Authors - Emerson Joey Caro Abstract - Detecting brain tumors or Brain Tumor Detection(BTD) from MRI scans is an essential step in the assessing of the presence and characteristics of any tumors and formulating an appropriate clinical management plan. The manual interpretation of MRI images by radiolo gists is not time-efficient as well as susceptible to mistakes, which drives the need for automated, accurate and reliable computational methods. In this study we will compare the most advanced Deep Learning (DL) ar chitectures, including traditional CNNs (VGG19, ResNet50, DenseNet), modernized CNNs inspired by transformer design (ConvNext) and Effi cientNet, to tell apart between tumor and non-tumor categories in brain MRI scans. Each model is trained and evaluated on a standardized dataset relying on measurable data such as accuracy, precision, recall, F1-score, F1 score, and confusion matrix. Our results demonstrate that modern CNN architectures such as ConvNext and EfficientNet outper form traditional CNNs, which capture both local texture, spatial patterns and the global spatial context, leading to improved context, resulting in enhanced classification performance. This benchmark is informative in evaluating the best models used in deep learning and adopt them to identify brain tumors, and in turn may be used in optimizing the use of diagnostic decision-making to improve and reducing the burden on the diagnosis.
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