Lung Mass Identification Using Tiny Deep Learning Based on Lightweight MobileNet and Raspberry Pi 5 for Low Source Medical Diagnostic
Bibliographic record
Abstract
Medical imaging analysis has greatly benefited from deep learning, especially Convolutional Neural Networks (CNNs).However, their enormous parameter sizes and high computational cost restrict their use on low-resource systems.This paper suggests a small deep learning solution for resource-constrained contexts that uses a lightweight CNN, MobileNetV2, deployed on a Raspberry Pi 5 to enable lung mass identification from chest X-ray (CXR) pictures.A total of 2,322 NIH CXR pictures tagged as normal or mass were used to assess two iterations of the model: pretrained and trained from scratch.With just 3.4 million parameters and 300 million FLOPs, the pretrained MobileNetV2 obtained a validation accuracy of 95.25%, test accuracy of 89.9%, precision of 91.43%, and F1 score of 90.14%.A validation accuracy of 91.03%, test accuracy of 85.06%, precision of 88.24%, and F1 score of 85.21% were attained by the scratch-trained version.The results show that it is possible to implement precise CNN-based medical diagnostics on inexpensive, lowpower devices, which could increase access to AI-assisted healthcare in underprivileged areas.This study demonstrates the feasibility of a lightweight deep learning model for realtime lung mass detection in resource-constrained medical settings by presenting its end-toend deployment on the Raspberry Pi 5, from training to on-device inference.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".