%% Step 1: Initialize
clc;
clear;

%% Step 2: Load Input CT Images
function images = load_images(dataset_path)
    image_files = dir(fullfile(dataset_path, '*.png'));
    if isempty(image_files)
        image_files = dir(fullfile(dataset_path, '*.jpg'));
    end
    images = cell(1, numel(image_files));
    for i = 1:numel(image_files)
        images{i} = imread(fullfile(dataset_path, image_files(i).name));
    end
end

images = load_images('dataset_path');

%% Step 3: Pre-Processing (Image Enhancement)
function enhanced = median_filter(image)
    enhanced = medfilt2(image);
end

enhanced_images = cellfun(@median_filter, images, 'UniformOutput', false);

%% Step 4: Segmentation using Multilevel Thresholding and PSO
function gray = convert_to_grayscale(image)
    if size(image,3) == 3
        gray = rgb2gray(image);
    else
        gray = image;
    end
end

function thresholds = otsu_thresholding(image)
    thresholds = graythresh(image);
end

function optimal_thresholds = PSO_optimize_thresholds(image, initial_thresholds)
    % Placeholder PSO implementation
    optimal_thresholds = initial_thresholds; % Dummy placeholder
end

function segmented = apply_thresholds(image, thresholds)
    segmented = imbinarize(image, thresholds);
end

segmented_images = cell(1, numel(enhanced_images));
for i = 1:numel(enhanced_images)
    gray_img = convert_to_grayscale(enhanced_images{i});
    init_thresh = otsu_thresholding(gray_img);
    opt_thresh = PSO_optimize_thresholds(gray_img, init_thresh);
    segmented_images{i} = apply_thresholds(gray_img, opt_thresh);
end

%% Step 5: Feature Extraction and Classification using CNN
function model = load_trained_CNN_model(model_path)
    % Placeholder for CNN model loading
    load(model_path, 'model');
end

function features = extract_features(model, image)
    % Placeholder CNN feature extraction
    features = activations(model, image, 'fc');
end

function label = classify_CNN(model, features)
    % Placeholder classification
    score = mean(features(:));
    if score > 0.5
        label = 'Malignant';
    else
        label = 'Benign';
    end
end

trained_CNN = load_trained_CNN_model('model_path');
classification_results = cell(1, numel(segmented_images));
for i = 1:numel(segmented_images)
    features = extract_features(trained_CNN, segmented_images{i});
    classification_results{i} = classify_CNN(trained_CNN, features);
end

%% Step 6: Performance Evaluation
function metrics = evaluate_performance(predictions, ground_truth)
    TP = sum(strcmp(predictions, 'Malignant') & strcmp(ground_truth, 'Malignant'));
    TN = sum(strcmp(predictions, 'Benign') & strcmp(ground_truth, 'Benign'));
    FP = sum(strcmp(predictions, 'Malignant') & strcmp(ground_truth, 'Benign'));
    FN = sum(strcmp(predictions, 'Benign') & strcmp(ground_truth, 'Malignant'));
    
    sensitivity = TP / (TP + FN);
    specificity = TN / (TN + FP);
    accuracy = (TP + TN) / numel(predictions);
    
    metrics = struct('Sensitivity', sensitivity, 'Specificity', specificity, 'Accuracy', accuracy);
end

ground_truth_labels = {}; % Add actual labels here
metrics = evaluate_performance(classification_results, ground_truth_labels);

%% Step 7: Display Results
for i = 1:numel(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);