Adaptive Loss and Deep Convolutional Neural Networks: A Blending Approach to Self-adaptive Deep Learning Models for Brain Tumor Classification

The primary goal of this research was to lay the groundwork for improvements to self-adaptive deep learning models such as ResNet152, DenseNet169, and InceptionResNetV2. The foundation of this new paradigm is the sophisticated blending of innovative techniques. For example, an adaptive loss function was carefully crafted to dynamically emphasize different tumor stages, and parameterized layers were embedded to modify architectural attributes to facilitate intricate feature extraction. Gradient-based optimization modules were also integrated seamlessly, allowing for real-time parameter refinement. The proposed method makes use of an adaptive loss and a self-adaptive deep convolutional neural network. Adaptive loss function, gradient-based optimization, parameterized layers, the harmonious confluence of these approaches gave the models the ability to adapt autonomously to the nuances of distinct tumor stages. Clinical decision-making may benefit from this adaptability, since it raises the possibility of dramatically upgrading the accuracy of brain tumor class. The model examination further confirmed ResNet152’s superiority. Remarkably, ResNet152 performed at a 98.97% accuracy rate, a 98.99% precision rate, a 98.86% recall rate, and an F1 score of 98.99%. With a loss of only 0.006, ResNet152 is clearly the best of all the models. When compared to the other models, ResNet152 was the obvious winner across all of these crucial metrics.