Adopting Run Length Features to Detect and Recognize Brain Tumor in Magnetic Resonance Images

Derea, Aya S; Abbas, Heba Kh.; Mohamad, Haidar J.; Al-Zuky, Ali A.

doi:10.1109/cas47993.2019.9075718

Cited by 5 publications

(3 citation statements)

References 9 publications

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“…The automatic characterization algorithms address the features within the studied images to represent the biological data for tumour and related diseases [17]. Consequent analysis, statistics, classification, and nomogram describe image features in the image [18]. Some studies of MRI to diagnose breast cancer are based on radiomics quantified analysis [19].…”

Section: Introductionmentioning

confidence: 99%

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Texture Features of Grey Level Size Zone Matrix for Breast Cancer Detection

Abtan

Al-Saleh

Mohamad

et al. 2023

Iraqi Journal of Science

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The texture analysis of cancer cells leads to a procedure to distinguish spatial differences within an image and extract essential information. This study used two test tumours images to determine cancer type, location, and geometric characteristics (area, size, dimensions, radius, etc.). The suggested algorithm was designed to detect and distinguish breast cancer using the segmentation-based threshold technique. The method of texture analysis Grey Level Size Zone method was used to extract 11 features: Small Zone Emphasis, Large Zone Emphasis, Low Grey Level Zone Emphasis, High Grey Level Zone Emphasis, Small Zone Low Grey Level Emphasis, Small Zone High Grey Level Emphasis, Large Zone Low Grey Level Emphasis, Large Zone High Grey Level Emphasis, Grey Level Non-Uniformity Normalized, Grey Level Non-Uniformity, and Zone Percentage. The results show that the tumuor's location is highly accurate depending on the extracting properties. The results of the suggested method give the decision to identify the type of tumours. The geometry of the tumour helps describe the tumour.

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Section: Introductionmentioning

confidence: 99%

“…The studied features with its equation for the GLSZM[18] grey level and t size zone for grey level; B: size zone image for all image pixel value.…”

mentioning

confidence: 99%

Texture Features of Grey Level Size Zone Matrix for Breast Cancer Detection

Abtan

Al-Saleh

Mohamad

et al. 2023

Iraqi Journal of Science

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“…An examination of gray levels and the extraction of textural features were carried out in order to portray the physiological changes in fundus images [ 2 ] produced by the increase in cup size.…”

Section: Introductionmentioning

confidence: 99%

Glaucoma Detection Using Image Processing and Supervised Learning for Classification

Joshi

Partibane²,

Hatamleh

et al. 2022

Journal of Healthcare Engineering

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A difficult challenge in the realm of biomedical engineering is the detection of physiological changes occurring inside the human body, which is a difficult undertaking. At the moment, these irregularities are graded manually, which is very difficult, time-consuming, and tiresome due to the many complexities associated with the methods involved in their identification. In order to identify illnesses at an early stage, the use of computer-assisted diagnostics has acquired increased attention as a result of the requirement of a disease detection system. The major goal of this proposed work is to build a computer-aided design (CAD) system to help in the early identification of glaucoma as well as the screening and treatment of the disease. The fundus camera is the most affordable image analysis modality available, and it meets the financial needs of the general public. The extraction of structural characteristics from the segmented optic disc and the segmented optic cup may be used to characterize glaucoma and determine its severity. For this study, the primary goal is to estimate the potential of the image analysis model for the early identification and diagnosis of glaucoma, as well as for the evaluation of ocular disorders. The suggested CAD system would aid the ophthalmologist in the diagnosis of ocular illnesses by providing a second opinion as a judgment made by human specialists in a controlled environment. An ensemble-based deep learning model for the identification and diagnosis of glaucoma is in its early stages now. This method’s initial module is an ensemble-based deep learning model for glaucoma diagnosis, which is the first of its kind ever developed. It was decided to use three pretrained convolutional neural networks for the categorization of glaucoma. These networks included the residual network (ResNet), the visual geometry group network (VGGNet), and the GoogLeNet. It was necessary to use five different data sets in order to determine how well the proposed algorithm performed. These data sets included the DRISHTI-GS, the Optic Nerve Segmentation Database (DRIONS-DB), and the High-Resolution Fundus (HRF). Accuracy of 91.11% for the PSGIMSR data set and the sensitivity of 85.55% and specificity of 95.20% for the suggested ensemble architecture on the PSGIMSR data set were achieved. Similarly, accuracy rates of 95.63%, 98.67%, 95.64%, and 88.96% were achieved using the DRIONS-DB, HRF, DRISHTI-GS, and combined data sets, respectively.

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Brain Tumor Detection Using Convolutional Neural Network and Transfer Learning Approach

Tyagi,

Priyanka

2023

2023 International Conference on Advanced Computing &Amp; Communication Technologies (ICACCTech)

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Adopting Run Length Features to Detect and Recognize Brain Tumor in Magnetic Resonance Images

Cited by 5 publications

References 9 publications

Texture Features of Grey Level Size Zone Matrix for Breast Cancer Detection

Texture Features of Grey Level Size Zone Matrix for Breast Cancer Detection

Glaucoma Detection Using Image Processing and Supervised Learning for Classification

Brain Tumor Detection Using Convolutional Neural Network and Transfer Learning Approach

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