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has been cited by the following article:

• TITLE: An Adapted Convolutional Neural Network for Brain Tumor Detection

  AUTHORS: Kamagaté Beman Hamidja, Kanga Koffi, Brou Pacôme, Olivier Asseu, Souleymane Oumtanaga

  KEYWORDS: Brain Tumor, MRI, Convolutional Neural Network, KKDNet, GoogLeNet, DensNet, ResNet, ShuffleNet

  JOURNAL NAME: Open Journal of Applied Sciences, Vol.14 No.10, October 23, 2024

  ABSTRACT: In medical imaging, particularly for analyzing brain tumor MRIs, the expertise of skilled neurosurgeons or radiologists is often essential. However, many developing countries face a significant shortage of these specialists, which impedes the accurate identification and analysis of tumors. This shortage exacerbates the challenge of delivering precise and timely diagnoses and delays the production of comprehensive MRI reports. Such delays can critically affect treatment outcomes, especially for conditions requiring immediate intervention, potentially leading to higher mortality rates. In this study, we introduced an adapted convolutional neural network designed to automate brain tumor diagnosis. Our model features fewer layers, each optimized with carefully selected hyperparameters. As a result, it significantly reduced both execution time and memory usage compared to other models. Specifically, its execution time was 10 times shorter than that of the referenced models, and its memory consumption was 3 times lower than that of ResNet. In terms of accuracy, our model outperformed all other architectures presented in the study, except for ResNet, which showed similar performance with an accuracy of around 90%.