An automatic and intelligent brain tumor detection using Lee sigma filtered histogram segmentation model

Kurian, Simy Mary; Juliet, Sujitha

doi:10.1007/s00500-022-07457-2

Cited by 14 publications

(7 citation statements)

References 32 publications

(35 reference statements)

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“…The study by Kurian and Juliet [30] suggests a unique method for quickly and accurately identifying brain cancers termed Lee sigma filtered histogram segmentation (LSFHS). Preprocessing, segmentation, feature extraction, and classification are the foundations of the LSFHS approach.…”

Section: Related Workmentioning

confidence: 99%

Image-based MRI detection of brain tumours using convolutional neural networks

Preethi,

NG,

Sankar

et al. 2023

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Rapid and uncontrolled cellular proliferation is what distinguishes a brain tumor. Unfortunately, brain tumors cannot be prevented other than via surgery. As predicted, deep learning may help diagnose and cure brain cancers. The segmentation approach has been widely studied for brain tumor removal. This uses the segmentation approach, one of the most advanced methods for object detection and categorization. To efficiently assess the tumor's size, an accurate and automated brain tumor segmentation approach is needed. We present a fully automated brain tumor separation method for imaging investigations. The approach has been developed with convolutional neural networks. The Multimodal Brain Tumor Image Segmentation (BRATS) datasets tested our strategy. This result suggests that DL should investigate heterogeneous MRI image segmentation to improve brain tumor segmentation accuracy and efficacy. This study may lead to more accurate medical diagnoses and treatments. Researchers in this study also found a way to automatically find cancerous tumours by using the Grey Level Co-Occurrence Matrix (GLCM) and discrete wavelet transform (DWT) to find features in MRI images. They then used a CNN to guess the final prognosis. The preceding section details this technique. When compared to the other algorithm, the CNN method uses computer resources better.

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Section: Related Workmentioning

confidence: 99%

Image-based MRI detection of brain tumours using convolutional neural networks

Preethi,

NG,

Sankar

et al. 2023

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“…To separate an image into two pieces, imaging segmentation is utilized in the field of medical images. After the process of segmentation, the brain regions are recognized by the classification approach [13,14]. Various approaches and algorithms are established for tumor segmentation because of the intricate segmentation procedure in the MRI image [15].…”

Section: Introductionmentioning

confidence: 99%

Brain Tumor Segmentation and Classification Using Binomial Thresholding-Based Bidirectional-Long-Short Term Memory

2024

IJIES

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A brain tumor arises when abnormal cells develop in the brain, leading to an elevated risk of illness and mortality due to the accelerated growth of these tumor cells. Magnetic resonance imaging (MRI) is utilized for detecting and diagnosing brain tumors by providing images of the brain's internal structure. However, brain tumors lead to the death of numerous lives due to inaccurate segmentation and classification of brain tumors. In this research, the binomial thresholding-based bidirectional-long-short term memory (BT-Bi-LSTM) is proposed for accurate segmentation and classification. Initially, the image is acquired from BRATS 2019 and BRATS 2020 datasets and then normalization and contrast limited adaptive histogram equalization (CLAHE) approaches are established in preprocessing. The BT technique is employed for segmenting tumor portions from the pre-processed images. The graylevel Co-occurrence matrix (GLCM) and local ternary pattern (LTP) are employed to extract the features. Finally, the Bi-LSTM is used to classify the types of brain tumors. The BT-Bi-LSTM achieves better accuracy, precision, recall, and f1-score of 99.76%, 99.52%, 99.31%, and 98.69% for BRATS 2019 dataset compared to the existing approaches like DNN-based mathematical approach, tumor localization enhancement approach and U-net architecture, and Hybrid Convolution Neural Network (HCNN). When compared to tumor localization enhancement approach and U-net architecture, CNN, hybrid Deep CNN with k-means clustering, and 2D U-net, the BT-Bi-LSTM achieves better accuracy, precision, and recall of 99.89%, 99.76%, and 99.62% for BRATS 2020 dataset respectively.

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“…For the purpose of classifying brain tumors in public datasets, a hybrid CNN is designed in. [20][21][22] They demonstrated that by tuning the CNN's hyperparameters with a specialized approach, accuracy can be improved.…”

Section: Introductionmentioning

confidence: 99%

Multi‐classificationof brain tumor by using deep convolutional neural network model inmagnetic resonance imagingimages

Singh,

Merlin,

Prabu

et al. 2023

Int J Imaging Syst Tech

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Brain tumors are still diagnosed and classified based on the results of histopathological examinations of biopsy samples. The existing method requires extra effort from the user, takes too long, and can lead to blunders. These limitations underline the need of employing a fully automated deep learning system for the multi‐classification of brain tumors. In order to facilitate early detection, this study employs a convolutional neural network (CNN) to multi‐classify brain tumors. In this research, we present three distinct CNN models for use in three separate categorization tasks. The first CNN model can correctly categorize brain tumors 99.74% of the time. The second CNN model is 96.27% accurate in differentiating between normal, glioma, meningioma, pituitary, and metastatic brain tumors. The third CNN model successfully distinguishes between Grades II, III, and IV brain tumors 99.18% of the time. The Hybrid Particle Swarm Grey Wolf Optimization (HPSGWO) technique is used to quickly and accurately determine optimal values for all of CNN models most important hyperparameters. An HPSGWO algorithm is used to fine‐tune all the necessary hyperparameters for optimal classification performance. The results are compared with standard existing CNN models across a range of performance measures. The proposed models are trained using publicly available large clinical datasets. To verify their initial multi‐classification of brain tumors, clinicians and radiologists might use the proposed CNN models.

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An automatic and intelligent brain tumor detection using Lee sigma filtered histogram segmentation model

Cited by 14 publications

References 32 publications

Image-based MRI detection of brain tumours using convolutional neural networks

Image-based MRI detection of brain tumours using convolutional neural networks

Brain Tumor Segmentation and Classification Using Binomial Thresholding-Based Bidirectional-Long-Short Term Memory

Multi‐classificationof brain tumor by using deep convolutional neural network model inmagnetic resonance imagingimages

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