Automatic Detection of Brain Tumor Using Deep Learning Algorithms

Sangeetha, R.; Mohanarathinam, A.; Aravindh, G.; Jayachitra, S.; Bhuvaneswari, M.

doi:10.1109/iceca49313.2020.9297536

Cited by 22 publications

(9 citation statements)

References 10 publications

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“…When the training and testing phases are complete, standardized assessment criteria must be used to assess the model’s efficacy in object detection. The researchers [ 11 , 34 , 50 ] used a variety of measures for evaluation, such as precision (PR), recall (RE), sensitivity (SE), specificity (SP), accuracy (AC), F1-score, and confusion matrix (CM). These measures are calculated by applying the model to a dataset of 613 MRIs and counting the number of True Positives (TP), True Negatives (TN), False Positives (FP), and False Negatives (FN).…”

Section: Methodsmentioning

confidence: 99%

“…Due to its measuring of the harmonic mean between FNs and FPs, the F1-score is more applicable in unbalanced datasets. Equations (7)–(11) [ 34 , 50 ] are used to determine each model’s accuracy, precision, sensitivity, specificity, and F1-score in order to evaluate their overall performance:

…”

Section: Methodsmentioning

confidence: 99%

“…The method improved upon multimodal 3D-CNNs in order to capture the unique features of brain tumor lesions using many imaging modalities. Additionally, in [ 50 ], the researchers investigated the use of VGGNets, GoogleNets, and ResNets, among other CNN designs, in the context of brain tumor classification. According to the findings, ResNet-50 outperformed GoogleNet and VGGNets, with an accuracy rate of 96.50%, compared to 93.45% and 89.33%, respectively.…”

Section: Related Workmentioning

confidence: 99%

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Brain Tumor Detection Based on Deep Learning Approaches and Magnetic Resonance Imaging

Abdusalomov,

Mukhiddinov,

Whangbo

2023

Cancers

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The rapid development of abnormal brain cells that characterizes a brain tumor is a major health risk for adults since it can cause severe impairment of organ function and even death. These tumors come in a wide variety of sizes, textures, and locations. When trying to locate cancerous tumors, magnetic resonance imaging (MRI) is a crucial tool. However, detecting brain tumors manually is a difficult and time-consuming activity that might lead to inaccuracies. In order to solve this, we provide a refined You Only Look Once version 7 (YOLOv7) model for the accurate detection of meningioma, glioma, and pituitary gland tumors within an improved detection of brain tumors system. The visual representation of the MRI scans is enhanced by the use of image enhancement methods that apply different filters to the original pictures. To further improve the training of our proposed model, we apply data augmentation techniques to the openly accessible brain tumor dataset. The curated data include a wide variety of cases, such as 2548 images of gliomas, 2658 images of pituitary, 2582 images of meningioma, and 2500 images of non-tumors. We included the Convolutional Block Attention Module (CBAM) attention mechanism into YOLOv7 to further enhance its feature extraction capabilities, allowing for better emphasis on salient regions linked with brain malignancies. To further improve the model’s sensitivity, we have added a Spatial Pyramid Pooling Fast+ (SPPF+) layer to the network’s core infrastructure. YOLOv7 now includes decoupled heads, which allow it to efficiently glean useful insights from a wide variety of data. In addition, a Bi-directional Feature Pyramid Network (BiFPN) is used to speed up multi-scale feature fusion and to better collect features associated with tumors. The outcomes verify the efficiency of our suggested method, which achieves a higher overall accuracy in tumor detection than previous state-of-the-art models. As a result, this framework has a lot of potential as a helpful decision-making tool for experts in the field of diagnosing brain tumors.

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Section: Methodsmentioning

confidence: 99%

…”

Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Brain Tumor Detection Based on Deep Learning Approaches and Magnetic Resonance Imaging

Abdusalomov,

Mukhiddinov,

Whangbo

2023

Cancers

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“…While VGGNet achieved a validation accuracy of 97% on a specific dataset, the personalized CNN model exhibited a lower validation accuracy of 86%. The researchers in [37] presented numerous CNN-based categorization methods, each with many repetitions, including VGGNets, GoogleNets, and ResNets. GoogleNet and VGGNets have lower accuracy ratings (93.45% and 89.33%, respectively) than ResNet-50 (96.50%).…”

Section: Related Workmentioning

confidence: 99%

A Novel Hybrid System of Detecting Brain Tumors in MRI

Agarwal,

Pande,

Mohanty

et al. 2023

IEEE Access

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The growth of irregular brain cells leads to a disease called brain tumor (BT). It is difficult to predict a patient's chance of survival due to the low rate and wide range of tumor shapes. Even though it is possible to manually detect cancer, doing so is difficult and time-consuming and runs the risk of producing false-positive results. This can be done via MRI, which is necessary for locating cancer. It is very difficult to reliably identify different illnesses from MRI images for successful therapy via a computer-aided diagnostic system. In the experiment, three openly accessible benchmark datasets were utilized. To perform feature extraction in our proposed method, a CNN model was employed, followed by the application of five machine learning classifiers: Decision tree, Naive Bayes, Adaptive Boosting, K-nearest neighbor, and support vector machine. The outcomes show that the proposed CNN architecture with the KNN classifier performs better than previous CNN models by outperforming other cutting-edge DL models under various classification metrics. Finally, the achieved F1-Score, precision, recall, and accuracy values for the classification and detection of the proposed model were 99.58%, 99.59%, 99.58%, and 99.58%, respectively. For the comparison study, additional Transfer Learning models are utilized. Experimental findings support the strength of the proposed architecture, which has rapidly accelerated and improved the classifications of BTs. The designed method outperforms the body of existing knowledge, demonstrating that it is a quick and precise method for classifying BTs.

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“…Sangeetha et al [ 6 ] provided a classifier with several iterations based on many CNN architectures. For the next 60 iterations, VGGNet has an accuracy rate of 89.33%, Google Net has a rate of 93.45%, and ResNet 50 has a rate of 96.50%.…”

Section: State-of-the-art Modelsmentioning

confidence: 99%

Application of Genetic Algorithm and U-Net in Brain Tumor Segmentation and Classification: A Deep Learning Approach

Arif

Jims

Ajesh

et al. 2022

Computational Intelligence and Neuroscience

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The development of unusual cells in the cerebrum causes brain cancer. It is classified primarily into two classes: a noncarcinogenic (benign) type of growth and cancerous (malignant) growth. Early detection of this disease is a quintessential task for all medical practice professionals. For traditional approaches of tumor detections, certain limitations exist. They include less effectiveness, inability to detect due to low-quality processing of images, less dataset for training and testing, less predictive nature to models, and skipping of quintessential stages. All these lead to inaccurate results of tumor detections. To overcome this issue, this paper brings an effective deep learning technique for brain tumor detection with the following stages: (a) data collection from REMBRANDT dataset containing multisequence MRI of 130 patients; (b) preprocessing using conversion to greyscale, skull stripping, and histogram equalization; (c) segmentation uses genetic algorithm; (d) feature extraction using discrete wavelet transform (DWT); (e) particle swarm optimization technique for feature selection; (f) classification using U-Net. Experiment evaluation states that the proposed model (GA-UNET) outperforms (accuracy: 0.97, sensitivity: 0.98, specificity: 0.98) compared to other advanced models.

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Automatic Detection of Brain Tumor Using Deep Learning Algorithms

Cited by 22 publications

References 10 publications

Brain Tumor Detection Based on Deep Learning Approaches and Magnetic Resonance Imaging

Brain Tumor Detection Based on Deep Learning Approaches and Magnetic Resonance Imaging

A Novel Hybrid System of Detecting Brain Tumors in MRI

Application of Genetic Algorithm and U-Net in Brain Tumor Segmentation and Classification: A Deep Learning Approach

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