Rough K-means and support vector machine based brain tumor detection

Halder, Amiya; Dobe, Oyendrila

doi:10.1109/icacci.2017.8125826

Cited by 11 publications

(8 citation statements)

References 6 publications

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“…But now, IoMT can improve EMR/EHR with better access, as well as better data flows. Notice how real-time heart rate results, and other results may flow into the electronic health record [4,5]. Consider how the IoMT promotes equal 24/7 access to patient and physician.…”

Section: Introductionmentioning

confidence: 99%

A Smart Solution for Cancer Patient Monitoring Based on Internet of Medical Things Using Machine Learning Approach

Sriram

Reddy

Babu

et al. 2022

Evidence-Based Complementary and Alternative Medicine

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The Internet of Medical Things (IoMT) is a huge, exciting new phenomenon that is changing the world of technology and innovating various industries, including healthcare. It has specific applications and changes in the medical world based on what can be done for clinical workflow models. The first and most fundamental thing that IoMT does in healthcare is to bring a flood of new data into medical processes. In this study, an efficient Internet of Medical Things based cancer detection model was proposed. In fact, for many, new fitness monitors and watches are one of the best examples on the Internet; these mobile, portable, wearable devices can record real-time heart rate, blood pressure, and eye movement of cancer patients. These details are sent to doctors or anywhere else. The proposed method leads to a kind of big data renaissance in the health service. The proposed model gets more accuracy while comparing with the existing models. This will help the doctors to analyze the patients’ health report and provides better treatment.

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Section: Introductionmentioning

confidence: 99%

A Smart Solution for Cancer Patient Monitoring Based on Internet of Medical Things Using Machine Learning Approach

Sriram

Reddy

Babu

et al. 2022

Evidence-Based Complementary and Alternative Medicine

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show abstract

“…Table 13 shows the accuracy table for various techniques. [6] 87.23% Rao et al [7] 89% Dandil et al [22] 90.79% SVM classifier [6] 91.49% Devasena and Hemalatha [23] 98.8% El-Dahshan et al [24] 99% Halder and Dobe [9] 99.05% Arakeri and Reddy [25] 99.09% Proposed Method 99.3%…”

Section: 𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦 𝑆𝑐𝑜𝑟𝑒 =mentioning

confidence: 99%

“…A modified mean-shift-based fuzzy c-mean segmentation technique for the detection of brain tumors is proposed in [8] which is fast to provide segmentation results. In [9], the authors proposed an SVM and rough K-means-based brain tumor detection algorithm, which classify MRI images and claimed almost 99.05% of accuracy. Authors in [10] convert MRI images into OtsoBinarization followed by K-means clustering segmentation in brain tumor detection and classification.…”

mentioning

confidence: 99%

An internet of things-based automatic brain tumor detection system

Rahman

Reza

Shabuj

2022

IJEECS

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Due to the advances in information and communication technologies, the usage of the internet of things (IoT) has reached an evolutionary process in the development of the modern health care environment. In the recent human health care analysis system, the amount of brain tumor patients has increased severely and placed in the 10th position of the leading cause of death. Previous state-of-the-art techniques based on magnetic resonance imaging (MRI) faces challenges in brain tumor detection as it requires accurate image segmentation. A wide variety of algorithms were developed earlier to classify MRI images which are computationally very complex and expensive. In this paper, a cost-effective stochastic method for the automatic detection of brain tumors using the IoT is proposed. The proposed system uses the physical activities of the brain to detect brain tumors. To track the daily brain activities, a portable wrist band named Mi Band 2, temperature, and blood pressure monitoring sensors embedded with Arduino-Uno are used and the system achieved an accuracy of 99.3%. Experimental results show the effectiveness of the designed method in detecting brain tumors automatically and produce better accuracy in comparison to previous approaches.

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“…In our study, it is shown that some of the methods which segment the tumor accurately compared to the general image processing techniques. Some of the methods are Support Vector Machine (SVM) [9-12] Random Forest (RF) [13][14][15] and Naïve Bayesian (NB) [16], K nearest neighbor (KNN) [17][18][19], Artificial Neural Networks (ANN) [20][21][22][23] and hybrid methods [24][25][26]. All the methods above require special features separating a tumor pixel from a Non-tumor pixel.…”

Section: Review Of Literaturementioning

confidence: 99%

A dual autoencoder and singular value decomposition based feature optimization for the segmentation of brain tumor from MRI images

Aswani

Menaka

2021

BMC Med Imaging

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Background The brain tumor is the growth of abnormal cells inside the brain. These cells can be grown into malignant or benign tumors. Segmentation of tumor from MRI images using image processing techniques started decades back. Image processing based brain tumor segmentation can be divided in to three categories conventional image processing methods, Machine Learning methods and Deep Learning methods. Conventional methods lacks the accuracy in segmentation due to complex spatial variation of tumor. Machine Learning methods stand as a good alternative to conventional methods. Methods like SVM, KNN, Fuzzy and a combination of either of these provide good accuracy with reasonable processing speed. The difficulty in processing the various feature extraction methods and maintain accuracy as per the medical standards still exist as a limitation for machine learning methods. In Deep Learning features are extracted automatically in various stages of the network and maintain accuracy as per the medical standards. Huge database requirement and high computational time is still poses a problem for deep learning. To overcome the limitations specified above we propose an unsupervised dual autoencoder with latent space optimization here. The model require only normal MRI images for its training thus reducing the huge tumor database requirement. With a set of normal class data, an autoencoder can reproduce the feature vector into an output layer. This trained autoencoder works well with normal data while it fails to reproduce an anomaly to the output layer. But a classical autoencoder suffer due to poor latent space optimization. The Latent space loss of classical autoencoder is reduced using an auxiliary encoder along with the feature optimization based on singular value decomposition (SVD). The patches used for training are not traditional square patches but we took both horizontal and vertical patches to keep both local and global appearance features on the training set. An Autoencoder is applied separately for learning both horizontal and vertical patches. While training a logistic sigmoid transfer function is used for both encoder and decoder parts. SGD optimizer is used for optimization with an initial learning rate of .001 and the maximum epochs used are 4000. The network is trained in MATLAB 2018a with a processor capacity of 3.7 GHz with NVIDIA GPU and 16 GB of RAM. Results The results are obtained using a patch size of 16 × 64, 64 × 16 for horizontal and vertical patches respectively. In Glioma images tumor is not grown from a point rather it spreads randomly. Region filling and connectivity operations are performed to get the final tumor segmentation. Overall the method segments Meningioma better than Gliomas. Three evaluation metrics are considered to measure the performance of the proposed system such as Dice Similarity Coefficient, Positive Predictive Value, and Sensitivity. Conclusion An unsupervised method for the segmentation of brain tumor from MRI images is proposed here. The proposed dual autoencoder with SVD based feature optimization reduce the latent space loss in the classical autoencoder. The proposed method have advantages in computational efficiency, no need of huge database requirement and better accuracy than machine learning methods. The method is compared Machine Learning methods Like SVM, KNN and supervised deep learning methods like CNN and commentable results are obtained.

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Rough K-means and support vector machine based brain tumor detection

Cited by 11 publications

References 6 publications

A Smart Solution for Cancer Patient Monitoring Based on Internet of Medical Things Using Machine Learning Approach

A Smart Solution for Cancer Patient Monitoring Based on Internet of Medical Things Using Machine Learning Approach

An internet of things-based automatic brain tumor detection system

A dual autoencoder and singular value decomposition based feature optimization for the segmentation of brain tumor from MRI images

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