Citation :

Vivek Kumar Gupta, Suresh Jain, Kailash Chandra Bandhu, "Brain Tumor Detection through Deep Learning-Based Medical Image Classification," International Journal of Engineering Trends and Technology (IJETT), vol. 74, no. 4, pp. 360-377, 2026. Crossref, https://doi.org/10.14445/22315381/IJETT-V74I4P127

Abstract

Magnetic Resonance Imaging (MRI) classification of brain tumors has become a needed procedure in clinical diagnosis, where correct and timely diagnosis could be very useful in the planning of treatment and the outcome of the patient. Nevertheless, current methods of deep learning are largely limited to generalization because of single -dataset dependence, absence of attention, and interpretability. In response to these issues, this paper suggests a multi-faceted deep learning-based model of robust brain tumor classification with the use of heterogeneous MRI data. The main purpose of the study is to construct and test the effective classification model with multiple convolutional neural (CNN) architectures and attention-increased models. To guarantee the cross-dataset validation and strength, the study utilizes two publicly available datasets- the Figshare Brain Tumor Dataset and BraTS2020. All MRI images are subjected to a standardized preprocessing pipeline that includes resizing, normalization, and data augmentation. CNN-based VGG16, ResNet50, AlexNet, EfficientNetB0, and a newly designed lightweight LeNet with Squeeze-and-Excitation (SE) blocks are trained and optimized through transfer learning and tuned hyperparameters. Further, the experimental results will show that the suggested LeNet + SE model has the highest performance on the BraTS2020 dataset, having an accuracy of 98.44, precision of 98.43, recall of 98.44, and F1-score of 98.43. VGG16 and ResNet50 have the highest accuracy of 93.31 on the Figshare dataset, which means that they have good classification by structured MRI data. The high accuracy of the suggested model makes the focus of attention-based channel recalibration efficient in capturing discriminative tumor features without a significant sacrifice in computational efficiency. The proposed framework has great potential in improving the robustness, generalization, and interpretability of brain tumor classification that can be utilized as a reliable method of classifying medical images.

Keywords

Brain Tumor, Deep Learning, Medical Imaging, Image Classification, Tumor Detection.

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