Novel Deep Learning Architectures for Classification and Segmentation of Brain Tumors from MRI Images

• URL: http://arxiv.org/abs/2512.06531v1
• Date: Sat, 06 Dec 2025 18:49:57 GMT
• Title: Novel Deep Learning Architectures for Classification and Segmentation of Brain Tumors from MRI Images
• Authors: Sayan Das, Arghadip Biswas,
• Abstract summary: Brain tumors pose a significant threat to human life, therefore it is very much necessary to detect them accurately in the early stages.<n>The incidence of brain tumors has risen amongst children and adolescents in recent years, resulting in a substantial volume of data.<n>We have proposed two novel Deep Learning Architectures - (a) SAETCN (Self-Attention Enhancement Tumor Classification Network) for the classification of different kinds of brain tumors.
• Score: 0.8846824366848378
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Brain tumors pose a significant threat to human life, therefore it is very much necessary to detect them accurately in the early stages for better diagnosis and treatment. Brain tumors can be detected by the radiologist manually from the MRI scan images of the patients. However, the incidence of brain tumors has risen amongst children and adolescents in recent years, resulting in a substantial volume of data, as a result, it is time-consuming and difficult to detect manually. With the emergence of Artificial intelligence in the modern world and its vast application in the medical field, we can make an approach to the CAD (Computer Aided Diagnosis) system for the early detection of Brain tumors automatically. All the existing models for this task are not completely generalized and perform poorly on the validation data. So, we have proposed two novel Deep Learning Architectures - (a) SAETCN (Self-Attention Enhancement Tumor Classification Network) for the classification of different kinds of brain tumors. We have achieved an accuracy of 99.38% on the validation dataset making it one of the few Novel Deep learning-based architecture that is capable of detecting brain tumors accurately. We have trained the model on the dataset, which contains images of 3 types of tumors (glioma, meningioma, and pituitary tumors) and non-tumor cases. and (b) SAS-Net (Self-Attentive Segmentation Network) for the accurate segmentation of brain tumors. We have achieved an overall pixel accuracy of 99.23%.

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