A mass correlation based deep learning approach using deep Convolutional neural network to classify the brain tumor

Satyanarayana, Gandi; Naidu, P. Appala; Desanamukula, Venkata Subbaiah; kumar, Kadupukotla Satish; Rao, B. Chinna

doi:10.1016/j.bspc.2022.104395

Cited by 26 publications

(7 citation statements)

References 19 publications

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“…The model demonstrated promising results in subtype classification, with a commendable accuracy of approximately 87.2% in differentiating between glioma subtypes. Satyanarayana et al 50 presented a CNN with mass correlation analysis for feature extraction and weight assignment. The lack of information in the paper regarding the validation process and the potential biases introduced by the preprocessing steps.…”

Section: Related Workmentioning

confidence: 99%

A multi-class brain tumor grading system based on histopathological images using a hybrid YOLO and RESNET networks

Elazab,

Gab-Allah,

Elmogy

2024

Sci Rep

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Gliomas are primary brain tumors caused by glial cells. These cancers’ classification and grading are crucial for prognosis and treatment planning. Deep learning (DL) can potentially improve the digital pathology investigation of brain tumors. In this paper, we developed a technique for visualizing a predictive tumor grading model on histopathology pictures to help guide doctors by emphasizing characteristics and heterogeneity in forecasts. The proposed technique is a hybrid model based on YOLOv5 and ResNet50. The function of YOLOv5 is to localize and classify the tumor in large histopathological whole slide images (WSIs). The suggested technique incorporates ResNet into the feature extraction of the YOLOv5 framework, and the detection results show that our hybrid network is effective for identifying brain tumors from histopathological images. Next, we estimate the glioma grades using the extreme gradient boosting classifier. The high-dimensional characteristics and nonlinear interactions present in histopathology images are well-handled by this classifier. DL techniques have been used in previous computer-aided diagnosis systems for brain tumor diagnosis. However, by combining the YOLOv5 and ResNet50 architectures into a hybrid model specifically designed for accurate tumor localization and predictive grading within histopathological WSIs, our study presents a new approach that advances the field. By utilizing the advantages of both models, this creative integration goes beyond traditional techniques to produce improved tumor localization accuracy and thorough feature extraction. Additionally, our method ensures stable training dynamics and strong model performance by integrating ResNet50 into the YOLOv5 framework, addressing concerns about gradient explosion. The proposed technique is tested using the cancer genome atlas dataset. During the experiments, our model outperforms the other standard ways on the same dataset. Our results indicate that the proposed hybrid model substantially impacts tumor subtype discrimination between low-grade glioma (LGG) II and LGG III. With 97.2% of accuracy, 97.8% of precision, 98.6% of sensitivity, and the Dice similarity coefficient of 97%, the proposed model performs well in classifying four grades. These results outperform current approaches for identifying LGG from high-grade glioma and provide competitive performance in classifying four categories of glioma in the literature.

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Section: Related Workmentioning

confidence: 99%

A multi-class brain tumor grading system based on histopathological images using a hybrid YOLO and RESNET networks

Elazab,

Gab-Allah,

Elmogy

2024

Sci Rep

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“…In recent years, the rising incidence and mortality rates of brain tumor diseases have posed significant threats to human well-being and life ( Satyanarayana, 2023 ). Because of the different causes and locations of brain tumors, the treatment methods for brain tumors are very different.…”

Section: Introductionmentioning

confidence: 99%

A robust approach for multi-type classification of brain tumor using deep feature fusion

Chen,

Tan,

Zhang

et al. 2024

Front. Neurosci.

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Brain tumors can be classified into many different types based on their shape, texture, and location. Accurate diagnosis of brain tumor types can help doctors to develop appropriate treatment plans to save patients’ lives. Therefore, it is very crucial to improve the accuracy of this classification system for brain tumors to assist doctors in their treatment. We propose a deep feature fusion method based on convolutional neural networks to enhance the accuracy and robustness of brain tumor classification while mitigating the risk of over-fitting. Firstly, the extracted features of three pre-trained models including ResNet101, DenseNet121, and EfficientNetB0 are adjusted to ensure that the shape of extracted features for the three models is the same. Secondly, the three models are fine-tuned to extract features from brain tumor images. Thirdly, pairwise summation of the extracted features is carried out to achieve feature fusion. Finally, classification of brain tumors based on fused features is performed. The public datasets including Figshare (Dataset 1) and Kaggle (Dataset 2) are used to verify the reliability of the proposed method. Experimental results demonstrate that the fusion method of ResNet101 and DenseNet121 features achieves the best performance, which achieves classification accuracy of 99.18 and 97.24% in Figshare dataset and Kaggle dataset, respectively.

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“…Deep learning (DL), a machine learning (ML) approach, has gained prominence and a great deal of interest in each domain, particularly in medical image analysis [ 20 ]. Deep learning can analyze a lot of imbalanced data by passing it through numerous layers; each layer can extract features incrementally and transfer them to the next layer, giving deep learning more power and flexibility [ 21 , 22 ].…”

Section: Introductionmentioning

confidence: 99%

Blockchain-Based Deep CNN for Brain Tumor Prediction Using MRI Scans

2023

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Brain tumors are nonlinear and present with variations in their size, form, and textural variation; this might make it difficult to diagnose them and perform surgical excision using magnetic resonance imaging (MRI) scans. The procedures that are currently available are conducted by radiologists, brain surgeons, and clinical specialists. Studying brain MRIs is laborious, error-prone, and time-consuming, but they nonetheless show high positional accuracy in the case of brain cells. The proposed convolutional neural network model, an existing blockchain-based method, is used to secure the network for the precise prediction of brain tumors, such as pituitary tumors, meningioma tumors, and glioma tumors. MRI scans of the brain are first put into pre-trained deep models after being normalized in a fixed dimension. These structures are altered at each layer, increasing their security and safety. To guard against potential layer deletions, modification attacks, and tempering, each layer has an additional block that stores specific information. Multiple blocks are used to store information, including blocks related to each layer, cloud ledger blocks kept in cloud storage, and ledger blocks connected to the network. Later, the features are retrieved, merged, and optimized utilizing a Genetic Algorithm and have attained a competitive performance compared with the state-of-the-art (SOTA) methods using different ML classifiers.

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A mass correlation based deep learning approach using deep Convolutional neural network to classify the brain tumor

Cited by 26 publications

References 19 publications

A multi-class brain tumor grading system based on histopathological images using a hybrid YOLO and RESNET networks

A multi-class brain tumor grading system based on histopathological images using a hybrid YOLO and RESNET networks

A robust approach for multi-type classification of brain tumor using deep feature fusion

Blockchain-Based Deep CNN for Brain Tumor Prediction Using MRI Scans

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