Automatic Classification of Ovarian Cancer Types from CT Images Using Deep Semi-Supervised Generative Learning and Convolutional Neural Network
Bibliographic record
Abstract
The classification of ovarian cancer types is a very challenging process for physicians' eyes.To solve this problem, this article proposes a new deep learner, which classifies ovarian cancer types from Computerized Tomography (CT) images.Firstly, a Deep Convolutional Neural Network (DCNN) model depending on AlexNet is proposed to categorize ovarian cancer from CT images.But its efficiency is not satisfactorily high.So, DCNN is built based on the fusion of AlexNet, VGG, and GoogLeNet.The fusion is carried out at the SoftMax layer by fusing the SoftMax values of each network structure using a weighted sum to obtain the overall classification outcome.But overfitting problems can occur due to an inadequate number of training images.Thus, a Deep Semi-Supervised Generative Learning with DCNN model (DSSGL-DCNN) is proposed by using a Generative Adversarial Network (GAN) which augments the training samples to solve the overfitting problem.Once the augmented dataset is obtained, the fused DCNN model is learned to classify ovarian cancer types.Further, the classified outcomes can be used as a useful guideline for physicians in medical diagnosis.Finally, the experimental results show that the DSSGL-DCNN achieves higher efficiency compared to the other DCNN architectures.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".