Breast cancer (BC) remains a leading cause of mortality among women worldwide, with over two million new cases annually. Early detection improves survival rates significantly, yet resource limitations in low- and middle-income countries (LMICs) hinder access to advanced diagnostic tools. While deep learning (DL) models have shown high accuracy in breast cancer detection (BCD), their computational complexity and hardware requirements make them impractical for deployment on low-power devices. To address this, we propose a lightweight convolutional neural network (CNN) for BCD, leveraging knowledge distillation (KD) to transfer knowledge from a complex teacher model (TM) to a smaller student model (SM). Our approach achieves up to 99.3% accuracy, 100% precision, and 99% recall while reducing the number of trainable parameters by 87% compared to conventional deep models. The proposed model successfully runs on a Raspberry Pi 4B with an inference time of 500 ms and memory usage of just 12% (of 8GB RAM), demonstrating its suitability for telemedicine and mobile diagnostics. Additionally, resource utilization experiments confirm that inference remains stable at 10% CPU usage and 400C when using a heat sink and fan, ensuring sustained deployment. Future work will explore federated learning for decentralized training, integration of multimodal data for enhanced diagnosis, and cloud-based model updates using delay-tolerant networks (DTNs) for remote healthcare applications.

The traditional password‐based authentication systems are easy to breach because they rely on what the user knows and not on who is the user. This makes them prone to impersonation, as the culprit can gain access to the system once they knew the user's password, which led to the development of biometric‐based authentication systems. Nonetheless, noisy data, intra‐, and inter‐class variations render these systems inaccurate. However, combining multiple biometric traits (multi‐biometric systems) promises to increase the accuracy of biometric systems. But, this leads to an overhead in energy and latency in the system. These overheads are frowned at in embedded systems, especially those deployed in remote areas. Here, the authors propose a trust management‐based multi‐biometric system. The system switches between a uni‐biometric and a multi‐biometric system to achieve high accuracy and reduced overhead (i.e. energy and latency). The system uses the uni‐biometric technique for trusted users and the multi‐biometric method for untrustworthy users who are zero‐effort impostors. It is found to reduce the false positive rate by 0.29 of Fingerprint uni‐biometric system and the false negative rate by a factor of 0.5 of the multi‐biometric system. Also, the energy consumption is reduced by a factor of 0.2 of the multi‐biometric system.

Breast cancer (BC) mortality rates remain high in Low- and Middle-Income Countries (LMICs) due to limited awareness, poverty, and inadequate medical facilities that hinder early detection. Although deep learning models have achieved high accuracy in BC detection (BCD), they require substantial computational resources, making them unsuitable for deployment in remote or rural areas. This study proposes a lightweight convolutional neural network (CNN) using Knowledge Distillation (KD) for BCD, where a large Teacher Model (TM) transfers learned representations to a smaller Student Model (SM), which is better suited for deployment on low-power devices. We compare it with two prominent model compression techniques: pruning and quantization. Experimental results indicate that the TensorFlow Lite (TFLite)-optimized Student Model (SM_TFLite) achieved 97.67% accuracy, representing a 2.33% relative loss to its teacher, a result comparable to other compression techniques. Its mean accuracy is 73.97% with a 95% Confidence Interval of [65.04%, 82.90%] in a cross-dataset experiment. However, SM_TFLite was the most compact (5.21 kB) and fastest (3.3 ms latency), outperforming both pruned (2924.31 kB, 13.68 ms) and quantized models (746–751 kB, 4–5 ms). Evaluation on a Raspberry Pi 4 Model B demonstrated that all models exhibited similar CPU and memory usage, with SM_TFLite causing only a minor increase in device temperature. These results demonstrate that KD combined with TFLite conversion offers the best trade-off between accuracy, compactness, and speed.