other · Figshare · 2024
Confusion matrix for binary classification.
Nicodemus Songose Awarayi (18414494), Frimpong Twum (14593669), James Ben Hayfron-Acquah (17507473), Kwabena Owusu-Agyemang (18414497)
Cited by 0Open access
Abstract
<div><p>This study aims to develop an optimally performing convolutional neural network to classify Alzheimer’s disease into mild cognitive impairment, normal controls, or Alzheimer’s disease classes using a magnetic resonance imaging dataset. To achieve this, we focused the study on addressing the challenge of image noise, which impacts the performance of deep learning models. The study introduced a scheme for enhancing images to improve the quality of the datasets. Specifically, an image enhancement algorithm based on histogram equalization and bilateral filtering techniques was deployed to reduce noise and enhance the quality of the images. Subsequently, a convolutional neural network model comprising four convolutional layers and two hidden layers was devised for classifying Alzheimer’s disease into three (3) distinct categories, namely mild cognitive impairment, Alzheimer’s disease, and normal controls. The model was trained and evaluated using a 10-fold cross-validation sampling approach with a learning rate of 0.001 and 200 training epochs at each instance. The proposed model yielded notable results, such as an accuracy of 93.45% and an area under the curve value of 0.99 when trained on the three classes. The model further showed superior results on binary classification compared with existing methods. The model recorded 94.39%, 94.92%, and 95.62% accuracies for Alzheimer’s disease versus normal controls, Alzheimer’s disease versus mild cognitive impairment, and mild cognitive impairment versus normal controls classes, respectively.</p></div>
Cite this paper
(18414494), N. S. A., (14593669), F. T., (17507473), J. B. H.-A., & (18414497), K. O.-A. (2024). Confusion matrix for binary classification. Figshare. https://doi.org/10.1371/journal.pone.0302358.g006
(18414494), Nicodemus Songose Awarayi, et al. “Confusion matrix for binary classification.” Figshare, 2024, https://doi.org/10.1371/journal.pone.0302358.g006.
(18414494), Nicodemus Songose Awarayi, Frimpong Twum (14593669), James Ben Hayfron-Acquah (17507473), and Kwabena Owusu-Agyemang (18414497). 2024. “Confusion matrix for binary classification.” Figshare. https://doi.org/10.1371/journal.pone.0302358.g006.
(18414494), N.S.A. et al. (2024) ‘Confusion matrix for binary classification’, Figshare. Available at: https://doi.org/10.1371/journal.pone.0302358.g006.
@misc{184144942024confusion,
title = {{Confusion matrix for binary classification.}},
author = {(18414494), Nicodemus Songose Awarayi and (14593669), Frimpong Twum and (17507473), James Ben Hayfron-Acquah and (18414497), Kwabena Owusu-Agyemang},
journal = {Figshare},
year = {2024},
doi = {10.1371/journal.pone.0302358.g006}
}Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.