Classification of MRI brain tumor and mammogram images using learning vector quantization neural network

Sonavane, Ravindra; Sonar, Poonam; Sutar, Surendra

doi:10.1109/ssps.2017.8071610

Cited by 6 publications

(5 citation statements)

References 14 publications

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“…The proposed method of brain tumor classification has been implemented on different normal, benign and malignant real MR images and the specificity, sensitivity and the PNN accuracy classifier has been measured, implementing the equations given above. Table 1 depicts the accuracy of the proposed method compared to the accuracy of the systems created by [12].…”

Section: Resultsmentioning

confidence: 99%

“…The feature extraction such as texture feature, method named gray level co-occurrence matrix (GLCM) is implemented. The created algorithm obtained the sensitivity of 82.50%, specificity of 42.6% and accuracy of 68.85% for breast cancer database while it obtains sensitivity of 81.82%, specificity of 77.53% and accuracy of 79.35% for database of clinical Brain MRI [12].…”

Section: Related Workmentioning

confidence: 99%

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ON-Line MRI Image Selection and Tumor Classification using Artificial Neural Network

Ahmed¹

2020

IHJPAS

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When soft tissue planning is important, usually, the Magnetic Resonance Imaging (MRI) is a medical imaging technique of selection. In this work, we show a modern method for automated diagnosis depending on a magnetic resonance images classification of the MRI. The presented technique has two main stages; features extraction and classification. We obtained the features corresponding to MRI images implementing Discrete Wavelet Transformation (DWT), inverse and forward, and textural properties, like rotation invariant texture features based on Gabor filtering, and evaluate the meaning of every property in the classification. The classifier is according to Feed Forward Back Propagation Artificial Neural Network (FP-ANN) in the classification stage. The properties thereafter derived to be implemented to teach a neural network based binary classifier that will be automatically able to conclude whether the image is that of a pathological, suffering from brain lesion, or a normal brain. The proposed algorithm obtained the sensitivity of 97.50%, specificity of 82.86% and accuracy of 94.3% for clinical Brain MRI database. This outcome proofs that the presented algorithm is robust and effective compared with other recent techniques.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

ON-Line MRI Image Selection and Tumor Classification using Artificial Neural Network

Ahmed¹

2020

IHJPAS

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“…The Brain Tumor Classification dataset from Kaggle is used to evaluate the suggested model's performance and accuracy. There are 2870 MRIs in the entire dataset [5]. Training and testing datasets are two separate sections of the dataset.…”

Section: Data Setmentioning

confidence: 99%

Brain Tumour Detection Using Resnet 50 and Mobilenet

G.V.S.N.R.V. Prasad

2023

IJRITCC

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The scientific community defines a brain tumour as a mass or growth of abnormal cells in the brain. A brain tumour is a development of abnormal cells, some of which may develop into cancer. MRI scans are the most common way to find brain tumours and are used to detect brain cancer. There are different types of tumours exist. They are cancerous(malignant)and non- cancerous(benign) in the brain identification of unchecked tissue growth in MRI may help us diagnose brain cancer. Machine Learning and Deep Learning algorithms are used to identify this tissue growth. When these algorithms are applied to MRI scans, a faster prediction of brain tumours is made, and a better degree of accuracy aids in treating patients. MRI scans allow us to perform rapid analysis and identify the exact location of unwanted tissue growth. Various uses include image recognition and identifying objects, image classification, segmentation, neural network and data processing. The proposed model successfully classified the MRI image into four classes: glioma, meningioma, and pituitary tumour and no tumour, indicating that the given brain MRI has no tumour. In this paper the proposed models are MobileNet and Resnet50 and gives accuracy of 0.98. These models classifies the type of tumour very accurately.

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“…In contrast, conducting computer vision-based research related to the identification of arteries for use in biometric systems [7]. The results of the researchers explain that each has a different vein shape and has a unique, in feature extraction using the symmetry phase method to separate arteries with other parts of the palm.…”

Section: Introductionmentioning

confidence: 99%

Detection of Industrial Machine Work Errors using LVQ Neural Network

Rasyid¹,

Tahir²,

Syafaruddin³

2019

Proceedings of the 1st International Conference on Science and Technology, ICOST 2019, 2-3 May, Makassar, Indonesia

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In the world of industry, the utilization of technology machinery industry is one of the most important factors to facilitate the employment of human. However, an industrial machine does not work regardless of fault that can inhibit the production process and cause harm to the industry. This paper aims to detect errors with the industrial machine work to analyze the movement of industrial machinery in a video, at this stage of the process of preprocessing, image resizes, do segmentation method thresholding, and the morphological operations with the opening operation. The further step, the feature extraction performed by converting a binary image into vector data is used as input data in the classification process using Algorithm Learning Vector Quantization (LVQ) Neural Network version 1 and version 2. Research results obtained detection accuracy reached 100% for training using LVQ1 much higher than the results of the training using LVQ2 with an accuracy of only 67.59%.

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Classification of MRI brain tumor and mammogram images using learning vector quantization neural network

Cited by 6 publications

References 14 publications

ON-Line MRI Image Selection and Tumor Classification using Artificial Neural Network

ON-Line MRI Image Selection and Tumor Classification using Artificial Neural Network

Brain Tumour Detection Using Resnet 50 and Mobilenet

Detection of Industrial Machine Work Errors using LVQ Neural Network

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