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In clinical environments, the transparency of artificial intelligence models is paramount, as understanding the rationale behind automated choices is vital. Although black-box AI frameworks exhibit remarkable efficacy, their deployment in healthcare remains impractical unless their underlying mechanisms can be clarified for medical professionals. Deep neural networks (DNNs) achieve state-of-the-art results, yet the justifications they provide are frequently incomprehensible to human observers. Conversely, traditional machine learning approaches and manually designed features extracted to capture distinct data characteristics offer greater transparency, though human constraints in feature engineering often limit their overall effectiveness. To bridge this gap, we present ExShall-CNN, a novel and transparent shallow convolutional neural network tailored for medical image analytics. This framework builds upon hand-crafted feature methodologies to preserve human interpretability, guaranteeing clear and verifiable decision-making. We introduce the explainable shallow convolutional neural network (ExShall-CNN), which merges the high performance of sophisticated deep architectures like U-Net with the inherent clarity of hand-crafted features for segmenting medical images. Grounded in contemporary machine learning progress, ExShall-CNN integrates widely utilized kernels while maintaining structural openness, allowing medical practitioners to visually inspect and comprehend its outputs. This balanced strategy delivers the precision expected of deep learning paradigms alongside the explainability required for clinical deployment.

The reliability and safety of an entire hydraulic system are compromised by the occurrence of cavitation in axial piston pumps. Because vibration signals can indicate cavitation states within these pumps, they have been increasingly coupled with machine learning techniques for detection purposes. Nevertheless, raw vibration signals captured under actual operating conditions are frequently corrupted by noise, complicating the precise identification of cavitation within noisy environments. To address this issue, this study introduces an intelligent methodology designed to identify cavitation in axial piston pumps under high-noise conditions. Initially, a convolutional neural network (CNN) is trained utilizing spectrogram images that are converted from raw, unstructured vibration datasets across varying cavitation states. Subsequently, the gradient-weighted class activation mapping (Grad-CAM) technique is applied to highlight and visualize the class-discriminative zones within these spectrogram images. Ultimately, a novel image processing approach leveraging the Grad-CAM heatmap is introduced to automatically filter out background noise and amplify the key class features in the spectrograms. The empirical outcomes demonstrate that this strategy substantially boosts the diagnostic efficacy of the CNN framework under noisy conditions. Specifically, the accuracy of cavitation condition classification is elevated from 0.50 to 0.89 at a signal-to-noise ratio of 4 dB, and from 0.80 to 0.92 at 6 dB.

Verbal autopsies (VA) serve as a frequent mechanism within Low- and Medium-Income Countries (LMIC) to ascertain the cause of death (CoD) for individuals who pass away outside of clinical environments, relative to the established international gold standard of physician medical certification. Field interviewers gather detailed testimonies from the families of the deceased concerning the events and circumstances preceding the fatality. This gathered information is documented in text format, referred to as VA narratives. These textual records contain granular details suitable for identifying the CoD. Nonetheless, this methodology continues to be a manual operation characterized by high costs, inconsistency, prolonged processing times, and subjectivity (making it susceptible to errors), among other limitations. Consequently, these disadvantages undermine the VA reporting infrastructure, which is otherwise crucial for defining public health agendas and populating civil registration frameworks.To address this operational gap, the present study introduces interpretable deep learning (DL) methodologies designed to analyze VA narratives and deliver CoD predictions in an efficient, transparent, economical, and precise manner. The verification of our DL models is conducted via optimization and performance accuracy machine learning (ML) curves mapped against the volume of training samples. Our models achieved training set accuracies of (LSTM = 76.11%, CNN = 76.35%, and SEDL = 82.1%), validation accuracies of (LSTM = 67.05%, CNN = 66.16%, and SEDL = 82%), and test set accuracies of (LSTM = 67%, CNN = 66.2%, and SEDL = 82%). Additionally, Local Interpretable Model-agnostic Explanations (LIME) are utilized to enhance the interpretability of the outcomes, thereby fostering confidence in automated healthcare solutions.This work offers robust deep learning strategies for CoD determination via VAs, where the stacked ensemble deep learning (SEDL) model achieved optimal performance, outperforming both Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN). Our empirical evidence suggests that ensemble DL paradigms can be successfully integrated into the CoD classification workflow to assist medical experts in reaching a diagnosis. Ultimately, this integration aims to compress the diagnostic turnaround time for physicians reviewing the narratives, while simultaneously reducing operational expenses and mitigating human error. The scope of this study was constrained by the volume of training data required for model optimization and the substantial lexical diversity present within the unstructured text.