An intelligent content-based image retrieval system for clinical decision support in brain tumor diagnosis

Arakeri, Megha P.; Reddy, G. Ram Mohana

doi:10.1007/s13735-013-0037-5

Cited by 21 publications

(10 citation statements)

References 43 publications

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“…In 2013, Arakeri and Reddy [43]introduced an intellectual CBIR approach to diagnose the brain tumor. For this, the implemented model uses two steps: (a) Classify the query image and (b) retrievesimilar MR images.…”

Section: Related Workbased On Brain Tumor Classificationmentioning

confidence: 99%

A Compact Review on State-of-the-Art Brain Tumor Classification Models

Amit Uttarkar

2024

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The brain tumor is normally caused by the occurrence of abnormal cells in the brain region. Malignant or cancerous and benign tumors are the two major types in the brain tumor classification. Brain tumor classification is the process of differentiating various stages of tumors like grading of gliomas as well as primary gliomas from metastases. The diagnosis of a brain tumor was made by the study of MR images. Some of the notable brain tumor classification techniques are knowledge-based techniques, support vector machine classifiers (SVM), and neural network classifiers. This survey intends to provide a review of65 paperson the topic of brain tumor classification. Mainly, the review comes with two major aspects: the analysis of classification algorithms and the analysis of segmentation algorithms. At first, aclear literature review is made in terms of various brain tumor classification models. Subsequently, the analysis is made under the performance measure especially the accuracy rate is analyzed from all the reviewed papers. Further analysis is made regarding the used dataset, image modalities, and the used optimization concept as well. All the analytical results are explained in terms of tabulation and diagrammatic graphical representation. Finally, the clear problem statement is described showingthe different challenges faced in the classification process and the future direction that is to be made.

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Section: Related Workbased On Brain Tumor Classificationmentioning

confidence: 99%

A Compact Review on State-of-the-Art Brain Tumor Classification Models

Amit Uttarkar

2024

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“…Kini, kajian dalam CBIRS lebih popular berbanding dengan TBIRS (Guo et al 2016;Arakeri and Ram Mohana Reddy 2013;Madugunki et al 2011). Menurut (Kumar et al 2013), staf klinikal memilih kes yang serupa dengan mengutamakan ciri-ciri visual dalam menjalankan diagnosis dan rawatan.…”

Section: Tbirs Dan Cbirsunclassified

Content-based Image Retrieval for Medical Application

Zaki¹,

Ahmad²,

Hussain³

et al. 2018

JKUKM

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ABSTRAK Dapatan semula imej (DSI) adalah sistem pencarian imej yang menggunakan ciri-ciri tertentu atau konteks khusus Kata kunci: Imej perubatan; sistem dapatan semula imej; aplikasi perubatan; modaliti x-ray; TBIRS ABSTRACT Image retrieval is an image searching system that uses certain characteristics or context in an image. In a medical field, image retrieval system has been used to provide the needed correct images to the physicians while the diagnosis and treatment process is being conducted. The earlier image retrieval system was a text-based image retrieval system (TBIRS) which using keyword for the image context and it requires human's help to manually make text annotation on the images. This system is still being widely used in the hospitals nowadays. The text annotation process is a laborious task especially when dealing with a huge database and is prone to human errors. To overcome the aforementioned issues, the approach of a content-based image retrieval system (CBIRS) with automatic indexing using visual features such as color

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“…Several machine learning classifiers like K-Nearest Neighbour [29], multilevel SVM [29] and Back Propagation Neural Network [29] are used over selected relevant feature sets. The detailed performance analyses of tumor images over various FSClassifier combinations are shown in experimental results.…”

Section: Proposed Approachmentioning

confidence: 99%

Performance analysis of Gabor-Wavelet based features in classification of high grade malignant brain tumors

Vidyarthi

Mittal

2015

2015 39th National Systems Conference (NSC)

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Incorporating machine learning based concepts and knowledge in medical science domain elaborates the interdisciplinary research areas. Using such the identification of the disease is accomplished at initial state of its generation with better accuracy as compared to naked eye. In past, lot of feature extraction approaches were proposed to extract the features in spectral domain. Among all approaches, two major approaches was Gabor filter based feature extraction and Wavelet based feature extraction mechanism. But it leads to the kiosk that which feature extraction mechanism is better for classification perspective. This paper focuses on machine learning based approach for performance analysis for the grade of malignant tumor types using a diverse feature set based on Gabor and Wavelet transformation. Further, various feature selection (FS) algorithms are taken into consideration for selection of the best feature set among the feature vector. For experimental purpose, several state-of-art classifiers are used for analysis of the performance for the classification of malignant brain tumors in magnetic resonance (MR) images. In experimentation several high grade malignant brain tumors like Central Neuro Cytoma (CNC), Glioblastoma Multiforme (GBM), Gliomas, Intra Ventricular Malignant Mass, and Metastasis are taken into consideration having 30 images of each tumor type. The classification accuracies achieved using various combinations of FS-Classifier's are presented in detail.

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An intelligent content-based image retrieval system for clinical decision support in brain tumor diagnosis

Cited by 21 publications

References 43 publications

A Compact Review on State-of-the-Art Brain Tumor Classification Models

A Compact Review on State-of-the-Art Brain Tumor Classification Models

Content-based Image Retrieval for Medical Application

Performance analysis of Gabor-Wavelet based features in classification of high grade malignant brain tumors

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