Mixture Model Segmentation System for Parasagittal Meningioma brain Tumor Classification based on Hybrid Feature Vector

Prabhu, L. Arokia Jesu; Jayachandran, A.

doi:10.1007/s10916-018-1094-3

Cited by 24 publications

(13 citation statements)

References 18 publications

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“…Figure 1 shows Sacroiliac joint (SJI) examinations with magnetic resonance imaging (SIJ) in people with Ankylosing spondylitis (AS). [14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31] The Assessment of Spondyloarthritis International Society (ASAS) states that an essential need for AS inclusion is sacroiliitis on imaging (MRI or radiography). Sacroiliitis has a specific plain radiography grading system that ranges from normal (IV) to the most severe (IV) and the grade is listed accordingly.…”

Section: Inflammatory Lesionsmentioning

confidence: 99%

An Overview of Inflammatory Spondylitis for Biomedical Imaging Using Deep Neural Networks

Kokkula Shiva Prasad

2023

IJRITCC

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Ankylosing Spondylitis (AS) is an axial spine inflammatory illness and also chronic that might present with a range of clinical symptoms and indicators. The illness is most frequently characterized by increasing spinal stiffness and persistent back discomfort. The affect of the sacroiliac joints, spine, peripheral joints, entheses and digits are the main cause of the illness. AS symptoms include reduced spinal mobility, aberrant posture, hip and dactylitis, enthesitis, peripheral arthritis, and buttock pain. With their exceptional picture classification ability, the diagnosis of AS illness has been transformed by deep learning techniques in artificial intelligence (AI). Despite the excellent results, these processes are still being widely used in clinical practice at a moderate rate. Due to security and health concerns, medical imaging applications utilizing deep learning must be viewed with caution. False instances, whether good or negative, have far-reaching effects on the well-being of patients and these are to be considered. These are extracted from the fact of the state-of-the-art of deep learning (DL) algorithms lack internal workings comprehension and have complicated interconnected structure, huge millions of parameters, and also a "black box" aspect compared to conventional machine learning (ML) algorithms. XAI (Explainable AI) approaches make it easier to comprehend model predictions, which promotes system reliability, speeds up the diagnosis of the AS disease, and complies with legal requirements.

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Section: Inflammatory Lesionsmentioning

confidence: 99%

An Overview of Inflammatory Spondylitis for Biomedical Imaging Using Deep Neural Networks

Kokkula Shiva Prasad

2023

IJRITCC

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“…The brain tumor threatens human life directly and arises when the development of tissues in brain grows unnaturally. It is basically a collection, or mass, of abnormal cells in the brain [3]. Brain tumor is one of the critical reasons of psychiatric complications and depressions [4], which arises due to multiple reasons like age, gender, inheritance, working environment, physical fitness and mental distress and etc.…”

Section: Introductionmentioning

confidence: 99%

Optimization and efficiency analysis of deep learning based brain tumor detection

Saeed¹,

Halepoto²,

Khaskheli³

et al. 2023

Mehran Univ. res. j. eng. technol.

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Brain tumors are spreading very fast across the world. It is one of the aggressive diseases which eventually lead to death if not being detected timely and appropriately. The difficult task for neurologists and radiologists is detecting brain tumor at early stages. However, manually detecting brain tumor from magnetic resonance imaging images is challenging, and susceptible to errors as experienced physician is required for this. To resolve both the concerns, an automated brain tumor detection system is developed for early diagnosis of the disease. In this paper, the diagnosis via MRI images are being done along with classification in terms of its type. The proposed system can specifically classify four brain tumor condition classification like meningioma tumor, pituitary tumor, glioma tumor and no tumor. The convolutional neural network method is used for classification and diagnosis of tumors which has accuracy of about 93.60%. This study is done on a KAGGLE dataset which comprises of 3274 Brain MRI scans. This model can be applied for real time brain tumor detection.

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“…For MA segmentation, researchers applied DCNNs to produce a pixel‐level probability map and predicted MA and optic cup boundaries. Although the above innovative deep learning methods have made effective progress in MA segmentation, due to the fact that the segmentation model does not extract richer global feature information in the feature extraction process, there is a big gap in the effect of boundary segmentation between different algorithms 18,19 . Using only the final output features of the encoder may lead to ignoring the advantages of the original features, and eventually lead to the omission of a large amount of global semantic information in image prediction, thus weakening the segmentation performance of the network.…”

Section: Introductionmentioning

confidence: 99%

“…Although the above innovative deep learning methods have made effective progress in MA segmentation, due to the fact that the segmentation model does not extract richer global feature information in the feature extraction process, there is a big gap in the effect of boundary segmentation between different algorithms. 18,19 Using only the final output features of the encoder may lead to ignoring the advantages of the original features, and eventually lead to the omission of a large amount of global semantic information in image prediction, thus weakening the segmentation performance of the network. In addition, high model complexity and a large number of parameters are also the development trend of the segmentation model, which makes it difficult for neural networks to converge and train.…”

mentioning

confidence: 99%

Microaneurysm classification system in color fundus images using auto‐weight dilated convolutional network

Jayachandran,

Ratheesh Kumar

2023

Int J Imaging Syst Tech

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Microaneurysms (MAs) are the early indications of diabetic retinopathy (DR), which may result in total visual loss. MAs detection is an exciting work due to its small, darkish color and subtle nature. The automatic detection and categorization of MAs in retinal fundus images using a multi‐scale approach based on Deep Neural Networks and Neighborhood Analysis is proposed in this research article. MAs segmentation, classification, and preprocessing comprise the three steps of the proposed technique. To extract the multi‐scale MA features, the auto‐weight dilated convolutional unit (AD) is specifically used for convolutional feature maps. To fuse convolutional feature maps in encoding layers, the AD unit used a learnable set of parameters. An efficient architecture for feature extraction during the encoding step is incorporated into the AD unit. We integrated the AD unit with the benefits of the U‐Net network for deep and shallow features. Additionally, in order to improve the suggested model and produce the final derivation, we developed a novel optimization approach. After that, neighborhood analysis is performed to name the Micro‐aneurysm because the lesion is actually a collection of independent little images rather than the entire image. The classification accuracy of the proposed method for the three different data sets such as MESSIDOR, online retinal fundus image database for glaucoma analysis (ORIGA), and RIM‐ONE‐R1, is 99.28%, 98.95%, and 98.76% respectively. The results show a good performance of the proposed model against the other analyzed procedure.

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Mixture Model Segmentation System for Parasagittal Meningioma brain Tumor Classification based on Hybrid Feature Vector

Cited by 24 publications

References 18 publications

An Overview of Inflammatory Spondylitis for Biomedical Imaging Using Deep Neural Networks

An Overview of Inflammatory Spondylitis for Biomedical Imaging Using Deep Neural Networks

Optimization and efficiency analysis of deep learning based brain tumor detection

Microaneurysm classification system in color fundus images using auto‐weight dilated convolutional network

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