Detection of Brain Tumor in MRI Images, Using a Combination of Fuzzy C-Means and Thresholding

Hamad, Yousif; Simonov, Konstantin; Naeem, Mohammad

doi:10.4018/ijapuc.2019010104

Cited by 12 publications

(3 citation statements)

References 18 publications

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“…As the human mind while making a decision or arriving at a conclusion, at first it collects some relevant data, then some partial facts are generated from that data (Mathur, 2018). Fuzzy rule plays important in complex and mysterious systems (Hamad Y. A. and Naeem M. B.,2019).…”

Section: First Classification Systemmentioning

confidence: 99%

Appending Global to Local features for Skin Lesion Classification on Dermoscpic Images

Mahmood

2021

JER is an international, peer-reviewed journal that publishes f

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Skin cancer is the deadliest diseases compared with all other kinds of cancer. In this paper various pre- and post-treatments are proposed for improving automated melanoma diagnosis of dermoscopy images. At first pre-processing have done to exclude unwanted parts, a new triple-A segmentation proposes to extract lesion according to their histogram patterns. Lastly, suggest appending process with testing many factors for superior detection decision. This paper offers a novel approach with testing different detection rules: first system used fuzzy rules based on a different features, a second test has been done by modeled local colours with bag-of-features classifier. Then proposed adding lesion shape on two previous systems as their global form in the first one, while distributing it and appending with local colour patches in the second system. For each case, different features; various colour models, and many other parameters are examined to decide which settings are more discriminating. Evaluates performance of each method has carried out on (ISIC2019 Challenge) dermoscopic database. The novel processes with their a specific parameters are rising the classification accuracy to 98.26%.

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Section: First Classification Systemmentioning

confidence: 99%

Appending Global to Local features for Skin Lesion Classification on Dermoscpic Images

Mahmood

2021

JER is an international, peer-reviewed journal that publishes f

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“…Nonetheless, SVMs may be used unsupervised as well [9]. Researchers have suggested using an improved support vector machine (ISVM) classifier to classify brain cancers [11,12]. The proposed approach uses a dataset of images processed through the K-means segmentation technique to identify abnormal cells in MRI scans as cancer.…”

Section: Introductionmentioning

confidence: 99%

Brain Pathology Classification of MR Images Using Machine Learning Techniques

Ramaha,

Mahmood,

Hameed

et al. 2023

Computers

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A brain tumor is essentially a collection of aberrant tissues, so it is crucial to classify tumors of the brain using MRI before beginning therapy. Tumor segmentation and classification from brain MRI scans using machine learning techniques are widely recognized as challenging and important tasks. The potential applications of machine learning in diagnostics, preoperative planning, and postoperative evaluations are substantial. Accurate determination of the tumor’s location on a brain MRI is of paramount importance. The advancement of precise machine learning classifiers and other technologies will enable doctors to detect malignancies without requiring invasive procedures on patients. Pre-processing, skull stripping, and tumor segmentation are the steps involved in detecting a brain tumor and measurement (size and form). After a certain period, CNN models get overfitted because of the large number of training images used to train them. That is why this study uses deep CNN to transfer learning. CNN-based Relu architecture and SVM with fused retrieved features via HOG and LPB are used to classify brain MRI tumors (glioma or meningioma). The method’s efficacy is measured in terms of precision, recall, F-measure, and accuracy. This study showed that the accuracy of the SVM with combined LBP with HOG is 97%, and the deep CNN is 98%.

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“…It takes an essential part in medical imaging. This process has been useful in several medical areas, such as brain tumor detection [2], cancer diagnosis [3], blood vessels analysis [4] and diabetic retinopathy [5]. It can assist doctors and radiologists to diagnose illnesses, therapy evaluation, tissue volume measurements, aid in computer guided surgery, planning for treatments, anatomical structure study and surgery simulator [6].…”

Section: Introductionmentioning

confidence: 99%

A New Hybrid Image Segmentation Method Based on Fuzzy C-Mean and Modified Bat Algorithm

Boulanouar¹,

Chaabane²

2020

IJCDS

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Magnetic resonance imaging (MRI) plays an important role in clinical diagnosis, because of that it has attracted increasing attention in recent years. The symptom of many diseases corresponds to the brain's structural variants. The detection of various diseases has became very useful through the segmentation methods. Fuzzy c-means (FCM) considers among the popular clustering algorithms for medical image segmentation. However, FCM is sensitive to the noise and falls into local optimal solution easily because of the random initialization of the cluster centers. In this research, we propose a hybrid method based on modified fuzzy bat algorithm (MFBA) and the FCM clustering algorithm named MFBAFCM. This developed approach uses the MFBA to get better initial cluster centers for the FCM algorithm by using a new fitness function, which combines intra cluster distance with fuzzy cluster validity indices. Experimental results on several MRI brain images corrupted by different levels of intensity non-uniformity and noise, show that the proposed method produced better results than the standard FCM and some other recent published works.

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Detection of Brain Tumor in MRI Images, Using a Combination of Fuzzy C-Means and Thresholding

Cited by 12 publications

References 18 publications

Appending Global to Local features for Skin Lesion Classification on Dermoscpic Images

Appending Global to Local features for Skin Lesion Classification on Dermoscpic Images

Brain Pathology Classification of MR Images Using Machine Learning Techniques

A New Hybrid Image Segmentation Method Based on Fuzzy C-Mean and Modified Bat Algorithm

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