imagethreshold and ANN,INCREESING ACCURACY

if I have an ANN code to classify ADHA MRI images with little accuracy can I increese it if I make multithresholding for this MRI images , can I increes this accuracy

[Umar]

Hi Sara,

Incorporating multi threshold ing in the preprocessing stage of ADHD MRI image classification can potentially boost the accuracy of the ANN model. By leveraging multithresholding to extract more detailed information and improve feature representation, the classification algorithm can make more accurate predictions. Experimenting with different threshold values and fine-tuning the process can further optimize the classification performance. Here is a simplified example demonstrating how multithresholding can be applied in MATLAB for enhancing ADHD MRI image classification accuracy:

% Load and preprocess MRI images

mri_data = preprocess_mri_images('ADHD_images/');

% Apply multithresholding

thresholded_images = multithresholding(mri_data);

% Train ANN with thresholded images

ann_model = train_ann(thresholded_images, labels);

% Evaluate the model

accuracy = test_ann(ann_model, test_data);

disp(['Classification Accuracy: ', num2str(accuracy)]);

I hope this answers your question.

Answer 1

[Muskan]

Multithresholding involves segmenting an image into multiple regions based on different threshold values. This technique can enhance the features of the image, making it easier for an ANN to learn and classify. By segmenting the MRI images into regions of interest, you might be able to highlight important structures or patterns that are indicative of ADHD.

You can use the MATLAB function "multithresh" to acheive multilevel image thresholds. Please refer to the documentation of "multithresh" for a better understanding: https://www.mathworks.com/help/images/ref/multithresh.html

Here is a sample code on how you can apply multithresholding in MATLAB:

% Load MRI image
image = imread('Image.jpg');

% Convert to grayscale if necessary
if size(image, 3) == 3
    image = rgb2gray(image);
end

% Normalize the image
image = double(image) / 255.0;

% Define threshold values
thresholds = multithresh(image, 3); % Example with 3 thresholds

% Segment the image
segmented_image = imquantize(image, thresholds);

% Display the segmented image
imshow(segmented_image, []);

Kindly refer to the following documentation for more understanding in Image thresholding: https://www.mathworks.com/discovery/image-thresholding.html