Analyzing Activation Functions With Transfer Learning-Based Layer Customization for Improved Brain Tumor Classification

Abstract

Brain tumors pose a significant global health concern, requiring early and accurate detection for effective treatment. Our study presents a binary brain tumor classification architecture leveraging Deep Neural Network (DNN) pre-trained models to reduce misclassification rates. We modified five Convolutional Neural Network (CNN) models using Transfer Learning (TL) and evaluated the effects of seven different Activation Functions (AF). Our proposed architecture was trained, tested, and validated using the ‘‘Br35H: Brain Tumor Detection 2020’’ dataset. The results show that our modified DenseNet121 with Swish AF achieves the best classification performance, with a balanced test accuracy of 99.14% and high scores in Area Under the Curve (AUC), Cohen’s Kappa, Precision, Recall, F1-Score, and Specificity. The proposed architecture also demonstrates practical values in improving medical outcomes, enabling radiologists to focus on complex cases and patient care. It also reduces manual classification time and effort, leading to cost savings for healthcare facilitators. Our study highlights the potential of DNN in brain tumor classification, paving the way for advancements in medical imaging and healthcare technology. The proposed architecture can be adapted for various medical imaging tasks, making it a valuable tool for medical professionals and contributing to improved patient outcomes and enhanced healthcare efficiency.