Multiclass classification of brain tumors using a novel CNN architecture
Multimedia Tools and Applications, ISSN: 1573-7721, Vol: 81, Issue: 21, Page: 29847-29863
2022
- 36Citations
- 37Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
Brain tumors are a deadly condition that radiologists have a tough time diagnosing. It is critical to make treatment-related decisions based on accurate and timely categorization of malignant cancers. Several approaches for detecting brain tumors have been presented in recent years. These strategies, however, necessitate handmade feature extraction and manual tumor segmentation prior to classification, which is error-prone and time-consuming. To properly extract features and identify brain cancers, an automated tumor diagnosis approach is necessary. Despite significant advancements in the development of such systems, the techniques face challenges due to low accuracy and large false-positive values. In this study, we propose a 13-layer CNN architecture for classifying brain tumors from MRI scans. We tested the suggested model’s performance on a benchmark dataset of 3064 MRI images of three different types of brain cancer (glioma, pituitary, and meningioma) and achieved the highest accuracy of 97.2%, outperforming previous work on the same database. Furthermore, we validated our model on a cross-dataset scenario to demonstrate its efficacy in a real-world scenario. The main goal is to create a lightweight CNN architecture with fewer layers and learnable parameters that can reliably detect tumors in MRI images in the shortest amount of time. The findings show that the proposed technique is effective in classifying brain tumors using MRI images. Because of its adaptability, the proposed algorithm can be easily used in practice to assist doctors in diagnosing brain tumors at an early stage.
Bibliographic Details
Springer Science and Business Media LLC
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