Evaluation of SVM performance in the detection of lung cancer in marked CT scan dataset

Kareem, Hamdalla Fadil; Al-Huseiny, Muayed S.; Mohsen, Furat Y.; Khalil, Enam A.; Hassan, Zainab S.

doi:10.11591/ijeecs.v21.i3.pp1731-1738

Cited by 46 publications

(22 citation statements)

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“…On comparison with the recent works carried out using this same dataset, our model yields good results. With the same dataset work carried out using SVM classification, the accuracy achieved is 89.8876% [ 30 ]. In work carried out with the AlexNet architecture, accuracy is 93.548% [ 31 ].…”

Section: Discussionmentioning

confidence: 99%

A Transfer Learning Approach with a Convolutional Neural Network for the Classification of Lung Carcinoma

et al. 2022

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Lung cancer is among the most hazardous types of cancer in humans. The correct diagnosis of pathogenic lung disease is critical for medication. Traditionally, determining the pathological form of lung cancer involves an expensive and time-consuming process investigation. Lung cancer is a leading cause of mortality worldwide, with lung tissue nodules being the most prevalent way for doctors to identify it. The proposed model is based on robust deep-learning-based lung cancer detection and recognition. This study uses a deep neural network as an extraction of features approach in a computer-aided diagnosing (CAD) system to assist in detecting lung illnesses at high definition. The proposed model is categorized into three phases: first, data augmentation is performed, classification is then performed using the pretrained CNN model, and lastly, localization is completed. The amount of obtained data in medical image assessment is occasionally inadequate to train the learning network. We train the classifier using a technique known as transfer learning (TL) to solve the issue introduced into the process. The proposed methodology offers a non-invasive diagnostic tool for use in the clinical assessment that is effective. The proposed model has a lower number of parameters that are much smaller compared to the state-of-the-art models. We also examined the desired dataset’s robustness depending on its size. The standard performance metrics are used to assess the effectiveness of the proposed architecture. In this dataset, all TL techniques perform well, and VGG 16, VGG 19, and Xception for 20 epoch structure are compared. Preprocessing functions as a wonderful bridge to build a dependable model and eventually helps to forecast future scenarios by including the interface at a faster phase for any model. At the 20th epoch, the accuracy of VGG 16, VGG 19, and Xception is 98.83 percent, 98.05 percent, and 97.4 percent.

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Section: Discussionmentioning

confidence: 99%

A Transfer Learning Approach with a Convolutional Neural Network for the Classification of Lung Carcinoma

et al. 2022

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“…DT classifier constantly splits the data conferring a certain parameter. Each node in the tree splits the data while the leaves represent the choices or the outcome decisions [34]. Resilient propagation (RPROP) is considered a robust machine learning scheme that adopts the local gradient information for the weight step [35], [36].…”

Section: Classificationmentioning

confidence: 99%

Network intrusion detection system: machine learning approach

Jaradat

Barhoush²,

Easa³

2022

IJEECS

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The main goal of intrusion detection system (IDS) is to monitor the network performance and to investigate any signs of any abnormalities over the network. Recently, intrusion detection systems employ machine learning techniques, due to the fact that machine learning techniques proved to have the ability of learning and adapting in addition to allowing a prompt response. This work proposes a model for intrusion detection and classification using machine learning techniques. The model first acquires the data set and transforms it in the proper format, then performs feature selection to pick out a subset of attributes that worth being considered. After that, the refined data set was processed by the Konstanz information miner (KNIME). To gain better performance and a decent comparative analysis, three different classifiers were applied. The anticipated classifiers have been executed and assessed utilizing the KNIME analytics platform using (CICIDS2017) datasets. The experimental results showed an accuracy rate ranging between (98.6) as the highest obtained while the average was (90.59%), which was satisfying compared to other approaches. The gained statistics of this research inspires the researchers of this field to use machine learning in cyber security and data analysis and build intrusion detection systems with higher accuracy.

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“…CNN was used for classification without using any class balancing method and achieved 93.55 % accuracy, 95.71 % sensitivity, and 95 % specificity. The paper [11] used the same with the SVM classifier but the lowest class was neglected and was not included in the training. An accuracy of 89.88 % was achieved.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Hybrid selection framework for class balancing approaches based on integrated CNN and decision making techniques for lung cancer diagnosis

Jassim

Jaber

2022

EEJET

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Lung cancer is the fastest-growing and most dangerous type of cancer worldwide. It ranks first among cancer diseases in the number of deaths, and diagnosing it at late stages makes treatment more difficult. Artificial intelligence has played an essential role in the medical field in general, and early diagnosis of diseases and analyzing medical images in particular, as it can reduce human errors that may occur with the medical expert in medical image analysis. In this study, a hybrid framework is proposed between deep learning using the proposed convolutional neural network and multi-criteria decision-making techniques in order to reach an effective and accurate classification model for lung cancer diagnosis and select the best methodology to solve the problem of class imbalance datasets, which is a general problem in medical data that causes problems and errors in prediction. The IQ-OTHNCCD dataset that has a class imbalance was used. Three class balancing techniques were used separately and the data from each one enters the proposed convolutional neural network for feature extraction and classification. Then the Fuzzy-Weighted Zero-Inconsistency algorithm and VIKOR were used to make the ranking for the best classification approach and determine the best technique to balance the classes. This contributed to increasing the efficiency of the classification, where the best model got an accuracy of 99.27 %, sensitivity of 99.33 %, specificity of 99 %, precision of 98.67 % and F1-score of 99 %. This study can be applied to any data that suffers from the class imbalance problem to find the best technique that gives the highest classification accuracy.

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Evaluation of SVM performance in the detection of lung cancer in marked CT scan dataset

Cited by 46 publications

References 0 publications

A Transfer Learning Approach with a Convolutional Neural Network for the Classification of Lung Carcinoma

A Transfer Learning Approach with a Convolutional Neural Network for the Classification of Lung Carcinoma

Network intrusion detection system: machine learning approach

Hybrid selection framework for class balancing approaches based on integrated CNN and decision making techniques for lung cancer diagnosis

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