Robustness Fine-Tuning Deep Learning Model for Cancers Diagnosis Based on Histopathology Image Analysis
importance 8/10
ai assisted
AI in cancer detection cancer diagnostics stage: research open-source model
Problem
Deep networks for diagnosing colon and lung cancers from histopathology images need improved performance.
Why it matters
Early cancer detection significantly increases likelihood of survival.
What the AI did
A fine-tuned deep network using regularization, batch normalization, and hyperparameter optimization was built to classify colon and lung cancer histopathology images.
Results
On LC2500 dataset: precision 99.84%, recall 99.85%, F1 99.84%, specificity 99.96%, accuracy 99.94%.
Impact
High benchmark scores on LC2500; clinical deployment not stated.
Record
| Venue | Diagnostics |
|---|---|
| Published | 2023 |
| Work type | article |
| Authors | Sameh Abd El-Ghany, Mohammad Azad, Mohammed Mahfouz Elmogy |
| Citations | 34 (OpenAlex, at import time) |
| References | 52 |
| Model or method | not stated in source |
| Organization | not stated in source |
| Primary source | https://doi.org/10.3390/diagnostics13040699 |
| Open access | gold |
| Keywords | histopathology, cancer diagnosis, deep learning, fine-tuning, LC2500 |
Importance rating
8
major — millions of lives, an industry or a field
Abstract states early cancer detection significantly increases survival and histopathology is the most accurate cancer diagnostic method; colon and lung cancers are the targeted diseases.