Computer Assisted Diagnostic System in Tumor Radiography

Faisal, Ahmed; Parveen, Sharmin; Badsha, Shahriar; Sarwar, Hasan; Reza, Ahmed Wasif

doi:10.1007/s10916-013-9938-3

Cited by 14 publications

(7 citation statements)

References 20 publications

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“…Hence, it generates the requirement of developing automated diagnostic systems for analysis and classification of such medical images. These intelligent systems can be a great instrument for the medical personnel in diagnosis, pre-surgical, and post-surgical procedures [ 6 – 10 ].…”

Section: Introductionmentioning

confidence: 99%

An Automated and Intelligent Medical Decision Support System for Brain MRI Scans Classification

2015

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A wide interest has been observed in the medical health care applications that interpret neuroimaging scans by machine learning systems. This research proposes an intelligent, automatic, accurate, and robust classification technique to classify the human brain magnetic resonance image (MRI) as normal or abnormal, to cater down the human error during identifying the diseases in brain MRIs. In this study, fast discrete wavelet transform (DWT), principal component analysis (PCA), and least squares support vector machine (LS-SVM) are used as basic components. Firstly, fast DWT is employed to extract the salient features of brain MRI, followed by PCA, which reduces the dimensions of the features. These reduced feature vectors also shrink the memory storage consumption by 99.5%. At last, an advanced classification technique based on LS-SVM is applied to brain MR image classification using reduced features. For improving the efficiency, LS-SVM is used with non-linear radial basis function (RBF) kernel. The proposed algorithm intelligently determines the optimized values of the hyper-parameters of the RBF kernel and also applied k-fold stratified cross validation to enhance the generalization of the system. The method was tested by 340 patients’ benchmark datasets of T1-weighted and T2-weighted scans. From the analysis of experimental results and performance comparisons, it is observed that the proposed medical decision support system outperformed all other modern classifiers and achieves 100% accuracy rate (specificity/sensitivity 100%/100%). Furthermore, in terms of computation time, the proposed technique is significantly faster than the recent well-known methods, and it improves the efficiency by 71%, 3%, and 4% on feature extraction stage, feature reduction stage, and classification stage, respectively. These results indicate that the proposed well-trained machine learning system has the potential to make accurate predictions about brain abnormalities from the individual subjects, therefore, it can be used as a significant tool in clinical practice.

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Section: Introductionmentioning

confidence: 99%

An Automated and Intelligent Medical Decision Support System for Brain MRI Scans Classification

2015

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“…The method of tumor detection based on the texture of the MRI is proposed to segment the abnormality from the regular adjacent, although small quantities misclassified, but the segmentation result is precise. Moreover, [10] suggested a strong technique for automatic detection of tumor for brain MRI images. He also proposed a new morphological technique which is used for the stripped skull region from the brain images.…”

Section: Literature Reviewmentioning

confidence: 99%

Improving the MRI Tumor Segmentation Process Using Appropriate Image Processing Techniques

Al-Othman¹,

Al-Ameen²,

Sulong³

2014

IJIGSP

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Segmenting tumor from MRI images is an essential but time consuming manual duty. Performing an automatic segmentation is a defying task since different forms of tumor tissue exist for diverse patients and in many cases the tumor is similar to the normal tissue. Various studies proposed earlier to handle the issue of precisely segmenting the tumor but they discard the degradations and their effect to the precision of the segmentation. This article provides a more precise segmentation process through the use of appropriate preprocessing algorithms. The authors studied many enhancement and restoration algorithms and selected the NL-means, Laplacian filter and histogram equalization to be used as preprocessing techniques. Experimental results showed that using a suitable preprocessing scheme would produce a better segmentation process.

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“…To adjust for the absence of preparing information, these repeat between sectioning the picture and describing the properties of the each one class. Dynamic shape techniques perform segmentation using both force and geometrical data of items to be portioned [12,13,14,15].…”

Section: Background Studymentioning

confidence: 99%

Development of KITE image processing technique for brain tumor detection by interfacing patient's Live Brain Image

Patil

2014

2014 Annual IEEE India Conference (INDICON)

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In this paper new technique called 'KITE' is implemented for brain tumor growth analysis. This is done by using gray images pixel count inside kite shape boundaries. We also differentiated gray color shades to focus over the outgrowth of tumor. As we know in medical field, countless radiological and pathological images in advanced organization are developed commonly in clinics and medical focuses with refined picture securing gadgets and computerized scanners. Medical images are for the most part intricate in nature and are utilized for diagnosis, therapy, exploration and training. Our implemented KITE technique proves the most cost effective and high precision application for detection of brain tumor.

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Computer Assisted Diagnostic System in Tumor Radiography

Cited by 14 publications

References 20 publications

An Automated and Intelligent Medical Decision Support System for Brain MRI Scans Classification

An Automated and Intelligent Medical Decision Support System for Brain MRI Scans Classification

Improving the MRI Tumor Segmentation Process Using Appropriate Image Processing Techniques

Development of KITE image processing technique for brain tumor detection by interfacing patient's Live Brain Image

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