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Explainability is essential for AI models, especially in clinical settings where understanding the model’s decisions is crucial. Despite their impressive performance, black-box AI models are unsuitable for clinical use if their operations cannot be explained to clinicians. While deep neural networks (DNNs) represent the forefront of model performance, their explanations are often not easily interpreted by humans. On the other hand, hand-crafted features extracted to represent different aspects of the input data and traditional machine learning models are generally more understandable. However, they often lack the effectiveness of advanced models due to human limitations in feature design. To address this, we propose ExShall-CNN, a novel explainable shallow convolutional neural network for medical image processing. This model improves upon hand-crafted features to maintain human interpretability, ensuring that its decisions are transparent and understandable. We introduce the explainable shallow convolutional neural network (ExShall-CNN), which combines the interpretability of hand-crafted features with the performance of advanced deep convolutional networks like U-Net for medical image segmentation. Built on recent advancements in machine learning, ExShall-CNN incorporates widely used kernels while ensuring transparency, making its decisions visually interpretable by physicians and clinicians. This balanced approach offers both the accuracy of deep learning models and the explainability needed for clinical applications. 

Scalp electroencephalogram (EEG) signals inherently have a low signal-to-noise ratio due to the way the signal is electrically transduced. Temporal and spatial information must be exploited to achieve accurate detection of seizure events. Most popular approaches to seizure detection using deep learning do not jointly model this information or require multiple passes over the signal, which makes the systems inherently non-causal. In this paper, we exploit both simultaneously by converting the multichannel signal to a grayscale image and using transfer learning to achieve high performance. The proposed system is trained end-to-end with only very simple pre- and post-processing operations which are computationally lightweight and have low latency, making them conducive to clinical applications that require real-time processing. We have achieved a performance of 42.05% sensitivity with 5.78 false alarm per 24 hours on the development dataset of v1.5.2 of the Temple University Hospital Seizure Detection Corpus. On a single core CPU operating at 1.7 GHz, the system runs faster than real-time (0.58 xRT), uses 16 Gbytes of memory, and has a latency of 300 msec.