Reducing Loss for Brain Tumour Detection and Classification in MRI using Deep Learning Techniques

The signs of a brain tumour might be general or specific. The typical symptom is brought on by a tumour pressing against the brain or spinal cord. When the tumour has impacted a certain section of the brain and that area is not working correctly. Identification of brain tumours was extremely difficult and complicated due to the location, kind, shape, and size of the tumour in the brain. Brain tumour diagnosis is difficult since the tumor's size and resolution cannot be clearly evaluated in the early stages of growth. However, if a tumour is found and diagnosed early in the stage of tumour growth, there is a good chance that the patient will be treated. As a result, early tumour diagnosis is essential for successful treatment. To solve this problem, we suggested an automated method that can more help detect and classify three different types of brain tumours on MRI using deep learning techniques, faster RCNN+ResNet50.

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Publication Details

Journal
Communications on Applied Nonlinear Analysis
Published
2024-08-15
DOI
https://doi.org/10.52783/cana.v31.1226
Primary Topic
Brain Tumor Detection and Classification
Type
article
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article

Reducing Loss for Brain Tumour Detection and Classification in MRI using Deep Learning Techniques

Venkata Ramana Saddi
Communications on Applied Nonlinear Analysis
Brain Tumor Detection and Classification
article

Reducing Loss for Brain Tumour Detection and Classification in MRI using Deep Learning Techniques

Venkata Ramana Saddi
article en

Abstract

The signs of a brain tumour might be general or specific. The typical symptom is brought on by a tumour pressing against the brain or spinal cord. When the tumour has impacted a certain section of the brain and that area is not working correctly. Identification of brain tumours was extremely difficult and complicated due to the location, kind, shape, and size of the tumour in the brain. Brain tumour diagnosis is difficult since the tumor's size and resolution cannot be clearly evaluated in the early stages of growth. However, if a tumour is found and diagnosed early in the stage of tumour growth, there is a good chance that the patient will be treated. As a result, early tumour diagnosis is essential for successful treatment. To solve this problem, we suggested an automated method that can more help detect and classify three different types of brain tumours on MRI using deep learning techniques, faster RCNN+ResNet50.

Communications on Applied Nonlinear AnalysisVol. 31(6s)
Good health and well-being
Openalex Percentile: Top 85%
Brain Tumor Detection and Classification
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