Squeezed-Eff-Net: Edge-Computed Boost of Tomography Based Brain Tumor Classification leveraging Hybrid Neural Network Architecture

TL;DR

A hybrid deep learning model based on SqueezeNet v1 and EfficientNet-B0, and enhanced with handcrafted radiomic descriptors that has almost clinical reliability in automated MRI-based classification of tumors highlighting its possibility of use in clinical decision-support systems.

Abstract

Brain tumors are one of the most common and dangerous neurological diseases which require a timely and correct diagnosis to provide the right treatment procedures. Even with the promotion of magnetic resonance imaging (MRI), the process of tumor delineation is difficult and time-consuming, which is prone to inter-observer error. In order to overcome these limitations, this work proposes a hybrid deep learning model based on SqueezeNet v1 which is a lightweight model, and EfficientNet-B0, which is a high-performing model, and is enhanced with handcrafted radiomic descriptors, including Histogram of Oriented Gradients (HOG), Local Binary Patterns (LBP), Gabor filters and Wavelet transforms. The framework was trained and tested only on publicly available Nickparvar Brain Tumor MRI dataset, which consisted of 7,023 contrast-enhanced T1-weighted axial MRI slices which were categorized into four groups: glioma, meningioma, pituitary tumor, and no tumor. The testing accuracy of the model was 98.93% that reached a level of 99.08% with Test Time Augmentation (TTA) showing great generalization and power. The proposed hybrid network offers a compromise between computation efficiency and diagnostic accuracy compared to current deep learning structures and only has to be trained using fewer than 2.1 million parameters and less than 1.2 GFLOPs. The handcrafted feature addition allowed greater sensitivity in texture and the EfficientNet-B0 backbone represented intricate hierarchical features. The resulting model has almost clinical reliability in automated MRI-based classification of tumors highlighting its possibility of use in clinical decision-support systems.

Figures (5)

• Figure 1: MRI images from the Brain Tumor dataset.
• Figure 2: Hybrid CNN Architecture Proposed Built-in Radiomics-enhanced SqueezeNet v1 and EfficientNet- B0 radiomics-enhanced pathways.
• Figure 3: Confusion Matrix for Brain Tumor MRI Classification using the Proposed Hybrid CNN.
• Figure 4: Training vs. Validation Accuracy Curve.
• Figure 5: Training vs. Validation Loss Curve.