Toward reliable computer-aided brain tumor diagnosis: a contrast-enhanced deep learning approach with hybrid KNN classification.
Authors
Affiliations (10)
Affiliations (10)
- Department of MBA, SRM Institute of Science and Technology, Chennai, Tamil Nadu, India.
- Department of CSE, SRM University, Amaravathi, India.
- Department of Electronics and Communication Engineering, Dr RVR NRI Institute of Technology (Deemed to be University), Eluru, India.
- Department of CSE, G H Raisoni University Amravati, Maharashtra, India.
- Department of Mechanical Engineering, Lakireddy Balireddy College of Engineering, Vijayawada, India.
- Department of Electronics and Communication Engineering, Chandigarh University, Gharuan, Punjab, India.
- Department of Information and Communication Technology, Marwadi University, Rajkot, Gujarat, India.
- Centre for Advanced Devices and Systems, Centre of Excellence for Robotics and Sensing Technologies, Multimedia University, Cyberjaya, Selangor, Malaysia.
- Faculty of Artificial Intelligence and Engineering, Multimedia University, Cyberjaya, Selangor, Malaysia.
- Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, UKM Bangi, Bangi, Selangor, Malaysia.
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 (<i>p</i> < 0.001) to the softmax head of the CNN in the paired statistical tests. Class Activation visualization based on gradient analysis revealed that the network concentrated on clinically sensible regions of the tumors rather than other irrelevant background regions. With a compact parameter footprint (0.20 MB in half precision) and an inference throughput of approximately 23.7 images per second on the CPU and 220.1 images per second on an NVIDIA A100 GPU at a batch size of 16, the proposed framework offers an accurate, interpretable, and computationally frugal solution that is suitable for computer-aided brain tumor screening in resource-constrained clinical settings.