Machine intelligence approach for optimization of cranial tumor image (original) (raw)
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Machine learning is at the heart of the big data rebellion sweeping the world today. It is the science of getting the computers to learn without being explicitly programmed as most of the technological systems are in an insurrection to be operated by intelligent machines capable to make the human like verdict to automatically solve human task with perfect results. Artificial Intelligence is the heart of every major technological system in the world today. This paper presents the challenges faced to develop a model to acquiesce excellent results and the different techniques of Machine learning here we also presents the broad view of the current techniques used for detection of Brain tumor in Computer aided diagnosis and a innovative method for detection of Brain tumor by Artificial intelligence using the algorithm of K-Nearest Neighbor. which is established on the training a model with different values of k and the an appropriate distance metrics is used for the distance calculation between pixels.
Comparative evaluation for detection of brain tumor using machine learning algorithms
IAES International Journal of Artificial Intelligence (IJ-AI)
Automated flaw identification has become more important in medical imaging. For patient preparation, unaided prediction of tumor (brain) detection in the magnetic resonance imaging process (MRI) is critical. Traditional ways of recognizing z are intended to make radiologists' jobs easier. The size and variety of molecular structures in brain tumors is one of the issues with MRI brain tumor diagnosis. Deep learning (DL) techniques (artificial neural network (ANN), naive Bayes (NB), multi-layer perceptron (MLP)) are used in this article to detect brain cancers in MRI data. The preprocessing techniques are used to eliminate textural features from the brain MRI images. These characteristics are then utilized to train a machine-learning system.
Classificatin of Brain Tumors by Machine Learning Algorithms (1)
The most prevalent illnesses in the world are skin diseases. Their tough skin texture, presence of hair on the skin, and colour make diagnosis exceedingly challenging. To improve the diagnostic accuracy of many kinds of skin disorders, techniques like machine learning must be developed. The application of machine learning methods in the medical profession is common for diagnosis. In order to decide, these algorithms employ feature values from photos as input. The feature extraction stage, the training stage, and the testing stage are the three steps of the procedure. Utilizing different skin imaging datasets, the technique trains itself using machine learning technologies. The goal of this procedure is to improve the diagnosis of skin diseases. Texture, colour, form, and their combinations are three crucial elements in picture categorisation. In this study, the skin illness is categorised using criteria of colour and texture. The hue of healthy skin differs from that of diseased skin. Using texture attributes in the photos, it is possible to distinguish between smoothness, coarseness, and regularity. In order to successfully diagnose skin illness, these two traits are investigated. In this study, the Hue-Saturation-Value (HSV) characteristics' entropy, variance, and maximum histogram value are employed. These characteristics are used in the Decision Tree (DT) and Support Vector Machine learning algorithms (SVM). Entropy is employed to divide the tree at the first level. Variance is employed at the second level to get leaves for texturing. In colour features, the HSV measure's highest histogram value is utilised to break the tree. The suggested algorithm's performance is evaluated using accuracy.
Healthcare Informatics Research, 2022
Objectives: A primary brain tumor starts to grow from brain cells, and it occurs as a result of errors in the DNA of normal cells. Therefore, this study was carried out to analyze the two-dimensional (2D) texture, morphology, and statistical features of brain tumors and to perform a classification using artificial intelligence (AI) techniques.Methods: AI techniques can help radiologists to diagnose primary brain tumors without using any invasive measurement techniques. In this paper, we focused on deep learning (DL) and machine learning (ML) techniques for texture, morphological, and statistical feature classification of three tumor types (namely, glioma, meningioma, and pituitary). T1-weighted magnetic resonance imaging (MRI) 2D scans were used for analysis and classification (multiclass and binary). A total of 102 features were calculated for each tumor, and the 20 most significant features were selected using the three-step feature selection method, which included removing duplic...
CLASSIFICATION AND DETECTION OF BRAIN TUMOR USING MACHINE LEARNING
Artificial Intelligence * Machines- CSIBER Press, 2023
Many consider brain tumours one of the most prevalent forms of cancer as one of the world's most severe diseases. Brain tumours kill thousands of individuals annually worldwide because of the tumour cells' accelerated proliferation. To save the lives of thousands of individuals worldwide, an accurate and quick examination of brain tumours and automatic detection of these tumours are necessary. Cancer may develop in some situations due to the expansion of aberrant brain cells, a condition recognized medically as a brain tumour. Magnetic resonance imaging, or MRI scans, is the preferred technique for detecting brain tumours. The information required to pinpoint the aberrant tissue growth that is taking place in the brain is obtained from MRI scans. A very effective strategy to identify brain tumours is using machine learning.
Aidriven image analysis in central nervous system tumorstraditional machine
The interpretation of imaging in medicine in general and in oncology specifically remains problematic due to several limitations which include the need to incorporate detailed clinical history, patient and disease-specific history, clinical exam features, previous and ongoing treatment, and account for the dependency on reproducible human interpretation of multiple factors with incomplete data linkage. To standardize reporting, minimize bias, expedite management, and improve outcomes, the use of Artificial Intelligence (AI) has gained significant prominence in imaging analysis. In oncology, AI methods have as a result been explored in most cancer types with ongoing progress in employing AI towards imaging for oncology treatment, assessing treatment response, and understanding and commu-nicating prognosis. Challenges remain with limited available data sets, variability in imaging changes over time augmented by a growing heterogeneity in analysis approaches. We review the imaging analysis workflow and examine how hand-crafted features also referred to as traditional Machine Learning (ML), Deep Learning (DL) approaches, and hybrid analyses, are being employed in AI-driven imaging analysis in central nervous system tumors. ML, DL, and hybrid approaches coexist, and their combination may produce superior results although data in this space is as yet novel, and conclusions and pitfalls have yet to be fully explored. We note the growing technical complexities that may become increasingly separated from the clinic and enforce the acute need for clinician engagement to guide progress and ensure that conclusions derived from AI-driven imaging analysis reflect that same level of scrutiny lent to other avenues of clinical research.
The brain is regarded as one of the most effective body-controlling organs. The development of technology has enabled the early and accurate detection of brain tumors, which makes a significant difference in their treatment. The adoption of AI has grown substantially in the arena of neurology. This systematic review compares recent Deep Learning (DL), Machine Learning (ML), and hybrid methods for detecting brain cancers. This article evaluates 36 recent articles on these techniques, considering datasets, methodology, tools used, merits, and limitations. The articles contain comprehensible graphs and tables. The detection of brain tumors relies heavily on ML techniques such as Support Vector Machines (SVM) and Fuzzy C-Means (FCM). Recurrent Convolutional Neural Networks (RCNN), DenseNet, Convolutional Neural Networks (CNN), ResNet, and Deep Neural Networks (DNN) are DL techniques used to detect brain tumors more efficiently. DL and ML techniques are merged to develop hybrid techniques. In addition, a summary of the various image processing steps is provided. The systematic review identifies outstanding issues and future goals for DL and MLbased techniques for detecting brain tumors. Through a systematic review, the most effective method for detecting brain tumors can be identified and utilized for improvement.
Artificial Intelligence and Precision Medicine: A New Frontier for the Treatment of Brain Tumors
Life
Brain tumors are a widespread and serious neurological phenomenon that can be life- threatening. The computing field has allowed for the development of artificial intelligence (AI), which can mimic the neural network of the human brain. One use of this technology has been to help researchers capture hidden, high-dimensional images of brain tumors. These images can provide new insights into the nature of brain tumors and help to improve treatment options. AI and precision medicine (PM) are converging to revolutionize healthcare. AI has the potential to improve cancer imaging interpretation in several ways, including more accurate tumor genotyping, more precise delineation of tumor volume, and better prediction of clinical outcomes. AI-assisted brain surgery can be an effective and safe option for treating brain tumors. This review discusses various AI and PM techniques that can be used in brain tumor treatment. These new techniques for the treatment of brain tumors, i.e., genomic pro...
International Journal on Recent and Innovation Trends in Computing and Communication
The hottest issue in medicine is image analysis. It has drawn a lot of researchers since it can effectively assess the severity of the condition and forecast the outcome. The noise trimming outcomes, on the other hand, have reduced with more complex trained images, which has tended to result in a lower prediction exactness score. So, a novel Machine Learning prediction framework has been built in this present study. This work also tries to predict brain tumours and evaluate their severity using MRI brain scans. Using the boosting function, the best results for error pruning are produced. The Proposed Solution function was then used to successfully complete the feature analysis and tumour prediction operations. The intended framework is evaluated in the Python environment, and a comparative analysis is performed to examine the prediction improvement score. It was discovered that an original MLPM model had the best tumour prediction precision.
Brain Tumor Analysis Using Machine Learning
2020
In the medical field, medical image fusion plays an important role in diagnosis of brain tumours that may be classified as malignant or benign. To cut back uncertainty and minimize redundancy whereas extracting all the helpful info from the supply pictures, it's the method of grouping multiple pictures of the identical scene into one united image. SVM is employed to fuse 2 totally different visions and brain tomography pictures. The image united is going to be a lot more informative than the pictures from the supply. The image united allows us to extract the characteristics of texture and wavelet. Based on supported trained and tested options, the SVM Classifier classifies brain tumours. The planned technique achieved sensitivity of 80.48 percent, accuracy of 99.69 percent and specificity of 99.9 percent. Experimental results obtained from the fusion method show that the utilization of the planned approach to image fusion shows higher performance compared to standard fusion meth...