Diagnosis of Medical Images Using Fuzzy Convolutional Neural Networks
Bibliographic record
Abstract
Brain tumors, characterized by the uncontrolled and rapid proliferation of cells, can result in fatal outcomes if not identified and treated promptly.Consequently, the development of a reliable and automated diagnostic system is of paramount importance.In this study, a Fuzzy Convolutional Neural Network (F-CNN) is employed for the efficient diagnosis of brain tumors (Glioma, Meningioma, Pituitary, and non-tumors), leveraging the computational capabilities of Google Colaboratory.The methodology comprises four stages: pre-processing, training, testing, and evaluation.The pre-processing stage entails rescaling the image, resizing, random flipping, and random rotation.The training phase involves the construction of an intelligent model, encompassing four blocks: convolution, ReLU, batch normalization, and max pooling.This is followed by flattening, a fuzzy inferences layer, and a dense layer with dropout.The model was trained using a Kaggle dataset comprising 7022 brain tumor MRI images and validated with a test set of 470 MRI images sourced from the Neurological Wholesale Hospital in Baghdad.The proposed F-CNN model achieved a high accuracy rate of 99.31% while maintaining low computational complexity and time.This work illustrates the potential of Deep Learning approaches, such as F-CNNs, in enhancing the precision and efficiency of medical imaging diagnostics.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".