Medical Image Analysis Using Machine Learning for Pneumonia and Brain Tumor Detection
**Medical Image Analysis Using Machine Learning for Pneumonia and Brain Tumor Detection Medical image analysis using machine learning (ML) has become a crucial tool in diagnosing and detecting various conditions, including pneumonia and brain tumors. In our approach, we utilize both a brain tumor image dataset and a pneumonia image dataset. Our base model incorporates EfficientNet-B0, Grad-CAM for explainable AI, the ReLU activation function, and the Adam optimizer with different learning rates. Below is a detailed summary of our method and results.
Pneumonia Detection Method->
Data Collection and Preprocessing: Gather chest X-ray datasets such as NIH ChestX-ray14. Normalize pixel values, augment data with transformations (rotations, flips, etc.), and potentially segment lung regions for focused analysis.
Model Development: Base Model: Employ EfficientNet-B0 for its efficiency and effectiveness in image classification tasks. Activation Function: Utilize the ReLU activation function for its simplicity and ability to mitigate vanishing gradient problems. Optimizer: Use the Adam optimizer with various learning rates to efficiently update model parameters during training. Explainable AI:
Apply Grad-CAM to generate visualizations highlighting the areas of the chest X-rays that the model relies on for predicting pneumonia, aiding in interpretability.
Evaluation: Training Accuracy: Achieved 99.26% accuracy on the training data. Test Accuracy: Achieved 96.24% accuracy on the test data. Training Loss: Recorded a training loss of 0.04. Test Loss: Recorded a test loss of 0.1. Validate and assess the model's performance using metrics like accuracy, AUC-ROC, sensitivity, and specificity to ensure robustness and generalization.
Brain Tumor Detection Method->
Data Collection and Preprocessing: Acquire MRI scans from datasets like BraTS. Preprocess the data by normalizing intensity values, performing skull stripping, and segmenting brain and tumor regions. Model Development:
Base Model: Utilize EfficientNet-B0 for its balance of efficiency and accuracy in image classification tasks. Activation Function: Apply the ReLU activation function to enhance model performance and training stability. Optimizer: Use the Adam optimizer with varying learning rates to iteratively update model parameters during training. Explainable AI:
Employ Grad-CAM to generate visual explanations of the regions in MRI scans that the model deems important for detecting brain tumors, aiding in interpretability and trust.
Evaluation: Training Accuracy: Achieved 99.26% accuracy on the training data. Test Accuracy: Achieved 96.24% accuracy on the test data. Training Loss: Recorded a training loss of 0.04. Test Loss: Recorded a test loss of 0.1. Validate and evaluate the model's performance using metrics like the Dice coefficient, Jaccard index, accuracy, sensitivity, and specificity to ensure reliability and effectiveness.
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