Brain Glial Cell Tumor Classification through Ensemble Deep Learning with APCGAN Augmentation

International Journal of Computational and Experimental Science and Engineering, Journal Year: 2025, Volume and Issue: 11(1)

Published: Jan. 5, 2025

Classification of brain tumor plays a vital role in medical imaging for accurate diagnosis, treatment, and monitoring. Deep learning approaches have gained significant traction this industry because their ability to extract relevant features from images. The research suggests employing an ensemble classifier with weighted voting mechanism categorize glial cell malignancies such as Astrocytoma, Glioblastoma multiforme, Oligodendroglioma, Ependymoma. proposed technique employs three main classifiers: Convolutional Neural Network (CNN), Long Short Term Memory (C-LSTM), + Conditional Random Fields (DCNN+CRF). algorithms require huge amount input data avoid overfitting. Adaptive Progressive Generative Adversarial Networks (APCGANs) are used produce realistic artificial images efficiently train the methodology. Overall, method strategy consistently outperforms other tested (CNN, C-LSTM, DCNN+CRF). Ensemble attained accuracy 99.4 %, recall - 99.1%, precision- 98.0%, F1-score 99.2%. demonstrates superior performance accurately classifying tumors, making it promising algorithm analysis tasks.

Language: Английский

Feature Extraction Using Hybrid Approach of VGG19 and GLCM For Optimized Brain Tumor Classification

International Journal of Computational and Experimental Science and Engineering, Journal Year: 2024, Volume and Issue: 10(4)

Published: Dec. 25, 2024

A brain tumor is among the illnesses that are fatal. This rationale behind significance of early disease detection. Intelligent techniques always needed to assist researchers and medical professionals in diagnosing tumors. Today's doctors employ a variety approaches identify illness. The most popular technique involves getting an MRI analyzing it look for specific diseases. However, manually evaluating pictures quite complex time-consuming. As result, attempts made discover novel methods cutting down on prediction time. Deep learning algorithms spotting tumor. Many deep employed, including CNN, RNN, LSTM, others. There benefits drawbacks related these methods. One widely utilized categorization CNN. It's critical best features while classifying Resnet, AlexNet, VGGNet, DenseNet some feature extraction employed. In this research, we proposed method extracts unique high-quality using hybrid approach VGG19 GLCM. CNN then used classify resulting images. suggested method's performance evaluation metrics—specificity, sensitivity, ROC, accuracy, loss—are examined. yields 0.98 accuracy. algorithm's sensitivity specificity 0.97 0.99, respectively. model examined by contrasting with currently use.

Language: Английский