Optimal Detection of Acute Lymphoblastic Leukemia Using Deep Transfer Learning Model
Bibliographic record
Abstract
Acute Lymphoblastic Leukemia (ALL), a rapidly progressing malignancy originating from hematopoietic cells, necessitates prompt and precise diagnosis due to its potential lethality within a short span of months.Technological advancements are therefore pivotal in aiding medical practitioners to reduce the probability of human error, expedite diagnosis and subsequently, improve patient outcomes.This study presents a novel system leveraging Convolutional Neural Networks (CNNs), capable of diagnosing ALL through image analysis of affected cells.Our proposed system employs two well-established CNN architectures, VGG16 and ResNet50, coupled with two optimization algorithms, Adam and RMSprop, to classify image data into two distinct categories.The utilized dataset, C-NMC Leukemia 2019, was subjected to a variety of test scenarios involving differing epoch variations (10, 20, 30, 40, 50, 60, 80, and 100) and a consistent learning rate of 0.0001.The results suggest that the proposed system exhibits superior performance when utilizing the VGG16 architecture in conjunction with the Adam optimizer, achieving a training accuracy of 93.80% and a testing accuracy of 87.00%.The findings of this study accentuate the potential of integrating deep learning techniques into the diagnostic process of ALL, thereby facilitating rapid, precise detection and ultimately contributing to the improvement of patient prognosis.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".