Application of machine learning on brain cancer multiclass classification

Panca, V.; Rustam, Zuherman

doi:10.1063/1.4991237

Cited by 27 publications

(9 citation statements)

References 11 publications

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“…In our case of Multiclass Classification Issue to be an 8-Class Classification Issue since we have a dataset that has eight class names, sensitivity, precision, and consistency are very critical for the system. Sensitivity reflected the proportion of both positive and genuinely positive events [ 52 ]. Specificity showed the percentage of true negative classified cases.…”

Section: Resultsmentioning

confidence: 99%

Implementing Multilabeling, ADASYN, and ReliefF Techniques for Classification of Breast Cancer Diagnostic through Machine Learning: Efficient Computer-Aided Diagnostic System

Khan

et al. 2021

Journal of Healthcare Engineering

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Multilabel recognition of morphological images and detection of cancerous areas are difficult to locate in the scenario of the image redundancy and less resolution. Cancerous tissues are incredibly tiny in various scenarios. Therefore, for automatic classification, the characteristics of cancer patches in the X-ray image are of critical importance. Due to the slight variation between the textures, using just one feature or using a few features contributes to inaccurate classification outcomes. The present study focuses on five different algorithms for extracting features that can extract further different features. The algorithms are GLCM, LBGLCM, LBP, GLRLM, and SFTA from 8 image groups, and then, the extracted feature spaces are combined. The dataset used for classification is most probably imbalanced. Additionally, another focal point is to eradicate the unbalanced data problem by creating more samples using the ADASYN algorithm so that the error rate is minimized and the accuracy is increased. By using the ReliefF algorithm, it skips less contributing features that relieve the burden on the process. Finally, the feedforward neural network is used for the classification of data. The proposed method showed 99.5% micro, 99.5% macro, 0.5% misclassification, 99.5% recall rats, specificity 99.4%, precision 99.5%, and accuracy 99.5%, showing its robustness in these results. To assess the feasibility of the new system, the INbreast database was used.

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Section: Resultsmentioning

confidence: 99%

Implementing Multilabeling, ADASYN, and ReliefF Techniques for Classification of Breast Cancer Diagnostic through Machine Learning: Efficient Computer-Aided Diagnostic System

Khan

et al. 2021

Journal of Healthcare Engineering

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“…This type of deep learning networks is very effective for high-performance computer vision model, and they efficiently learn and extract many visual features for well generalizing tasks without the need for hand-crafted feature extraction [19]. Most of the existed methods are based on clustering algorithms, machine learning, or using the whole image based on deep learning algorithms [20][21][22][23]. The performance of these methods depends on the quality and the type of the extracted features which can be varied [15,24].…”

Section: Related Workmentioning

confidence: 99%

Glioblastomas brain tumour segmentation based on convolutional neural networks

Al-Hadidi

Al-Saaidah

Al-Gawagzeh

2020

IJECE

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Brain tumour segmentation can improve diagnostics efficiency, rise the prediction rate and treatment planning. This will help the doctors and experts in their work. Where many types of brain tumour may be classified easily, the gliomas tumour is challenging to be segmented because of the diffusion between the tumour and the surrounding edema. Another important challenge with this type of brain tumour is that the tumour may grow anywhere in the brain with different shape and size. Brain cancer presents one of the most famous diseases over the world, which encourage the researchers to find a high-throughput system for tumour detection and classification. Several approaches have been proposed to design automatic detection and classification systems. This paper presents an integrated framework to segment the gliomas brain tumour automatically using pixel clustering for the MRI images foreground and background and classify its type based on deep learning mechanism, which is the convolutional neural network. In this work, a novel segmentation and classification system is proposed to detect the tumour cells and classify the brain image if it is healthy or not. After collecting data for healthy and non-healthy brain images, satisfactory results are found and registered using computer vision approaches. This approach can be used as a part of a bigger diagnosis system for breast tumour detection and manipulation.

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“…is the fuzziness degree for cluster partition. Then we calculate the membership by the equation: (10) After that, we calculate the centroid (11)…”

Section: Fuzzy Kernel C-meansmentioning

confidence: 99%

Prediction of Disease-Resistant Gene in Rice Based on Support Vector Machine and Fuzzy Kernel C-Means

Saragih¹

2018

Proceedings of the 1st Annual International Conference on Mathematics, Science, and Education (ICoMSE 2017)

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Rice production in Indonesia is important especially for national economy. Rice is the staple food for Indonesian, so Indonesia is the biggest rice consumers in the world, averaging more than 200 kilograms per head each year. Therefore, Indonesia needs to be aware of rice production systems because there are many diseases that distract from the growth of rice. One of that is Bacterial Leaf Blight (BLB) causes severe damages in many rice cultivation regions of the world. Bacterial Leaf Blight disease control through the development of resistant varieties is one of the effective and easiest ways to apply to farmers. The computational method is a way out to solve this problem with the use of machine learning. However, the two most popular, efficient and high accuracy methods are Support Vector Machine (SVM) and Fuzzy Kernel C-Means. Therefore, we propose to compare these two methods to assess the chance of a protein being disease-resistant. In this research, we found that SVM is better than Fuzzy Kernel C-Means because SVM has represented protein information with 90.91% accuracy and Fuzzy Kernel C-Means with 80.58% accuracy in the model. However, if the researcher is stuck on finding data, based on this research Fuzzy Kernel C-Means gives the highest accuracy in smallest dataset then SVM. Fuzzy Kernel C-Means has represented protein information with 80.58% and SVM just 23.2% accuracy in 10% training data set

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Application of machine learning on brain cancer multiclass classification

Cited by 27 publications

References 11 publications

Implementing Multilabeling, ADASYN, and ReliefF Techniques for Classification of Breast Cancer Diagnostic through Machine Learning: Efficient Computer-Aided Diagnostic System

Implementing Multilabeling, ADASYN, and ReliefF Techniques for Classification of Breast Cancer Diagnostic through Machine Learning: Efficient Computer-Aided Diagnostic System

Glioblastomas brain tumour segmentation based on convolutional neural networks

Prediction of Disease-Resistant Gene in Rice Based on Support Vector Machine and Fuzzy Kernel C-Means

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