ArticleFrontiers in human neuroscience2026
Toward reliable computer-aided brain tumor diagnosis: a contrast-enhanced deep learning approach with hybrid KNN classification.
Article in Frontiers in human neuroscience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Timely and accurate diagnosis of brain tumors using magnetic resonance imaging (MRI) is a major concern in neuro-oncology because of the potential effects of delayed and inconsistent radiological diagnoses on the management of such diseases. Manual analysis of MRIs is time-consuming and highly susceptible to observer variations; thus, there is a need for computer-based and more consistent diagnostic processes. In this study, an automatic diagnostic process based on deep learning was proposed using multiscale contrast enhancement along with lightweight convolutional features and classifiers for diagnosing four classes of brain tumors. The MRI images were analyzed using a Multi-scale Intensity Contrast Amplification (MICA) algorithm that enhances the relevant structural information of the tumors across fine, intermediate, and coarse spatial scales. The refined images were then fed into an in-house convolutional neural network (CNN) backbone architecture that uses depth-wise separable convolution, residual skip connection, and squeeze-and-excitation (SE) attention mechanism to extract compact and discriminative feature representations. To select the optimal decision layer for this representation, three supervised learning methods, namely K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Random Forest (RF), were trained on this set of features and compared with the softmax head of the CNN. The experiments were performed on a publicly available, three-source combined MRI dataset of 7,023 images across glioma, meningioma, pituitary tumor, and normal categories using both hold-out and 5-fold cross-validation setups. In all the evaluated configurations, the hybrid model of CNN and KNN provided the best hold-out accuracy (96.58%), whereas the hybrid model of CNN and SVM offered the best macro-average precision (98.81%) and area under the ROC curve (99.56%). Both hybrids were statistically superior (
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