A Robust and Lightweight Deep CNN for Liver Disease Classification in CT and MRI Using Enhanced Full-Image Processing
Bibliographic record
Abstract
Liver cancer remains a leading cause of mortality worldwide, where early and precise diagnosis plays a crucial role in improving patient outcomes.This study presents a novel deep convolutional neural network (CNN) architecture specifically designed for the classification of liver diseases in Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) images.Unlike previous methods that depend on patch-based analysis or high-complexity transfer learning models, our model processes entire preprocessed images using tailored Hounsfield unit (HU) filtering and Contrast Limited Adaptive Histogram Equalization (CLAHE), eliminating the need for manual annotation and region selection.The proposed CNN demonstrates significant improvements over existing models by achieving 99.8% accuracy, 99.9% precision, and 100% recall across three benchmark datasets: The Cancer Genome Atlas Liver Hepatocellular Carcinoma Collection (TCGA-LIHC), 3D-IRCADb-01, and LiTS17.It also reduces computational overhead while maintaining high diagnostic performance.These advancements highlight the effectiveness and efficiency of our approach in facilitating early detection and classification of liver tumors, offering substantial contributions to computer-aided diagnosis systems.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".