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EAN: enhanced AlexNet deep learning model to detect brain tumor using magnetic resonance images

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Abstract

Brain tumor is a severe condition that occurs due to the expansion of unnatural brain cells. Because tumors are rare and can take many different forms, it is challenging to estimate the endurance rate of a tumor affected patient. Examining the images obtained from Magnetic Resonance Imaging (MRI) is the fundamental method in locating the tumor affected part in the brain and detecting it with those MRI images is a labor-intensive and difficult process that may yield inaccurate findings. Implementing computer-aided methods is extremely important to overcome these limitations. With the support of the advancement in computer technologies like Artificial Intelligence (AI) and Deep learning (DL), we made use of one of the finest model in deep convolutional neural network (CNN), AlexNet to identify the tumor from MRI images. We incorporated an Enhanced AlexNet (EAN) in line to the proposed layers to categorize the images effectively. Needed data augmentation methods are used to progress the accuracy of our EAN model. From the investigation our EAN model performed well than the other traditional models with respect to accuracy, F1 score, recall and precision with minimum error rate. Our model has managed to produce accuracy rate of 99.32% in terms of classifying the brain tumor from the MRI Images.

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The data that support the findings of this study are available on request from the corresponding author.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, SRM Institute of Science and Technology, Ramapuram, Chennai, India

    M. Azhagiri

  2. Department of Computer Science & Engineering, Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology, Chennai, Tamil Nadu, India

    P. Rajesh

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  1. M. Azhagiri
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Azhagiri, M., Rajesh, P. EAN: enhanced AlexNet deep learning model to detect brain tumor using magnetic resonance images. Multimed Tools Appl 83, 66925–66941 (2024). https://doi.org/10.1007/s11042-024-18143-w

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