Malignant-benign classification of pulmonary nodules by bagging-decision trees

Tartar, Ahmet; Akan, Aydın

doi:10.1109/tiptekno.2015.7374622

Cited by 2 publications

(3 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This work will explore the possibility of transillumination of multispectral Nevoscopes to distinguish and dismantle ratiometric estimates using epiluminescence imagery to separate malignant melanomas from dysplastic nevi or other conventional skin lesions [22]. The authors in [23] describe a Tactile Cyber-Physical Imaging System (TIS) for the detection of malignant tumors. In the detection, the rule of all light within reflection terminal innervation ratio (TIR) is applied.…”

Section: Literature Surveymentioning

confidence: 99%

“…The fact that numerous certified sun absorbers exist increases the likelihood that the future sun will cause structural damage. Sunlight and tanning beds release UV radiation which increases the risk of melanoma and increases UV exposure in the vicinity of the equator [21][22][23][24]. A depiction of the ABCD (Asymmetry, Border, Color, and Dimension) run is as follows: start on the left side and work your way to the right: melanomas with (A) asymmetry, (B) an uneven, fatigued, or depressed edge, (C) concealment of different hues of darker, dull, or tan, and (D) an altered gauge width.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Cognitive Inspired & Computationally-Intelligent Early Melanoma Detection Using Feature Analysis Techniques

Gupta,

Sharma,

Tyagi

et al. 2023

JAIT

View full text Add to dashboard Cite

Melanoma is the most malignant kind of skin cancer, and it is responsible for the majority of deaths caused by skin cancer. However, this can be easily addressed by reverting to the standard method of damage removal if it is discovered in a timely manner. In this view, it is of the utmost importance to develop procedures for the early and reliable identification of melanomas. Since images for melanoma diagnosis are recorded in the clinic as an epiluminance image using a specific kind of equipment, the technique is machine-dependent. We make use of a method for managing images as well as a high-resolution shading image of a skin ulcer captured by a high-resolution camera or other device. Instead of depending on the epilumination images that are produced by the emergency equipment, the initial task that needs to be done in the present research is to capture high-resolution photographs of the skin injuries. The category limit will be determined with the use of machine learning, and then highlight extraction will take place. The research focuses on clinical photos obtained from fast cameras that were taken of individuals suffering from skin cancer. The problem of uneven illumination was kept at a strategic distance by the use of medial separation and pre-processing using the histogram. Utilisation of a brand new image segmentation method called "Otsu" for the extraction of sores. The extraction of ABCD (Asymmetry, Border, Color, and Dimension) involves the use of innovative methodologies as well as the Total Dermoscopic Value in order to characterise the weight coefficients. A solution to the problem of upgrading the categorization error that is based on machine learning. The parameters that are taken into consideration for evaluating the proposed model are senstivity, specificity, precision and accuracy and the results obtained are 1, 0.93, 0.93, and 0.93 respectively. It has been observed that the proposed model performs better as compared to the ones present in existing literature.

show abstract

Section: Literature Surveymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Cognitive Inspired & Computationally-Intelligent Early Melanoma Detection Using Feature Analysis Techniques

Gupta,

Sharma,

Tyagi

et al. 2023

JAIT

View full text Add to dashboard Cite

show abstract

“…Finally, this approach uses these models to predict the data. K predicted values can be obtained from each sample, and the final result can be obtained by voting [21]. Because the weights of the base learners in this algorithm are the same, the base learner selection in this algorithm will directly affect the results of the ensemble learning method.…”

Section: Webshell Attack Detection Based On Ensemble Learningmentioning

confidence: 99%

WS-LSMR: Malicious WebShell Detection Algorithm Based on Ensemble Learning

Luktarhan

Zhao

et al. 2020

IEEE Access

View full text Add to dashboard Cite

To solve the problem that the features produced by hidden means, such as code obfuscation and compression, in encrypted malicious WebShell files are not the same as those produced by nonencrypted files, a WebShell attack detection algorithm based on ensemble learning is proposed. First, this algorithm extracted the feature vocabulary of the unigrams and 4-grams based on opcode; subsequently, the 4-gram feature word weights were obtained according to the calculated Gini coefficient of the unigram feature words and used to select the features, which will be selected again based on the Gini coefficient of the 4-gram feature words. Consequently, a feature vocabulary that can detect encrypted and unencrypted WebShell files was constructed. Second, in order to improve the adaptability and accuracy of the detection method, an ensemble detection model called WS-LSMR, consisting of a Logistic Regression, Support Vector Machine, Multi-layer Perceptron and Random Forest, was constructed. The model uses a weighted voting method to determine the WebShell classification. This experiment demonstrated that compared with the traditional single WebShell detection algorithm, the recall rate and accuracy rate improved to 99.14% and 94.28%, respectively, which proves that this method has better detection performance.

show abstract

Malignant-benign classification of pulmonary nodules by bagging-decision trees

Cited by 2 publications

References 14 publications

Cognitive Inspired & Computationally-Intelligent Early Melanoma Detection Using Feature Analysis Techniques

Cognitive Inspired & Computationally-Intelligent Early Melanoma Detection Using Feature Analysis Techniques

WS-LSMR: Malicious WebShell Detection Algorithm Based on Ensemble Learning

Contact Info

Product

Resources

About