Journal of Artificial Intelligence and Metaheuristics
Journal DOI
2833-5597ISSN (Online)
Volume 10 , Issue 2 , PP: 52–66, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Doaa Sami Khafaga 1 *
Doi: https://doi.org/10.54216/JAIM.100204
The classification of brain tumors is crucial in the context of early intervention, as the appropriate and timely diagnosis can significantly influence the treatment plan and patient outcomes. Radiologists have long relied on their own judgment and have read these medical images through their own eyes, which is often subjective, time-consuming, and inter-observer variability is also likely to occur. Applications built on artificial intelligence (AI), or more specifically, deep learning (DL)-based algorithms, have radically changed the medical imaging field over the last couple of years and could potentially be used to automate the diagnosis process, offering prompt, trustworthy, and unbiased assessments. Despite such developments, most existing systems that rely on AI are constrained, especially when it comes to classification accuracy and robustness across different datasets. To overcome these problems, the article in this chapter presents a more effective DL model with a specifically designed architecture that aims to improve the classification of brain tumors. The specified methodology is based on preprocessing and data normalization steps that reduce noise and level out the data intensity, enabling effective feature extraction from the MRI images. This will increase the accuracy of the later classification. The primary component of the proposed methodology is an adapted version of DenseNet-201, designed explicitly for the four class brain tumor classification. To achieve optimal performance, the conventional output layer of DenseNet-201 was replaced with a Global Average Pooling (GAP) layer, designed to address the issues of vanishing gradients and overfitting commonly encountered during the training of deep networks. The architectural adjustment helps to combine the features and increase the overall generalization capacity of the model. The model was thoroughly tested using two datasets: one publicly available dataset on Figshare and a locally available dataset comprising a total of 3,504 T1-weighted contrast-enhanced MRI (T1-w MRI) images. The results of the experiment provided the proposed model with a general accuracy of 100 percent, which was higher than that of the existing comparative methods. Such results support the idea that complex architectural adjustments with the broader preprocessing strategy can be effective, and why deep neural networks can be viewed as trustworthy diagnostic tools in clinical neuro-oncology, potentially achieving extremely high accuracy.
Brain tumor , Deep Learning , DenseNet201 , Multi Classification , Transfer Learning
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