Dataset URL:
https://www.kaggle.com/datasets/an0gieg/pancreatic-cancer
https://www.cancerimagingarchive.net/collection/pancreas-ct/
Computing infrastructure:
Hardware: Intel(R) Core(TM)2 Duo, Harddisk-1 TB, RAM- 8 GB
Software: OS- Windows-10, IDE: MATLAB R2017a

Program Explanation:
1. Median Filtering for Image Enhancement: Reduced noise and enhanced edges for better visualization.  
2. Segmentation Using Multilevel Thresholding and PSO: Precisely identified regions of interest with optimized thresholds.  
3. Grayscale Conversion: Simplified image data for efficient segmentation and processing.  
4. Feature Extraction Using CNN: Extracted critical patterns from segmented images for accurate classification.  
5. Classification with Pre-Trained CNN: Predicted benign or malignant conditions using trained model layers.  
6. Performance Evaluation: Measured accuracy using sensitivity and specificity metrics.

Implementation Steps:

Step 1: Initialize
clc;
clear;
Step 2: Load Input CT Images
images = load_images('dataset_path'); % Load CT images from dataset
Step 3: Pre-Processing (Image Enhancement)
for i = 1:length(images)
    enhanced_images{i} = median_filter(images{i});
function enhanced_image = median_filter(image)
end
Step 4: Segmentation using Multilevel Thresholding and PSO
for i = 1:length(enhanced_images)
    Convert to Grayscale 
    grayscale_image = convert_to_grayscale(enhanced_images{i});
      Compute initial thresholds using Otsu's method
    initial_thresholds = otsu_thresholding(grayscale_image);
function thresholds = otsu_thresholding(image)
        Optimize thresholds using PSO
    optimal_thresholds = PSO_optimize_thresholds(grayscale_image, initial_thresholds);
function optimal_thresholds = PSO_optimize_thresholds(image, initial_thresholds)
      Apply optimized thresholds for segmentation
    segmented_images{i} = apply_thresholds(grayscale_image, optimal_thresholds);
function segmented_image = apply_thresholds(image, thresholds)
end
Step 5: Feature Extraction and Classification using CNN
trained_CNN = load_trained_CNN_model('model_path'); % Load pre-trained CNN
for i = 1:length(segmented_images)
    features = extract_features(trained_CNN, segmented_images{i}); % Extract Features
    classification_results{i} = classify_CNN(trained_CNN, features); % Classify as Benign/Malignant
function features = extract_features(model, image)
function label = classify_CNN(model, features)
end
Step 6: Performance Evaluation
metrics = evaluate_performance(classification_results, ground_truth_labels); % Sensitivity, Specificity, etc.
function metrics = evaluate_performance(predictions, ground_truth)
Step 7: Display Results
for i = 1:length(segmented_images)
    figure;
    subplot(1, 2, 1), imshow(images{i}), title('Original Image');
    subplot(1, 2, 2), imshow(segmented_images{i}), title('Segmented Image');
end
disp('Performance Metrics:');
disp(metrics);