Catalyzing Healthcare Advancements: Integrating IoT-Driven Smart Systems and Deep Learning for Precision Breast Cancer Detection in Telemedicine
Bibliographic record
Abstract
Background: Timely detection and treatment of serious diseases, including cancer, are crucial for saving lives and improving longevity.The Internet of Medical Things (IoMT) holds promise for enhancing healthcare by enabling real-time disease identification through automated image analysis.However, integrating large deep learning models with IoMT devices poses challenges.Objective: This study aims to develop an efficient deep learning model, "EffiPathNet," specifically designed for analyzing histopathological images with a focus on achieving both accuracy and speed.Method: EffiPathNet was developed to address the challenges associated with large models and to ensure compatibility with IoMT imaging devices.The model was tested on a reputable histopathological image dataset, evaluating its accuracy, speed, and computational requirements.Result: EffiPathNet achieved an average accuracy of 97.79% and a 0.987 F1 score, demonstrating its exceptional ability to accurately classify histopathological images.The model's lightweight design, requiring only a few kilobytes in size, enhances its compatibility with IoMT imaging devices.Main Findings: The study highlights EffiPathNet's efficacy in accurately classifying histopathological images and its potential for integration with IoMT devices.The lightweight design further enhances its suitability for practical IoMT applications.Conclusion: EffiPathNet emerges as a promising solution for real-time disease identification in histopathological images, combining high accuracy with computational efficiency.Its compatibility with IoMT devices suggests its potential for practical implementation in healthcare settings, contributing to timely and effective medical interventions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".