Explainable deep learning techniques for microscopic fungi classification using a learnable threshold-based ReLULeaky activation function and transfer learning.

Fungal infections are an increasing risk to human health. They can pose a threat to life and cause a variety of health problems. The traditional diagnosis of fungal infections is challenging due to several reasons, such as the lack of clinical mycologists, costly procedures, a high time commitment, and the need for accuracy and specificity requirements. However, early fungal infections detection is essential for effective treatment. In this work, an explainable fine-tuned ResNet34 model for fungi classification is proposed by integrating transfer learning with a learnable threshold-based ReLULeaky activation function to enrich feature representation and classification performance. To improve feature extraction and convergence, the proposed learnable threshold approach dynamically adjusts activation levels during backpropagation. Our proposed fine-tuned ReLULeaky-ResNet34 method outperforms many tests, achieving the best accuracy (95.39%), F1-score (96%), and precision (97%). In addition, the model achieves a 99.40% area under the curve score, ensuring robust classification performance. The study highlights the efficacy of adaptive thresholds by methodically comparing the current and proposed activation functions. Interpretability confirms that the model focuses on biologically significant morphological features. These results demonstrate that our fine-tuned ReLULeaky-ResNet34 model outperforms for accurate and faster fungi classification.