Early and accurate detection of oral cancer is critical for improving patient outcomes. This study presents a comprehensive comparison of five convolutional neural network architectures—MobileNetV2, Xception, VGG19, ResNet50, and DenseNet201—for automated oral cancer detection from histopathological images. All models were fine-tuned through transfer learning using ImageNet pretrained weights and evaluated on 5,192 histopathological images from Kaggle. On this dataset, MobileNetV2 achieved highest classification accuracy $(\mathbf{9 0. 0 0 \%})$, followed by DenseNet201 (87.69%), VGG19 (86.54%), Xception (86.15%), and ResNet50 (83.27%). Statistical validation via paired t-tests confirmed significant performance differences $(p<0.05)$. Beyond accuracy, MobileNetV2 demonstrated 38% faster training, 84% smaller model size, and 33% faster inference, showing promise for resource-constrained clinical settings pending external validation.
Prema Hiremath, K. N., S. Nambiar et al.· 2026 11th International Conf...· 0 citations
Massive MIMO systems require simultaneous optimization of energy efficiency, latency, and handover performance, yet existing approaches address these objectives in isolation across disparate parameter spaces. This paper proposes a multi-state parameter self-optimization framework that jointly optimizes across five interdependent operational states—channel, mobility, system configuration, power, and latency—using deep reinforcement learning. We formulate the problem as a multi-objective Markov decision process and implement five optimization approaches: Hybrid Action Space Reinforcement Learning, Q-Learning with Kalman Filter prediction, LSTM Autoencoder for PAPR reduction, bio-inspired Integrated Fruit Fly Salp Swarm Optimization for power allocation, and a proposed Multi-Agent Deep Q-Network (MA-DQN) with experience replay. Simulation results across antenna configurations from 16 to 256 elements and user counts from 5 to 40 show that the proposed MA-DQN achieves a composite performance score of $83 \pm 1.8 / 100$ across all five states (averaged over 10 seeded runs), outperforming the best single-objective method by $\mathbf{2 6} \boldsymbol{\%}$. The framework delivers 29-73% energy efficiency improvement over fixed baselines, with the learned policy favoring moderate power (0.1-0.5W) and lower antenna counts (16-32)—consistent with analytical models that show circuit power dominance at high antenna counts.
Madhu Kumari Ray, Sasmita Mohapatra, C. J.· 2026 11th International Conf...· 0 citations