Detection of Lung Cancer using SVM Classifier and KNN Algorithm

Sathishkumar, R.; Kalaiarasan, K.; Prabhakaran, Amrutha; Aravind, M.

doi:10.1109/icscan.2019.8878774

Cited by 15 publications

(3 citation statements)

References 10 publications

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“…To differentiate authentic films from deepfake ones using optical flow fields. Seeming motion in a video is captured by optical flow, which is calculated between consecutive frames this proposed by Irene Amerini and Leonardo Galteri [21]. There is a theory that deepfakes, especially with regard to facial motions, differ in motion from those that were taken in real life.…”

Section: Related Wordmentioning

confidence: 99%

Video Morphing Attack Detection Using Convolutional Neural Networks On Deep Fake Detection Algorithm

2024

eatp

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A method called deepfake produces fake video and films with artificial or substituted faces. Deepfakes are turning into a worrying societal phenomenon because they may be used maliciously to spread harmful information, fabricate electronic convincing proof, make fake political news, and even participate in online harassment and fraud. Regarding the fight against the ubiquitous threat provided by deepfake videos, our suggested technique, named "Video Morphing Attack Detection Using CNN," is a stronghold. We provide our system the capacity to model temporal relationships across sequences and extract complex characteristics from video frames by integrating the ResNeXt-50 and LSTM neural network designs, respectively. Our method effectively detects abnormalities suggestive of video morphing assaults via forward propagation and SoftMax activation. We guarantee strong detection performance while maintaining the veracity and integrity of visual information by utilizing dynamic face localization and specific assessment measures. Our system provides a comprehensive solution to reduce the negative impacts of deep fake manipulation in visual media, including vital functionalities for picture conversion, prediction creation, and data pretreatment. Convolutional neural network architecture ResNeXt-50 is a member of the ResNeXt family, which was first presented as an advancement of the ResNet design. To overcome certain constraints of conventional convolutional neural networks (CNNs), ResNeXt aims to achieve state-of-the-art performance in image classification tasks while managing intricate visual input. The "50" in ResNeXt-50 stands for the network's depth or the total number of layers it has. ResNeXt-50 comprises 50 layers in total, comprising fully connected, pooling, and convolutional layers.

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Section: Related Wordmentioning

confidence: 99%

Video Morphing Attack Detection Using Convolutional Neural Networks On Deep Fake Detection Algorithm

2024

eatp

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“…Two categories of issues: grouping problems and back-up problems, are well suited to supervised learning algorithms. The output variable usually takes on a limited number of discrete values [26] [27].…”

Section: A Supervised Learningmentioning

confidence: 99%

Lung cancer Prediction and Classification based on Correlation Selection method Using Machine Learning Techniques

Abdullah

Abdulazeez

Sallow

2021

QAJ

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Lung cancer is one of the leading causes of mortality in every country, affecting both men and women. Lung cancer has a low prognosis, resulting in a high death rate. The computing sector is fully automating it, and the medical industry is also automating itself with the aid of image recognition and data analytics. This paper endeavors to inspect accuracy ratio of three classifiers which is Support Vector Machine (SVM), K-Nearest Neighbor (KNN)and, Convolutional Neural Network (CNN) that classify lung cancer in early stage so that many lives can be saving. Basically, the informational indexes utilized as a part of this examination are taken from UCI datasets for patients affected by lung cancer. The principle point of this paper is to the execution investigation of the classification algorithms accuracy by WEKA Tool. The experimental results show that SVM gives the best result with 95.56%, then CNN with CNN 92.11% and KNN with 88.40%.

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“…Binarization is applied to determine the cases of lung cancer probability. SVM is used to classify the image and determine whether it is a malignant tumor or normal [8]. This proposed technique has the drawback of utilizing an advanced degree of the algorithm as well as the extreme gradient boosting algorithm for improved data set utilization.…”

Section: Introductionmentioning

confidence: 99%

Classification techniques using gray level co-occurrence matrix features for the detection of lung cancer using computed tomography imaging

Shankara

Hariprasad

Latha

2023

IJECE

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<span lang="EN-US">Lung cancer, which causes the majority of fatalities worldwide each year, is one of the deadliest diseases. The survival rate of cancer patients could be improved with better cancer detection methods. Image processing and machine learning have both been used to aid in lung cancer detection, but a method that both increase accuracy and increases a patient’s survival rate has yet to be identified. In an effort to find the most effective method for the accurate lung cancer recognition, this paper analyses and compares several classification algorithms. Lung computed tomography (CT) images are enhanced by removing noise using a median filter. For filtered image, threshold segmentation is used to segment it into distinct parts. From the segmented image different features are extracted using the grey level co-occurrence matrix (GLCM). several classification strategies, including support vector machine (SVM), random forest (RF), k-nearest neighbor (KNN), and decision tree (DT) methods, are used to classify lung images as malignant or normal based on the extracted features. Methods are evaluated based on a number of various performance measures, like accuracy, a precision, the recall, and the F1-Score. Based on the experimental outcomes, SVM outperforms other classification methods in accurately detecting lung cancer with an accuracy of 99.32%.</span>

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Detection of Lung Cancer using SVM Classifier and KNN Algorithm

Cited by 15 publications

References 10 publications

Video Morphing Attack Detection Using Convolutional Neural Networks On Deep Fake Detection Algorithm

Video Morphing Attack Detection Using Convolutional Neural Networks On Deep Fake Detection Algorithm

Lung cancer Prediction and Classification based on Correlation Selection method Using Machine Learning Techniques

Classification techniques using gray level co-occurrence matrix features for the detection of lung cancer using computed tomography imaging

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