated classification of keratitis, other cornea abnormalities, and normal cornea based on 6,567 slit-lamp images. Our system exhibits remarkable performance in cornea images captured by the different types of digital slit lamp cameras and a smartphone with the super macro mode (all AUCs>0.96). The comparable sensitivity and specificity in keratitis detection are observed between the system and experienced cornea specialists. Our system has the potential to be applied to both digital slit lamp cameras and smartphones to promote the early diagnosis and treatment of keratitis, preventing the corneal blindness caused by keratitis.

scholarly journals Multivariate Analysis for the Classification of Chocolate According to its Percentage of Cocoa by Using Terahertz Time-Domain Spectroscopy (THz-TDS)

Proceedings ◽  
2020 ◽  
Vol 70 (1) ◽  
pp. 109
Author(s):  
Jimy Oblitas ◽  
Jorge Ruiz
Keyword(s):  
Machine Learning ◽  
Time Domain ◽  
Electromagnetic Pulse ◽  
Machine Learning Algorithms ◽  
Classification Models ◽  
Terahertz Time Domain Spectroscopy ◽  
Time Domain Spectroscopy ◽  
Svm Algorithm ◽  
Classification Of Images

Terahertz time-domain spectroscopy is a useful technique for determining some physical characteristics of materials, and is based on selective frequency absorption of a broad-spectrum electromagnetic pulse. In order to investigate the potential of this technology to classify cocoa percentages in chocolates, the terahertz spectra (0.5–10 THz) of five chocolate samples (50%, 60%, 70%, 80% and 90% of cocoa) were examined. The acquired data matrices were analyzed with the MATLAB 2019b application, from which the dielectric function was obtained along with the absorbance curves, and were classified by using 24 mathematical classification models, achieving differentiations of around 93% obtained by the Gaussian SVM algorithm model with a kernel scale of 0.35 and a one-against-one multiclass method. It was concluded that the combined processing and classification of images obtained from the terahertz time-domain spectroscopy and the use of machine learning algorithms can be used to successfully classify chocolates with different percentages of cocoa.

Single image fog and haze removal based on self-adaptive guided image filter and color channel information of sky region

2017 ◽  
Vol 77 (11) ◽  
pp. 13513-13530 ◽  
Author(s):  
Bo Jiang ◽  
Hongqi Meng ◽  
Jian Zhao ◽  
Xiaolei Ma ◽  
Siyu Jiang ◽  
...  
Keyword(s):  
Single Image ◽  
Color Channel ◽  
Image Filter ◽  
Haze Removal ◽  
Guided Image Filter ◽  
Channel Information ◽  
Self Adaptive

scholarly journals Breast Cancer Type Classification Using Machine Learning

2021 ◽  
Vol 11 (2) ◽  
pp. 61
Author(s):  
Jiande Wu ◽  
Chindo Hicks
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Machine Learning ◽  
Triple Negative Breast Cancer ◽  
Triple Negative ◽  
Genomic Research ◽  
Support Vector ◽  
Cancer Type ◽  
Classification Models

Background: Breast cancer is a heterogeneous disease defined by molecular types and subtypes. Advances in genomic research have enabled use of precision medicine in clinical management of breast cancer. A critical unmet medical need is distinguishing triple negative breast cancer, the most aggressive and lethal form of breast cancer, from non-triple negative breast cancer. Here we propose use of a machine learning (ML) approach for classification of triple negative breast cancer and non-triple negative breast cancer patients using gene expression data. Methods: We performed analysis of RNA-Sequence data from 110 triple negative and 992 non-triple negative breast cancer tumor samples from The Cancer Genome Atlas to select the features (genes) used in the development and validation of the classification models. We evaluated four different classification models including Support Vector Machines, K-nearest neighbor, Naïve Bayes and Decision tree using features selected at different threshold levels to train the models for classifying the two types of breast cancer. For performance evaluation and validation, the proposed methods were applied to independent gene expression datasets. Results: Among the four ML algorithms evaluated, the Support Vector Machine algorithm was able to classify breast cancer more accurately into triple negative and non-triple negative breast cancer and had less misclassification errors than the other three algorithms evaluated. Conclusions: The prediction results show that ML algorithms are efficient and can be used for classification of breast cancer into triple negative and non-triple negative breast cancer types.

scholarly journals A Deep Convolutional Neural Wavelet Network for Classification of Medical Images

2018 ◽  
Vol 14 (11) ◽  
pp. 1488-1498
Author(s):  
Ramzi Ben Ali ◽  
Ridha Ejbali ◽  
Mourad Zaied
Keyword(s):  
Medical Images ◽  
Wavelet Network

scholarly journals A Portable Smartphone-Based Sensing System Using a 3D-Printed Chip for On-Site Biochemical Assays

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 4002 ◽  
Author(s):  
Feiyi Wu ◽  
Min Wang
Keyword(s):  
Organophosphorus Pesticides ◽  
Smartphone Apps ◽  
Color Channel ◽  
Calibration Equation ◽  
Sensing System ◽  
Biochemical Sensing ◽  
Biochemical Assays ◽  
Target Analytes ◽  
New Strategy ◽  
3D Printed

Recently, smartphone-based chromogenic sensing with paper-based microfluidic technology has played an increasingly important role in biochemical assays. However, generally there were three defects: (i) the paper-based chips still required complicated fabrication, and the hydrophobic boundaries on the chips were not clear enough; (ii) the chromogenic signals could not be steadily captured; (iii) the smartphone apps were restricted to the detection of specific target analytes and could not be extended for different assays unless reprogrammed. To solve these problems, in this study, a portable smartphone-based sensing system with a 3D-printed chip was developed. A 3D-printed imaging platform was designed to significantly reduce sensing errors generated during signal capture, and a brand-new strategy for signal processing in downloadable apps was established. As a proof-of-concept, the system was applied for detection of organophosphorus pesticides and multi-assay of fruit juice, showing excellent sensing performance. For different target analytes, the most efficient color channel could be selected for signal analysis, and the calibration equation could be directly set in user interface rather than programming environment, thus the developed system could be flexibly extended for other biochemical assays. Consequently, this study provides a novel methodology for smartphone-based biochemical sensing.

Classification of Tree and Network Topology Structures in Medical Images

2012 ◽  
pp. 79-90 ◽  
Author(s):  
Angeliki Skoura ◽  
Vasileios Megalooikonomou ◽  
Athanasios Diamantopoulos ◽  
George C. Kagadis ◽  
Dimitrios Karnabatidis
Keyword(s):  
Network Topology ◽  
Medical Images
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