A study of classification and feature extraction techniques for brain tumor detection

Jalali, Vatika; Kaur, Dapinder

doi:10.1007/s13735-020-00199-7

Cited by 28 publications

(4 citation statements)

References 70 publications

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“…In general, BTs are categorized as malignant (cancerous) or benign (noncancerous). (1). Benign or normal tumors do not cause any damage to the brain cells and can be easily treated.…”

Section: Introductionmentioning

confidence: 99%

A deep learning model for the localization and extraction of brain tumors from MR images using YOLOv7 and grab cut algorithm

Krishnapriya,

Karuna

2024

Front. Oncol.

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IntroductionBrain tumors are a common disease that affects millions of people worldwide. Considering the severity of brain tumors (BT), it is important to diagnose the disease in its early stages. With advancements in the diagnostic process, Magnetic Resonance Imaging (MRI) has been extensively used in disease detection. However, the accurate identification of BT is a complex task, and conventional techniques are not sufficiently robust to localize and extract tumors in MRI images. Therefore, in this study, we used a deep learning model combined with a segmentation algorithm to localize and extract tumors from MR images.MethodThis paper presents a Deep Learning (DL)-based You Look Only Once (YOLOv7) model in combination with the Grab Cut algorithm to extract the foreground of the tumor image to enhance the detection process. YOLOv7 is used to localize the tumor region, and the Grab Cut algorithm is used to extract the tumor from the localized region.ResultsThe performance of the YOLOv7 model with and without the Grab Cut algorithm is evaluated. The results show that the proposed approach outperforms other techniques, such as hybrid CNN-SVM, YOLOv5, and YOLOv6, in terms of accuracy, precision, recall, specificity, and F1 score.DiscussionOur results show that the proposed technique achieves a high dice score between tumor-extracted images and ground truth images. The findings show that the performance of the YOLOv7 model is improved by the inclusion of the Grab Cut algorithm compared to the performance of the model without the algorithm.

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“…In general, BTs are categorized as malignant (cancerous) or benign (noncancerous). (1). Benign or normal tumors do not cause any damage to the brain cells and can be easily treated.…”

Section: Introductionmentioning

confidence: 99%

A deep learning model for the localization and extraction of brain tumors from MR images using YOLOv7 and grab cut algorithm

Krishnapriya,

Karuna

2024

Front. Oncol.

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“…Also, It is one of the best and most accurate tests for tumor identification due to its ability to produce high-quality images of human anatomy in multiple dimensions [17]. MRI uses radio waves and magnetic resonance to retrieve detailed brain information and can help doctors in tumor diagnosis [18]. The brain can be imaged in three planes, coronal, axial and sagittal - see Figure 1 and multi-angle images can be obtained [19].…”

Section: Introductionmentioning

confidence: 99%

PRCnet: An Efficient Model for Automatic Detection of Brain Tumor in MRI Images

Farhan,

Khalid,

Manzoor

2023

Preprint

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Brain tumors are the most prevalent and life-threatening cancer; an early and accurate diagnosis of brain tumors increases the chances of patient survival and treatment planning. However, manual tumor detection is a complex, cumbersome and time-consuming task and is prone to errors, which relies on the radiologist’s experience. As a result, the development of accurate and automatic system for tumor detection is critical. In this paper, we proposed a new model called Parallel Residual Convolutional Network (PRCnet) model to classify brain tumors from Magnetic Resonance Imaging. The PCRnet model uses several techniques (such as filters of different sizes with parallel layers, connections between layers, batch normalization layer, and ReLU) and dropout layer to overcome the over-fitting problem, for achieving accurate and automatic classification of brain tumors. The PRCnet model is trained and tested on two different datasets and obtained an accuracy of 94.77% and 97.1% for dataset A and dataset B, respectively which is way better as compared to the state-of-the-art models.

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“…Ultrasonography has several advantages compared to other modalities, as it is reliable, cost-effective, and has a bedside availability. [14][15][16][17][18][19] It does not emit harmful radiation, unlike CT scan imaging, for the monitoring of ICP. The measured optic nerve sheath diameter (ONSD) using ocular ultrasound is a biomarker for predicting intracranial pressure (ICP).…”

Section: Introductionmentioning

confidence: 99%

A Novel Method for Prediction of Raised Intracranial Pressure Through Automated ONSD and ETD Ratio Measurement From Ocular Ultrasound

Singh,

Gupta,

Gupta

et al. 2023

Ultrason Imaging

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The paper presents a novel framework for the prediction of the raised Intracranial Pressure (ICP) from ocular ultrasound images of traumatic patients through automated measurement of Optic Nerve Sheath Diameter (ONSD) and Eyeball Transverse Diameter (ETD). The measurement of ONSD using an ocular ultrasound scan is non-invasive and correlates with the raised ICP. However, the existing studies suggested that the ONSD value alone is insufficient to indicate the ICP condition. Since the ONSD and ETD values may vary among patients belonging to different ethnicity/origins, there is a need for developing an independent global biomarker for predicting raised ICP condition. The proposed work develops an automated framework for the prediction of raised ICP by developing algorithms for the automated measurement of ONSD and ETD values. It is established that the ONSD and ETD ratio (OER) is a potential biomarker for ICP prediction independent of ethnicity and origin. The OER threshold value is determined by performing statistical analysis on the data of 57 trauma patients obtained from the AIIMS, New Delhi. The automated OER is computed and compared with the conventionally measured ICP by determining suitable correlation coefficients. It is found that there is a significant correlation of OER with ICP ( r = .81, p ≤ .01), whereas the correlation of ONSD alone with ICP is relatively less ( r = .69, p = .004). These correlation values indicate that OER is a better parameter for the prediction of ICP. Further, the threshold value of OER is found to be 0.21 for predicting raised ICP conditions in this study. Scatter plot and Heat map analysis of OER and corresponding ICP reveal that patients with OER ≥ 0.21, have ICP in the range of 17 to 35 mm Hg. In the data available for this research work, OER ranges from 0.17 to 0.35.

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A study of classification and feature extraction techniques for brain tumor detection

Cited by 28 publications

References 70 publications

A deep learning model for the localization and extraction of brain tumors from MR images using YOLOv7 and grab cut algorithm

A deep learning model for the localization and extraction of brain tumors from MR images using YOLOv7 and grab cut algorithm

PRCnet: An Efficient Model for Automatic Detection of Brain Tumor in MRI Images

A Novel Method for Prediction of Raised Intracranial Pressure Through Automated ONSD and ETD Ratio Measurement From Ocular Ultrasound

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