Prediction of Lungs Cancer in Medical Images Using Deep Learning Approach
Bibliographic record
Abstract
In this advanced and fast-moving world, imaging techniques are the root of any medical diagnosis system.Encroachment in technology and machine learning methods may well aid radiologists in the verdict of tumors without any aggressive measures.CT images are mostly used to identify the deep defection of human being especially for lungs cancer.In this proposed system, we implemented the automatic prediction of lungs cancer in CT images using a new deep learning architecture named as Improved Cross Channel AlexNet (ICCAN).Segmentation of lungs cancer is performed via super pixel segmentation algorithm.This segmentation is used to remove the null regions in the CT images and also retain the features of tumor to make the system of efficient prediction.Once the image is properly segmented then it was categorized using our proposed deep learning network of ICCAN.Our proposed deep network is designed using transfer learning mechanism from the pretrained model of AlexNet.The simulation results are analyzed and compared the performance of proposed system with two different existing algorithms of HMM and SVM classification in terms of Accuracy.We achieved 99% of accuracy and F1-score of 99.74% which is higher than the previous implementations and also time complexity of our system is very low compared with HMM and SVM.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".