An Improved Segmentation and Classifier Approach Based on HMM for Brain Cancer Detection

Sharma, Shruti; Rattan, Munish

doi:10.2174/1874120701913010033

Cited by 8 publications

(5 citation statements)

References 7 publications

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“…Existing work reported in the literature has shown that the classification of brain images is possible via supervised techniques such as ANN, Bayesian networks, k-NN, GMM, HMM, decision tree induction, rule-based classification, PCA and SVM [88,166,182], and via unsupervised techniques such as SOM and FCM. In reality, unsupervised classification, which does not require training data, has not been widely used in CAD systems, due to the specificity of the brain images in which the CAD system should be trained according to the truth field or clinical evidence.…”

Section: Critical Discussion About Classification Techniquesmentioning

confidence: 99%

“…It is possible to express a posterior probability of a label field from an observation in hidden Markov models (HMM), thanks to the Bayes theorem. HMMs [84][85][86][87][88] make it possible to model arbitrary characteristics of observations, making it possible to inject knowledge specific to the problem encountered into the model, in order to produce an ever finer resolution of spectral, spatial and temporal data. In the case of HMMs, the types of previous distributions that can be placed on masked states are severely limited; it is not possible to predict the probability of seeing an arbitrary observation.…”

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confidence: 99%

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A Survey on Computer-Aided Diagnosis of Brain Disorders through MRI Based on Machine Learning and Data Mining Methodologies with an Emphasis on Alzheimer Disease Diagnosis and the Contribution of the Multimodal Fusion

2020

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Computer-aided diagnostic (CAD) systems use machine learning methods that provide a synergistic effect between the neuroradiologist and the computer, enabling an efficient and rapid diagnosis of the patient’s condition. As part of the early diagnosis of Alzheimer’s disease (AD), which is a major public health problem, the CAD system provides a neuropsychological assessment that helps mitigate its effects. The use of data fusion techniques by CAD systems has proven to be useful, they allow for the merging of information relating to the brain and its tissues from MRI, with that of other types of modalities. This multimodal fusion refines the quality of brain images by reducing redundancy and randomness, which contributes to improving the clinical reliability of the diagnosis compared to the use of a single modality. The purpose of this article is first to determine the main steps of the CAD system for brain magnetic resonance imaging (MRI). Then to bring together some research work related to the diagnosis of brain disorders, emphasizing AD. Thus the most used methods in the stages of classification and brain regions segmentation are described, highlighting their advantages and disadvantages. Secondly, on the basis of the raised problem, we propose a solution within the framework of multimodal fusion. In this context, based on quantitative measurement parameters, a performance study of multimodal CAD systems is proposed by comparing their effectiveness with those exploiting a single MRI modality. In this case, advances in information fusion techniques in medical imagery are accentuated, highlighting their advantages and disadvantages. The contribution of multimodal fusion and the interest of hybrid models are finally addressed, as well as the main scientific assertions made, in the field of brain disease diagnosis.

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Section: Critical Discussion About Classification Techniquesmentioning

confidence: 99%

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confidence: 99%

A Survey on Computer-Aided Diagnosis of Brain Disorders through MRI Based on Machine Learning and Data Mining Methodologies with an Emphasis on Alzheimer Disease Diagnosis and the Contribution of the Multimodal Fusion

2020

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“…The World Health Organization states that degenerative diseases are the leading cause of death worldwide in the population aged 65 and older, with a higher death toll in developing countries. According to study [13], an estimated 23% of women and 14% of men aged over 65 suffer from degenerative diseases. The global prevalence of hypertension is estimated to be around 15-20%, with a higher incidence in the age group of 55-64 years.…”

Section: A Degenerative Diseasesmentioning

confidence: 99%

Fuzzy Deep Learning Approach for the Early Detection of Degenerative Disease

Irianto,

Karnila

et al. 2024

IJACSA

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Degenerative diseases can impact individuals of any age, encompassing children and teenagers; however, they typically tend to affect productive or adult individuals. Globally, conventional and advanced diagnostic methods, including those developed in Indonesia, have emerged to identify and manage these health conditions. Problems in brain tumor detection are the intricate process of precisely and effectively identifying the presence of tumors in the brain. On the other hand, diagnosing brain tumors in the laboratory poses issues related to time consumption, inaccuracy, lack of consistency, and costliness. This study specifically concentrates on the early detection of brain tumors by analyzing images generated through MRI scans. Unlike the traditional method of manual image analysis conducted by seasoned physicians, our approach integrates fuzzy logic to enable the early identification of brain tumors. The principal objective of this research is to enhance understanding and develop an intelligent, swift, and precise application for diagnosing brain tumors using medical imaging. The segmentation technique provides practical technology for the early detection of brain tumors. Utilizing a dataset comprising over 13,000 data points and undergoing a year-long training process with approximately 1,310 MRI images, the research culminates in the creation of a tool or software application system for the analysis of medical images. Despite the impressive precision score of 0.9992, highlighting its exceptional accuracy in correctly identifying positive instances, the recall value of 0.5767 suggests the potential exclusion of a significant number of actual positive instances in its predictions.

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“…Hidden Markov Models (HMM) have been proven to perform significantly better than Support Vector Regression (SVR) models when used for brain tumor segmentation. Particular research has focused this topic on how HMM can produce a better Peak Signal-to-Noise Ratio (PSNR) and Mean Squared Error (MSE) [26]. The research applies a HMM to a two-dimensional MRI scan, extracted from the BITE dataset [27].…”

Section: Classifiersmentioning

confidence: 99%