BrainNET: A Deep Learning Network for Brain Tumor Detection and Classification

Raj, Aditya; Anil, Abhishek; Deepa, P.; Sarma, Himangshu; Chandran, Rakhi

doi:10.1007/978-981-15-3992-3_49

Cited by 10 publications

(5 citation statements)

References 7 publications

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“…Asiri et al [ 13 ] used the VGG19 architecture and attained an accuracy of 98.0% on a dataset of 2870 images. Raj et al [ 14 ] in 2020 used a recurrent neural network technique and achieved an accuracy of 96%, specificity of 98%, and sensitivity of 97%. Poonguzhali et al [ 15 ] in 2019 analyzed 20 patient images using RCNN and SVM classifiers and achieved a sensitivity of 82% and specificity of 99%.…”

Section: Related Workmentioning

confidence: 99%

“…Raj et al [ 14 ] used a recurrent neural network and achieved an accuracy of 96%, specificity of 98%, and sensitivity of 97%. Poonguzhali et al [ 15 ] used a RCNN and SVM classifier on 20 patient images and achieved a sensitivity of 82% and specificity of 99%.…”

Section: Proposed Weighted Average Ensemble Deep Learning Model Archi...mentioning

confidence: 99%

“…Table 1 provides a summary of different research studies on medical image analysis, along with the number of images used, the technique employed, and the performance parameters achieved by each study. Raj et al [14] used a recurrent neural network and achieved an accuracy of 96%, specificity of 98%, and sensitivity of 97%. Poonguzhali et al [15] used a RCNN and SVM classifier on 20 patient images and achieved a sensitivity of 82% and specificity of 99%.…”

Section: Comparison Of Ensembled Model With State-of-artmentioning

confidence: 99%

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Weighted Average Ensemble Deep Learning Model for Stratification of Brain Tumor in MRI Images

Anand

Gupta

et al. 2023

Diagnostics

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Brain tumor diagnosis at an early stage can improve the chances of successful treatment and better patient outcomes. In the biomedical industry, non-invasive diagnostic procedures, such as magnetic resonance imaging (MRI), can be used to diagnose brain tumors. Deep learning, a type of artificial intelligence, can analyze MRI images in a matter of seconds, reducing the time it takes for diagnosis and potentially improving patient outcomes. Furthermore, an ensemble model can help increase the accuracy of classification by combining the strengths of multiple models and compensating for their individual weaknesses. Therefore, in this research, a weighted average ensemble deep learning model is proposed for the classification of brain tumors. For the weighted ensemble classification model, three different feature spaces are taken from the transfer learning VGG19 model, Convolution Neural Network (CNN) model without augmentation, and CNN model with augmentation. These three feature spaces are ensembled with the best combination of weights, i.e., weight1, weight2, and weight3 by using grid search. The dataset used for simulation is taken from The Cancer Genome Atlas (TCGA), having a lower-grade glioma collection with 3929 MRI images of 110 patients. The ensemble model helps reduce overfitting by combining multiple models that have learned different aspects of the data. The proposed ensemble model outperforms the three individual models for detecting brain tumors in terms of accuracy, precision, and F1-score. Therefore, the proposed model can act as a second opinion tool for radiologists to diagnose the tumor from MRI images of the brain.

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Section: Related Workmentioning

confidence: 99%

Section: Proposed Weighted Average Ensemble Deep Learning Model Archi...mentioning

confidence: 99%

Section: Comparison Of Ensembled Model With State-of-artmentioning

confidence: 99%

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Weighted Average Ensemble Deep Learning Model for Stratification of Brain Tumor in MRI Images

Anand

Gupta

et al. 2023

Diagnostics

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“…An algorithm named BrainNET was developed for the analysis of MRI images. This algorithm demonstrates efficient tumor detection and subsequent classification through segmentation [23]. The integration of technology in healthcare has brought about a transformative shift towards virtual monitoring with remarkable precision, and AI has played a crucial role in enabling innovative imaging solutions.…”

Section: K Narayanan S Latifimentioning

confidence: 99%

A Survey of Techniques for Brain Anomaly Detection and Segmentation Using Machine Learning

Narayanan¹,

Latifi²

2023

IJCNS

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In this research report, various Machine Learning (ML) models are discussed for the purpose of detecting brain anomalies like tumors. In the first step, we review previous work that uses Deep Learning (DL) to classify and detect brain tumors. Next, we present a detailed analysis of the ML methods in tabular form to address the brain tumor morphology, accessible datasets, segmentation, extraction, and classification using DL, and ML models. Finally, we summarize all relevant material for tumor detection, including the merits, limitations and future directions. In this study, it is found that employing DL-based and hybrid-based metaheuristic approaches proves to be more effective in accurately segmenting brain tumors, compared to the conventional methods. However, the brain tumor segmentation using ML models suffers from drawbacks due to limited labelled data, variability in tumor appearance, computational memory requirements, transparency in models, and difficulty in integration into clinical workflows. By pursuing techniques such as Data Augmentation, Pre-training, Active-learning, Multimodal fusion, Hardware acceleration, and Clinical integration, researchers and developers can overcome the bottlenecks and enhance the accuracy, efficiency, and clinical utility of ML-based brain tumor segmentation models.

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“…In addition to classification our algorithm also focuses on segmenting the Region of Interest (ROI) within tumor images during a phase. It's worth noting that our proposed algorithm achieves a 96% accuracy rate, in classification [67]. Introduce an innovative algorithm, aptly named BRAINnet, designed specifically for MRI analysis.…”

mentioning

confidence: 99%

Brain Tumor Classification and Detection Based DL Models: A Systematic Review

Neamah,

Mohamed,

Adnan

et al. 2024

IEEE Access

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In recent years, the realms of computer vision and deep learning have ushered in transformative changes across various domains. Among these, deep learning stands out for its remarkable capacity to handle vast datasets, revolutionizing numerous fields, including the biomedical sector. In particular, its prowess has been harnessed in the realm of brain tumor identification through MRI scans, yielding impressive results. This research project is dedicated to conducting an exhaustive exploration of existing endeavors in the domain of brain tumor identification and classification via MRI scans. This endeavor is poised to be of profound value to researchers looking to leverage their deep learning expertise in the realm of brain tumor detection and categorization. The initial phase involves an overview of prior studies that have employed deep learning for categorising and detecting brain tumors. Subsequently, a meticulous analysis of deep learning studies proposed in research publications spanning (2019 to 2022) is presented in tabular form. The conclusion section comprehensively assesses the merits and demerits inherent in deep neural networks. The insights gleaned from this study promise to equip future researchers with a holistic perspective on current research trends and a nuanced understanding of the effectiveness of diverse deep learning methodologies. It is our fervent belief that this research will significantly advance the understanding of brain tumors and their detection methodologies.

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BrainNET: A Deep Learning Network for Brain Tumor Detection and Classification

Cited by 10 publications

References 7 publications

Weighted Average Ensemble Deep Learning Model for Stratification of Brain Tumor in MRI Images

Weighted Average Ensemble Deep Learning Model for Stratification of Brain Tumor in MRI Images

A Survey of Techniques for Brain Anomaly Detection and Segmentation Using Machine Learning

Brain Tumor Classification and Detection Based DL Models: A Systematic Review

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