DEVELOPING AN IMPROVISED DEEP LEARNING ALGORITHM FOR DIAGNOSING GLAUCOMA

Pancreatic cancer is one of the deadliest cancers, with a high mortality rate due to late detection and limited diagnostic accuracy. Effective and early classification of pancreatic tumors from medical images is critical to improving patient outcomes. Conventional deep learning methods often struggle with overfitting, imbalanced datasets, and lack of generalization across different image modalities. Existing single-model deep learning approaches lack robustness and accuracy, particularly in the classification of complex and heterogeneous pancreatic tumors. There is a growing need for a scalable and ensemble-based solution to enhance diagnostic accuracy while minimizing false predictions. This study proposes an ensemble deep learning framework that integrates three high-performing convolutional neural networks (CNNs): ResNet50, DenseNet201, and InceptionV3. Each model is fine-tuned on a curated pancreatic tumor dataset using transfer learning and combined using a weighted majority voting mechanism. The framework enhances feature extraction diversity and leverages complementary model strengths. The proposed ensemble model achieved superior performance over individual models and existing hybrid approaches. Specifically, it attained an overall accuracy of 96.3%, precision of 95.1%, recall of 96.7%, F1-score of 95.9%, and AUC of 0.982 on the test dataset. Compared to state-of-the-art hybrid models such as CNN-SVM, ResNet-GRU, and DenseNet-LSTM, our method demonstrated higher stability and generalization in classification.