Towards Transparent Diagnostics: Investigating Architectural Trade-offs and Explainability in Malaria Detection

arXiv:2609.31682 · cs.CV · Submitted 2026-09-16 · Read on arXiv

cs.CV

Submitted: 2026-09-16

Updated: 2026-10-05

Terminology

• Accuracy — 24× in this paper · explained in 1 episode
• Explainability — 11× in this paper · explained in 3 episodes
• Pruning — 11× in this paper · explained in 4 episodes
• Interpretability — 6× in this paper · explained in 15 episodes
• AUC — 4× in this paper · explained in 2 episodes
• Explainable AI (XAI) — 4× in this paper · explained in 7 episodes
• Attention Mechanism — 2× in this paper · explained in 2 episodes
• Fidelity — 2× in this paper · explained in 2 episodes
• Local Interpretable Model-Agnostic Explanations — 2× in this paper
• Model-Agnostic — 2× in this paper · explained in 2 episodes
• Transfer Learning — 2× in this paper · explained in 10 episodes
• Benchmarking — 1× in this paper · explained in 3 episodes
• Feature Engineering — 1× in this paper · explained in 3 episodes
• Feature Extraction — 1× in this paper · explained in 2 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Gradient-weighted Class Activation Mapping — 1× in this paper
• Perplexity — 1× in this paper · explained in 3 episodes
• Quantization — 1× in this paper · explained in 12 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Shapley values — 1× in this paper · explained in 2 episodes
• Transformer — 1× in this paper · explained in 2 episodes

Sources

• M2ANET: Mobile Malaria Attention Network for efficient classification of plasmodium parasites in blood cells
• Learning both Weights and Connections for Efficient Neural Networks
• Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding
• Pruning Filters for Efficient ConvNets
• Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models
• Exploring the Lottery Ticket Hypothesis with Explainability Methods: Insights into Sparse Network Performance
• A Unified Approach to Interpreting Model Predictions

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