TinyScope: Lightweight Hyperspectral Tissue Classification Using TinyML Inference on a Single-board Computer

Hyperspectral imaging (HSI) provides high-resolution spectral information capable of distinguishing subtle physiological differences across tissue types, offering significant promise for surgical guidance and point-of-care diagnostics. However, the high dimensionality and computational demands of HSI data have posed barriers to its real-time deployment on low-power embedded platforms. In this work, we develop an end-to-end, deployable pipeline for tissue-level HSI classification on a Raspberry Pi 5 single-board computer, enabling fast and memory-efficient inference without the need for specialized accelerators. A dataset comprising of 630 hyperspectral images (i.e., hypercubes) of ex vivo abdominal tissues across six organ types and four animal models were curated using snapshot HSI cameras. Our approach uses a ResNet-18 model pretrained on ImageNet dataset and modified to accept 16-channel inputs, was fine-tuned to perform multi-tissue classification, achieving a top 1 accuracy of 85.26% using full-precision (FP32) weights. The trained models were then exported to TorchScript and subjected to post-training dynamic quantization. For the INT-8 ResNet-18 model, this resulted in a fourfold reduction in parameter memory (from 44.9 MB to 11.2 MB) and a substantial decrease in peak RAM usage on Pi 5, from 25.15 MB to 6.31 MB, with no loss in classification accuracy. Benchmarks conducted on the Raspberry Pi 5 CPU indicate that the quantized INT-8 model achieves a mean inference latency of 149.42 ms per 128 × 128 ×16 hyperspectral data cube, corresponding to a throughput of 6.37 hypercubes per second. These results demonstrate that high performance HSI classification can be achieved on an embedded hardware costing less than $200, using widely available convolutional architectures and quantization techniques. The proposed pipeline thus offers a practical, accessible, and compact solution for deploying HSI analytics in clinical settings, particularly promising for real-time surgical guidance and point-of-care applications.