$$\mathcal {B}\text {rain}{\mathcal{M}\mathcal{N}}\text {et}$$: a unified neural network architecture for brain image classification

Ghosh, Sudip; Deepti,; Gupta, Shivam

doi:10.1007/s13721-024-00443-8

Cited by 2 publications

(1 citation statement)

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“…These repositories serve as repositories for diverse medical imaging datasets, facilitating access to a broad spectrum of clinically relevant imaging data for research and analytical purposes. Through judicious selection and curation of MRI scans from these reputable sources, a robust foundation was established for the systematic analysis and exploration of brain pathology patterns in the context of tumor presence and classification [27], [28].…”

Section: Datasetmentioning

confidence: 99%

Automated classification of brain tumor-based magnetic resonance imaging using deep learning approach

Owida,

AlMahadin,

Al-Nabulsi

et al. 2024

IJECE

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The treatment of brain tumors poses significant challenges and contributes to a significant number of deaths on a global scale. The process of identifying brain tumors in medical practice involves the visual analysis of photographs by healthcare experts, who manually delineate the tumor locations. However, this approach is characterized by its time-consuming nature and susceptibility to errors. In recent years, scholars have put forth automated approaches to early detection of brain tumors. However, these techniques face challenges attributed to their limited precision and significant false-positive rates. There is a need for an effective methodology to identify and classify tumors, which involves extracting reliable features and achieving precise disease classification. This work presents a novel model architecture that is derived from the EfficientNetB3. The suggested framework has been trained and assessed on a dataset consisting of 7,023 magnetic resonance images. The findings of this study indicate that the fused feature vector exhibits superior performance compared to the individual vectors. Furthermore, the technique that was provided showed superior performance compared to the currently available systems and attained a 100% accuracy rate. As a result, it is viable to employ this technique within a clinical environment for the purpose of categorizing brain tumors based on magnetic resonance images scans.

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Section: Datasetmentioning

confidence: 99%