MRI brain tumor detection using optimal possibilistic fuzzy C-means clustering algorithm and adaptive k-nearest neighbor classifier

Kumar, Dhirendra; Satyanarayana, D.; Prasad, M. N. Giri

doi:10.1007/s12652-020-02444-7

Cited by 56 publications

(20 citation statements)

References 24 publications

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“…Extracting and selecting informative features is also tricky because it decreases classification accuracy (Amin et al, 2022). According to the literature review, numerous machine learning techniques have been employed to categorize MRI images (Rehman et al, 2020;Zhou et al, 2020;Kumar et al, 2021). Recent advances in machine learning have led to the application of numerous deep learning approaches for diagnosing MRI images (Alanazi et al, 2022;Alrashedy et al, 2022;Qureshi et al, 2022;Senan et al, 2022;Zeineldin et al, 2022).…”

Section: Motivationmentioning

confidence: 99%

“…These techniques are implemented on the BraTs 2012 dataset for both natural and syntactic images, and the proposed model indicates the result with the best accuracy is 0.98%, sensitivity is 0.92%, specificity is 0.96%, precision is 0.88%, and the dice score is 0.88%. The automatic brain tumor classification system is proposed in Kumar et al (2021) and the K-nearest neighbor algorithm is used to classify the MRI images as abnormal or normal. The fuzz C-means clustering technique is used for the segmentation of tumor regions.…”

Section: Machine Learning Techniquesmentioning

confidence: 99%

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Ensemble deep learning for brain tumor detection

Alsubai

Khan

Alqahtani

et al. 2022

Front. Comput. Neurosci.

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With the quick evolution of medical technology, the era of big data in medicine is quickly approaching. The analysis and mining of these data significantly influence the prediction, monitoring, diagnosis, and treatment of tumor disorders. Since it has a wide range of traits, a low survival rate, and an aggressive nature, brain tumor is regarded as the deadliest and most devastating disease. Misdiagnosed brain tumors lead to inadequate medical treatment, reducing the patient's life chances. Brain tumor detection is highly challenging due to the capacity to distinguish between aberrant and normal tissues. Effective therapy and long-term survival are made possible for the patient by a correct diagnosis. Despite extensive research, there are still certain limitations in detecting brain tumors because of the unusual distribution pattern of the lesions. Finding a region with a small number of lesions can be difficult because small areas tend to look healthy. It directly reduces the classification accuracy, and extracting and choosing informative features is challenging. A significant role is played by automatically classifying early-stage brain tumors utilizing deep and machine learning approaches. This paper proposes a hybrid deep learning model Convolutional Neural Network-Long Short Term Memory (CNN-LSTM) for classifying and predicting brain tumors through Magnetic Resonance Images (MRI). We experiment on an MRI brain image dataset. First, the data is preprocessed efficiently, and then, the Convolutional Neural Network (CNN) is applied to extract the significant features from images. The proposed model predicts the brain tumor with a significant classification accuracy of 99.1%, a precision of 98.8%, recall of 98.9%, and F1-measure of 99.0%.

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Section: Motivationmentioning

confidence: 99%

Section: Machine Learning Techniquesmentioning

confidence: 99%

Ensemble deep learning for brain tumor detection

Alsubai

Khan

Alqahtani

et al. 2022

Front. Comput. Neurosci.

View full text Add to dashboard Cite

show abstract

“…The artificial neural network classifier model is constructed using various information processing units that are more analogous to neurons in the brain. It follows the feed-forward back propagation learning mechanism to classify the MRI into tumor and non-tumor [29][30][31][32].…”

Section: Brain Tumor Segmentation Approachesmentioning

confidence: 99%

Real Time Brain Tumor Prediction Using Adaptive Neuro Fuzzy Technique

Nagendiran¹,

Chokkalingam²

2022

Intelligent Automation &Amp; Soft Computing

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Uncontrollable growth of cells may lead to brain tumors and may cause permanent damages to the brain or even death. To make early diagnosis and treatment, identifying the position and size of tumors is identified as a tedious and troublesome problem among the existing computer-aided diagnosis systems. Moreover, the progression of tumors may vary among the patients with respect to shape, location, and volume. Therefore, to effectively classify and diagnose the brain tumor images according to severity stages follows the sequence of processing such as pre-processing, segmentation, feature extraction, and classification techniques to carrying out the appropriate treatment. To enhance the performance of brain tumors detection and diagnosis, an adaptive neuro-fuzzybased suggestion system (ANFSS) is proposed with an effective shape-based feature selection technique. Then, the performance of proposed ANFSS is compared with existing classifier models in terms of brain tumor detection and proposed model achieves 98.8% accuracy in prediction of tumor.

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“…The proposed TPOT performs better than manual expert pipeline optimization and qualitative expert MRI review. In [107], an automatic classification method to effectively delineate brain tumors at an earlier stage using MRI images from different databases was presented. The methodology was outlined as pre-processing via Median Filter, 3×3 block conversion of images, extraction of texture features using gray-Level Co-Occurrence Matrix, classification, and segmentation.…”

Section: ) Ml-based Approaches In Brain Tumor Diagnosismentioning

confidence: 99%

Machine Learning and Deep Learning Approaches for Brain Disease Diagnosis: Principles and Recent Advances

et al. 2021

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Brain is the controlling center of our body. With the advent of time, newer and newer brain diseases are being discovered. Thus, because of the variability of brain diseases, existing diagnosis or detection systems are becoming challenging and are still an open problem for research. Detection of brain diseases at an early stage can make a huge difference in attempting to cure it. In recent years, the use of artificial intelligence (AI) is surging through all spheres of science, and no doubt, it is revolutionizing the field of neurology. Application of AI in medical science has made brain disease prediction and detection more accurate and precise. In this study, we present a review on recent machine learning and deep learning approaches in detecting four brain diseases such as Alzheimer's disease (AD), brain tumor, epilepsy, and Parkinson's disease. 147 recent articles on four brain diseases are reviewed considering diverse machine learning and deep learning approaches, modalities, datasets etc. Twenty-two datasets are discussed which are used most frequently in the reviewed articles as a primary source of brain disease data. Moreover, a brief overview of different feature extraction techniques that are used in diagnosing brain diseases is provided. Finally, key findings from the reviewed articles are summarized and a number of major issues related to machine learning/deep learning-based brain disease diagnostic approaches are discussed. Through this study, we aim at finding the most accurate technique for detecting different brain diseases which can be employed for future betterment. INDEX TERMS Alzheimer's disease, brain tumor, deep learning, epilepsy, Parkinson's disease, machine learning NOMENCLATURE The most commonly used abbreviations in this survey are summarized in Table 1.

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MRI brain tumor detection using optimal possibilistic fuzzy C-means clustering algorithm and adaptive k-nearest neighbor classifier

Cited by 56 publications

References 24 publications

Ensemble deep learning for brain tumor detection

Ensemble deep learning for brain tumor detection

Real Time Brain Tumor Prediction Using Adaptive Neuro Fuzzy Technique

Machine Learning and Deep Learning Approaches for Brain Disease Diagnosis: Principles and Recent Advances

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