Fibonacci-Net: A Lightweight CNN model for Automatic Brain Tumor Classification

• URL: http://arxiv.org/abs/2503.13928v1
• Date: Tue, 18 Mar 2025 05:47:02 GMT
• Title: Fibonacci-Net: A Lightweight CNN model for Automatic Brain Tumor Classification
• Authors: Santanu Roy, Ashvath Suresh, Archit Gupta, Shubhi Tiwari, Palak Sahu, Prashant Adhikari, Yuvraj S. Shekhawat,
• Abstract summary: This research proposes a very lightweight model "Fibonacci-Net" along with a novel pooling technique, for automatic brain tumor classification from MRI datasets.<n> Experimental results reveal that, after employing the proposed Fibonacci-Net, we have achieved 96.2% accuracy, 97.17% precision, 95.9% recall, 96.5% F1 score, and 99.9% specificity on the most challenging 44-classes MRI dataset''
• Score: 1.5705429611931057
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: This research proposes a very lightweight model "Fibonacci-Net" along with a novel pooling technique, for automatic brain tumor classification from imbalanced Magnetic Resonance Imaging (MRI) datasets. Automatic brain tumor detection from MRI dataset has garnered significant attention in the research community, since the inception of Convolutional Neural Network (CNN) models. However, the performance of conventional CNN models is hindered due to class imbalance problems. The novelties of this work are as follows: (I) A lightweight CNN model is proposed in which the number of filters in different convolutional layers is chosen according to the numbers of Fibonacci series. (II) In the last two blocks of the proposed model, depth-wise separable convolution (DWSC) layers are employed to considerably reduce the computational complexity of the model. (III) Two parallel concatenations (or, skip connections) are deployed from 2nd to 4th, and 3rd to 5th convolutional block in the proposed Fibonacci-Net. This skip connection encompasses a novel Average-2Max pooling layer that produces two stacks of convoluted output, having a bit different statistics. Therefore, this parallel concatenation block works as an efficient feature augmenter inside the model, thus, automatically alleviating the class imbalance problem to a certain extent. For validity purpose, we have implemented the proposed framework on three MRI datasets which are highly class-imbalanced. (a) The first dataset has four classes, i.e., glioma tumor, meningioma tumor, pituitary tumor, and no-tumor. (b) Second and third MRI datasets have 15 and 44 classes respectively. Experimental results reveal that, after employing the proposed Fibonacci-Net we have achieved 96.2% accuracy, 97.17% precision, 95.9% recall, 96.5% F1 score, and 99.9% specificity on the most challenging ``44-classes MRI dataset''.

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