How to increase accuracy from 64.44% in this code?

clc
clear all
close all

% Define the directory where images are located
imageDirectory = 'C:\Users\acer\OneDrive\Desktop\benign';

% Get a list of all folders within the directory
folders = dir(imageDirectory);
folders = folders([folders.isdir]);
folders = folders(3:end); % Remove '.' and '..' entries

% Initialize feature matrix and labels to store the extracted features and their corresponding labels
featureMatrix = [];
labels = [];

% Loop through each folder
for folderIdx = 1:length(folders)
    folderPath = fullfile(imageDirectory, folders(folderIdx).name);
    fprintf('Processing images in folder: %s\n', folderPath);
    
    % Get a list of all images within the folder
    imageFiles = dir(fullfile(folderPath, '*.png')); 
    
    % Determine the label for this folder (benign or malignant)
    if contains(lower(folders(folderIdx).name), 'benign')
        label = 0; % Benign
    else
        label = 1; % Malignant
    end
    
    % Loop through each image
    for imageIdx = 1:length(imageFiles)
        imagePath = fullfile(folderPath, imageFiles(imageIdx).name);
        
        % Read the image
        originalImage = imread(imagePath);
        
        % Preprocessing techniques (use histogram equalization as enhancement)
        preprocessedImage = histeq(originalImage);
        
        % Convert to grayscale if the image has multiple channels
        if size(preprocessedImage, 3) > 1
            preprocessedImage = rgb2gray(preprocessedImage);
        end
        
        % Apply thresholding
        thresholdedImage = imbinarize(preprocessedImage, graythresh(preprocessedImage));
        
        % Feature extraction (same as before)
        meanIntensity = mean2(preprocessedImage);
        stdDeviation = std2(preprocessedImage);
        medianIntensity = median(preprocessedImage(:));
        rangeValue = range(preprocessedImage(:));
        skewnessValue = skewness(double(preprocessedImage(:)));
        kurtosisValue = kurtosis(double(preprocessedImage(:)));
        percentiles = prctile(double(preprocessedImage(:)), [5, 25, 50, 75, 95]);
        entropyValue = entropy(preprocessedImage);
        varianceValue = var(double(preprocessedImage(:)));
        histogramFeatures = [mean(preprocessedImage(:)), var(double(preprocessedImage(:))), ...
            skewness(double(preprocessedImage(:))), kurtosis(double(preprocessedImage(:))), ...
            mode(preprocessedImage(:)), entropy(preprocessedImage(:))];
        
        % Append the features to the feature matrix
        features = [meanIntensity, stdDeviation, medianIntensity, rangeValue, ...
            skewnessValue, kurtosisValue, percentiles, entropyValue, varianceValue, histogramFeatures];
        featureMatrix = [featureMatrix; features];
        
        % Append the label to the labels array
        labels = [labels; label];
    end
end

% Convert featureMatrix and labels to double data type
featureMatrix = double(featureMatrix);
labels = double(labels);

% SVM Classifier
svmModel = fitcsvm(featureMatrix, labels, 'KernelFunction', 'linear');

% Evaluate the SVM model using k-fold cross-validation
numFolds = 5; % Number of cross-validation folds
cv = cvpartition(size(featureMatrix, 1), 'KFold', numFolds);
accuracy = zeros(numFolds, 1);

for foldIdx = 1:numFolds
    trainIdx = cv.training(foldIdx);
    testIdx = cv.test(foldIdx);
    
    svmModel = fitcsvm(featureMatrix(trainIdx, :), labels(trainIdx), 'KernelFunction', 'linear');
    predictedLabels = predict(svmModel, featureMatrix(testIdx, :));
    
    accuracy(foldIdx) = sum(predictedLabels == labels(testIdx)) / numel(labels(testIdx));
end

% Calculate the mean accuracy across all folds
meanAccuracy = mean(accuracy);

fprintf('Mean Accuracy: %.2f%%\n', meanAccuracy * 100);