Classification of liver tumor using SFTA based Naïve Bayes classifier and support vector machine

Summary In recent years, one of the largest causes of death in human beings is liver tumor and cancer. In the current scenario, identifying the cancer tumor manually is very difficult and takes a lot of time as the world is battling with COVID‐19. The doctors and physicians are busy in serving and curing them. To predict the stage of liver tumor and plan the treatment, the segmentation is done from CT scanned images. In this research, two‐stage automatic liver segmentation and tumor identification framework using a fully connected convolutional neural network (FC‐CNN) model is proposed. During the first stage, the liver region is segmented using level set method from the preprocessed (OTSU thresholding) CT images. The features are extracted and utilized to detect the liver tumor in the second stage. The developed FC‐CNN is trained and tested using the extracted second order statistical textural features to classify the tumor affected and normal image. The proposed FC‐CNN model is trained and tested with 3D‐IRCADb‐01 and Kaggle datasets. The tested results prove that the proposed FC‐CNN model outperforms other reported methods. From the performance analysis, it is observed that it achieves a good accuracy and sensitivity rate of 99.11% and 98.10%, respectively.

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“…In DNN there will be a requirement of balancing the dataset while parting between testing and training in the ratio of 3:7 ratio. 30…”

Section: The Proposed Cnn Architecture For Tumor Classificationmentioning

confidence: 99%

Automatic liver tumor segmentation and identification using fully connected convolutional neural network from CT images

Vadlamudi

Reddy

et al. 2022

Concurrency and Computation

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“…Naïve Bayes (NB) performs the classification based on the Bayes theorem. NB has been used by A. Krishna et al [25] for the classification of liver tumor and by Shapla Rani Ghosh and Sajjad Waheed [26] for diagnosis of liver disease., S. Aman [9] et al has studied the usage of linear, nonlinear and decision tree-based classification algorithms to diagnose liver disease disorder. In their study authors have successfully concluded that use of CART algorithm to diagnosis liver disease has more accuracy rates as compare to LDA, DLDA, QDA, DQDA, NB, ANN.…”

Section: A Data Mining Models In Liver Disease Diagnosismentioning

confidence: 99%

Two-Stage CBR Based Healthcare Model to Diagnose Liver Disease

Sharma¹,

Mehrotra²

2021

IJCDS

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In India, liver disease is one of the major cause of deaths in all age groups and moreover its resistance to early detection is more disturbing fact. It is therefore important to have a decision support system for early prediction of liver disease so that proper medical assistance can be given to patients. A number of researches have been done to address this area. However, the current literature fails to offer a system, which can make such similar kind of early prediction. Hence, this research proposes a Two-stage CBR (TSCBR) model to enhance the accuracy of prediction of liver disease. Case Based Reasoning (CBR) is added to 5 different classification models including neural network (NN), decision tree based Random Forest (RF), logistic regression (LR), Support Vector Machine (SVM) and naive Bayes (NB) to enhance the improvement on the correct diagnosis of disease. The main focus of this research is to reduce the number of cases that are wrongly classified as not having liver disease. Every new case passes through two stages of proposed model to see whether the new case belongs to liver disease or not. To analyze the accuracy of the proposed research two stage CBR model is compared with already existing models to diagnose liver disease without CBR. 10-fold cross-validation approach is used to reduce biasness and to analyze the performance of proposed system. It was observed that naive Bayes with CBR model shows the highest accuracy of 0.83 among all the existing compared classification technique. The proposed technique decreases the number of false negative cases. Thus, resulting in enhanced number of early detections of liver cases. It has been observed that the accuracy of TSCBR is better than the existing system.

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“…In [9], authors have employed SVM classifier in their work and have achieved an accuracy of 92.5 %. Use of Knearest Neighbor (KNN) Classifier for medical image retrieval is discussed in [10].…”

Section: Literature Surveymentioning

confidence: 99%

Performance Analysis of Classification of Liver Tumors using Support Vector Machine and Rough Set based Classifiers

2019

IJITEE

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In recent years the medical diagnosis is majorly done based on the medical images captured using various imaging modalities. The medical doctors and radiologists use these medical images to identify the pathological problems or diseases and suggest the patient about further treatment. In this process, medical doctors and radiologists often prefer to make use of software which can assist in taking the decision. Such an approach is called as Computer Aided Diagnosis (CAD). The CAD systems usually comprise of many phases like segmentation of portion corresponding to a particular organ or region under consideration, detecting the pathology bearing area in that and further classifying into various disease classes. Here, Accuracy of classifiers majorly decides the effectiveness of the diagnosis. In this paper, classification of liver tumors into benign and malignant is considered as a case study. Implementation of two different classifiers namely Support Vector Machine and Rough Set based classifier is carried out using set of features extracted such as Texture features using Average Correction Higher order Local Autocorrelation Coefficients and shape features using Legendre moments. Comparison of performance of both the classifiers is made and tabulated. Here, Rough Set based classifier has performed better when compared with Support Vector Machine.

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Classification of liver tumor using SFTA based Naïve Bayes classifier and support vector machine

Cited by 11 publications

References 7 publications

Automatic liver tumor segmentation and identification using fully connected convolutional neural network from CT images

Automatic liver tumor segmentation and identification using fully connected convolutional neural network from CT images

Two-Stage CBR Based Healthcare Model to Diagnose Liver Disease

Performance Analysis of Classification of Liver Tumors using Support Vector Machine and Rough Set based Classifiers

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