Mammogram Learning System for Breast Cancer Diagnosis Using Deep Learning SVM

Jayandhi, G.; Jasmine, J. S. Leena; Joans, S. Mary

doi:10.32604/csse.2022.016376

Cited by 15 publications

(7 citation statements)

References 21 publications

(48 reference statements)

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“…As a fundamental task in natural language processing, text classification is essential for entity recognition, relationship extraction, and knowledge graph construction. Traditional classification methods are based on the manual extraction of text features with machine learning, such as Bayesian classifiers, Decision Trees, SVM [14] and the Hidden Markov Model (HMM) [15], which are widely used in text classification tasks. At present, deep learning-based methods have been applied to text classification tasks by training different neural network models, such as Convolutional Neural Network (CNN) [16], Recurrent Neural Network (RNN) [17], and Bidirectional Long Short-Term Memory (BiLSTM) [18], etc.…”

Section: Text Classification Methodsmentioning

confidence: 99%

Attack Behavior Extraction Based on Heterogeneous Cyberthreat Intelligence and Graph Convolutional Networks

Tang¹,

Wang²,

Qiu³

et al. 2023

Computers, Materials &Amp; Continua

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The continuous improvement of the cyber threat intelligence sharing mechanism provides new ideas to deal with Advanced Persistent Threats (APT). Extracting attack behaviors, i.e., Tactics, Techniques, Procedures (TTP) from Cyber Threat Intelligence (CTI) can facilitate APT actors' profiling for an immediate response. However, it is difficult for traditional manual methods to analyze attack behaviors from cyber threat intelligence due to its heterogeneous nature. Based on the Adversarial Tactics, Techniques and Common Knowledge (ATT&CK) of threat behavior description, this paper proposes a threat behavioral knowledge extraction framework that integrates Heterogeneous Text Network (HTN) and Graph Convolutional Network (GCN) to solve this issue. It leverages the hierarchical correlation relationships of attack techniques and tactics in the ATT&CK to construct a text network of heterogeneous cyber threat intelligence. With the help of the Bidirectional Encoder Representation from Transformers (BERT) pretraining model to analyze the contextual semantics of cyber threat intelligence, the task of threat behavior identification is transformed into a text classification task, which automatically extracts attack behavior in CTI, then identifies the malware and advanced threat actors. The experimental results show that F1 achieve 94.86% and 92.15% for the multi-label classification tasks of tactics and techniques. Extend the experiment to verify the method's effectiveness in identifying the malware and threat actors in APT attacks. The F1 for malware and advanced threat actors identification task reached 98.45% and 99.48%, which are better than the benchmark model in the experiment and achieve state of the art. The model can effectively model threat intelligence text data and acquire knowledge and experience migration by correlating implied features with a priori knowledge to compensate for insufficient sample data and improve the classification performance and recognition ability of threat behavior in text.

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Section: Text Classification Methodsmentioning

confidence: 99%

Attack Behavior Extraction Based on Heterogeneous Cyberthreat Intelligence and Graph Convolutional Networks

Tang¹,

Wang²,

Qiu³

et al. 2023

Computers, Materials &Amp; Continua

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“…Deep learning architecture is employed in many medical image analysis systems such as pneumonia classification [15], mammogram classification [16], skin cancer [17,18], Covid-19 diagnosis [19], and vascular tissue simulation model [20]. They use a neural network for the classification and convolution and max pooling layer to extract deep features.…”

Section: Image/signal Acquisitionmentioning

confidence: 99%

Horizontal Voting Ensemble Based Predictive Modeling System for Colon燙ancer

Eswaran¹,

Anand²

2023

Computer Systems Science and Engineering

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Colon cancer is the third most commonly diagnosed cancer in the world. Most colon AdenoCArcinoma (ACA) arises from pre-existing benign polyps in the mucosa of the bowel. Thus, detecting benign at the earliest helps reduce the mortality rate. In this work, a Predictive Modeling System (PMS) is developed for the classification of colon cancer using the Horizontal Voting Ensemble (HVE) method. Identifying different patterns in microscopic images is essential to an effective classification system. A twelve-layer deep learning architecture has been developed to extract these patterns. The developed HVE algorithm can increase the system's performance according to the combined models from the last epochs of the proposed architecture. Ten thousand (10000) microscopic images are taken to test the classification performance of the proposed PMS with the HVE method. The microscopic images obtained from the colon tissues are classified into ACA or benign by the proposed PMS. Results prove that the proposed PMS has ∼8% performance improvement over the architecture without using the HVE method. The proposed PMS for colon cancer reduces the misclassification rate and attains 99.2% of sensitivity and 99.4% of specificity. The overall accuracy of the proposed PMS is 99.3%, and without using the HVE method, it is only 91.3%.

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“…There are currently several types of SVM, such as twin SVM [1], Gaussian SVM [2], multi-kernel SVM [3] and so on. Furthermore, SVM has been widely applied in a variety of fields, for example, handwritten hindi character recognition [4], face recognition [5], network intrusion detection [6] and breast cancer diagnosis [7], and so on. Although SVM is a common algorithm for classification problems that treats all samples equally and ignores the effect of outliers and noise on the construction of optimal hyperplanes, it fails to perform well when classifying new sets of data with fuzzy information.…”

Section: Introductionmentioning

confidence: 99%

Quantum Fuzzy Support Vector Machine for Binary Classification

Huang¹,

Zhang²,

Chen³

et al. 2023

Computer Systems Science and Engineering

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In the objective world, how to deal with the complexity and uncertainty of big data efficiently and accurately has become the premise and key to machine learning. Fuzzy support vector machine (FSVM) not only deals with the classification problems for training samples with fuzzy information, but also assigns a fuzzy membership degree to each training sample, allowing different training samples to contribute differently in predicting an optimal hyperplane to separate two classes with maximum margin, reducing the effect of outliers and noise, Quantum computing has super parallel computing capabilities and holds the promise of faster algorithmic processing of data. However, FSVM and quantum computing are incapable of dealing with the complexity and uncertainty of big data in an efficient and accurate manner. This paper research and propose an efficient and accurate quantum fuzzy support vector machine (QFSVM) algorithm based on the fact that quantum computing can efficiently process large amounts of data and FSVM is easy to deal with the complexity and uncertainty problems. The central idea of the proposed algorithm is to use the quantum algorithm for solving linear systems of equations (HHL algorithm) and the least-squares method to solve the quadratic programming problem in the FSVM. The proposed algorithm can determine whether a sample belongs to the positive or negative class while also achieving a good generalization performance. Furthermore, this paper applies QFSVM to handwritten character recognition and demonstrates that QFSVM can be run on quantum computers, and achieve accurate classification of handwritten characters. When compared to FSVM, QFSVM's computational complexity decreases exponentially with the number of training samples.

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Mammogram Learning System for Breast Cancer Diagnosis Using Deep Learning SVM

Cited by 15 publications

References 21 publications

Attack Behavior Extraction Based on Heterogeneous Cyberthreat Intelligence and Graph Convolutional Networks

Attack Behavior Extraction Based on Heterogeneous Cyberthreat Intelligence and Graph Convolutional Networks

Horizontal Voting Ensemble Based Predictive Modeling System for Colon燙ancer

Quantum Fuzzy Support Vector Machine for Binary Classification

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