An automated brain tumor detection and classification from MRI images using machine learning techniques with IoT

Budati, Anil Kumar; Katta, Rajesh Babu

doi:10.1007/s10668-021-01861-8

Cited by 28 publications

(8 citation statements)

References 28 publications

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“…Budati aims at the problem that MRI is used as a result of low ionization and radiation in various medical imaging technologies, while manual detection takes a lot of trouble. Therefore, a machine learning technology is introduced to achieve the classification, recognition and detection effect of tumor or non-tumor areas in view of brain MRI dataset [15]. Automatic brain tumor detection, which graphics is segmented and classification is executed on brain MRI graphics applying genetic sequence that is meta-heuristic optimization scheme and support vector machine is proposed in [16].…”

Section: Related Workmentioning

confidence: 99%

Computational Analysis based on Advanced Correlation Automatic Detection Technology in BDD-FFS System

Zheng¹,

Tahir²,

Li³

et al. 2022

IJACSA

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Big Data-Driven Fabric Future Systems (BDD-FFS) is currently attracting widespread attention in the healthcare research community. Medical devices rely primarily on the intelligent Internet to gather important health-related information. According to this, we provide patients with deeply supportive data to help them through their recovery. However, due to the large number of medical devices, the address of the device can be modified by intruders, which can be life-threatening for serious patients (such as tumor patients). A large number of abnormal cells in the brain can lead to brain tumors, which harm brain tissue and can be life-threatening. Recognition of brain tumors at the beginning of the process is significant for their detection, prediction and therapy. The traditional approach for detecting is for a human to perform a biopsy and examine CT scans or magnetic resonance imaging (MRI), which is cumbersome,unrealistic for great amounts of resource, and requests the radiologist to make inferential computations. A variety of automation schemes have been designed to address these challenges. However, there is an urgent need to develop a technology that will detect brain tumors with remarkable accuracy in a much shorter time. In addition, the selection of feature sets for prediction is crucial to realize significant accuracy. This work utilizes an associative action learner with an advanced feature group, Partial Tree (PART-T), to detect brain tumor recognition grades. The model presented was compared with existing methods through 10-fold cross-validation. Experimental results show that partial trees with advanced feature sets are superior to existing techniques in terms of performance indicators used for evaluation, such as accuracy, recall rate and F-measure.

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Section: Related Workmentioning

confidence: 99%

Computational Analysis based on Advanced Correlation Automatic Detection Technology in BDD-FFS System

Zheng¹,

Tahir²,

Li³

et al. 2022

IJACSA

View full text Add to dashboard Cite

show abstract

“…In addition to the MRI scanning, all the imaging techniques produce images in greyscale, except for the color Doppler technique, which produces color images. However, other techniques for tissue segmentation regions such as post-processing do not produce the desired results [ 12 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

Combining CNN Features with Voting Classifiers for Optimizing Performance of Brain Tumor Classification

Alturki

Umer

Ishaq

et al. 2023

Cancers

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Brain tumors and other nervous system cancers are among the top ten leading fatal diseases. The effective treatment of brain tumors depends on their early detection. This research work makes use of 13 features with a voting classifier that combines logistic regression with stochastic gradient descent using features extracted by deep convolutional layers for the efficient classification of tumorous victims from the normal. From the first and second-order brain tumor features, deep convolutional features are extracted for model training. Using deep convolutional features helps to increase the precision of tumor and non-tumor patient classification. The proposed voting classifier along with convoluted features produces results that show the highest accuracy of 99.9%. Compared to cutting-edge methods, the proposed approach has demonstrated improved accuracy.

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“…According to previous researchers, machine and deep learning approaches using MRI data could lead to easier detection and classification of brain tumors [8][9][10][11][12][13][14][15][16][17][18][19]. However, these approaches suffer from the following limitations:…”

Section: Introductionmentioning

confidence: 99%

Efficient Brain Tumor Detection with Lightweight End-to-End Deep Learning Model

Hammad

ElAffendi

Ateya

et al. 2023

Cancers

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In the field of medical imaging, deep learning has made considerable strides, particularly in the diagnosis of brain tumors. The Internet of Medical Things (IoMT) has made it possible to combine these deep learning models into advanced medical devices for more accurate and efficient diagnosis. Convolutional neural networks (CNNs) are a popular deep learning technique for brain tumor detection because they can be trained on vast medical imaging datasets to recognize cancers in new images. Despite its benefits, which include greater accuracy and efficiency, deep learning has disadvantages, such as high computing costs and the possibility of skewed findings due to inadequate training data. Further study is needed to fully understand the potential and limitations of deep learning in brain tumor detection in the IoMT and to overcome the obstacles associated with real-world implementation. In this study, we propose a new CNN-based deep learning model for brain tumor detection. The suggested model is an end-to-end model, which reduces the system’s complexity in comparison to earlier deep learning models. In addition, our model is lightweight, as it is built from a small number of layers compared to other previous models, which makes the model suitable for real-time applications. The optimistic findings of a rapid increase in accuracy (99.48% for binary class and 96.86% for multi-class) demonstrate that the new framework model has excelled in the competition. This study demonstrates that the suggested deep model outperforms other CNNs for detecting brain tumors. Additionally, the study provides a framework for secure data transfer of medical lab results with security recommendations to ensure security in the IoMT.

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An automated brain tumor detection and classification from MRI images using machine learning techniques with IoT

Cited by 28 publications

References 28 publications

Computational Analysis based on Advanced Correlation Automatic Detection Technology in BDD-FFS System

Computational Analysis based on Advanced Correlation Automatic Detection Technology in BDD-FFS System

Combining CNN Features with Voting Classifiers for Optimizing Performance of Brain Tumor Classification

Efficient Brain Tumor Detection with Lightweight End-to-End Deep Learning Model

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