Improving Cancer Classification Using Deep Reinforcement Learning with Convolutional LSTM Networks
Bibliographic record
Abstract
Gene Expression Microarray (GEM) data is biological data that contains valuable hidden information genes.The gene information extracted from variations of gene expression levels is utilized for disease detection and diagnosis, especially in cancer classification.Since GEM data contains a relatively large sample size with highly redundant and imbalanced data, the accuracy of the cancer classification result is lower.It is difficult to identify suitable features from large GEM datasets.Hence, in this paper, this model utilizes Grey Wolf Optimization (GWO) Model to select the features from the GEM data.Convolutional Neural Network with Long Short Time Memory (ConvLSTM) is developed by utilizing Deep Reinforcement Learning (DRL) to select the appropriate features and parameters for efficient cancer classification.The ConvLSTM model is used to convert low-level features into high-level ones by identifying distributed data representations.DRL optimizes ConvLSTM parameters iteratively which significantly impacts the overall learning process of this prediction model.In DRL, The Double Deep Q-Network (DDQN) model is introduced to minimize training-time overestimations of action values.Finally, the loss function is employed in the Neural Network (NN) of ConvLSTM for accurate cancer detection and diagnosis of cancer.The proposed model is termed Improved ConvLSTM using DDQN (ICL-DDQN).The ICL-DDQN-DDQN achieves accuracy of 92%, 91.67% and 92.22% for breast cancer, leukemia and lung cancer datasets which is 32.69%,57.16%,23.89% higher than 1D-CNN; 21.06%, 43.18%, 16.89% higher than DL-DCGN; 15%, 28.33%, 10.18% higher than DL-SAE and 6.15%, 132.79%, 4.83% higher than DL-AAA on respective datasets.The proposed model effectively detects cancer at its earlier stage, reducing manual inspection and time for doctors and physicians, resulting in more effective treatment.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".