Classification of Pseudopapilledema and Papilledema Using Decision Tree and Hu Moments
Abstract
Pseudopapilledema, characterized by an anomalous elevation of the optic disc without retinal nerve fiber layer edema, often mimics the presentation of true papilledema caused by increased intracranial pressure. Accurate differentiation between these conditions is critical to avoid unnecessary invasive procedures. This study employs a Decision Tree classifier to classify optic disc images into three categories: normal, papilledema, and pseudopapilledema. The dataset, obtained from Kaggle, consists of imbalanced images segmented using the Canny edge detection method and features extracted using Hu Moments. The dataset was divided into 80% training and 20% testing sets. Performance was evaluated using 5-fold cross-validation, yielding an average accuracy of 53.61%, precision of 55.20%, recall of 54.12%, and F1-score of 55.17%. The study provides a comprehensive analysis of the classifier's performance, including visualizations such as segmentation results, scatter plots of Hu Moments, and confusion matrices. The results indicate that while the Decision Tree classifier demonstrates moderate effectiveness, there is significant room for improvement. The research highlights the potential of machine learning models in medical diagnostics but also underscores the need for more robust algorithms and diverse datasets. Future work should focus on incorporating more complex models and expanding the dataset to enhance diagnostic accuracy. These findings contribute to the field of medical image analysis and propose a non-invasive diagnostic tool that, when integrated with clinical expertise, could improve patient outcomes and reduce unnecessary procedures
References
[2] M. R. Sharan, “Classification of Medicinal Leaf by Using Canny Edge Detection and SVM Classifier,” 2022 Int. Conf. Futur. Technol. INCOFT 2022, 2022, doi: 10.1109/INCOFT55651.2022.10094461.
[3] B. P. Sari, “Classification System for Cervical Cell Images based on Hu Moment Invariants Methods and Support Vector Machine,” 2021 Int. Conf. Intell. Technol. CONIT 2021, 2021, doi: 10.1109/CONIT51480.2021.9498353.
[4] Y. Jusman, “Classification System of Malaria Disease with Hu Moment Invariant and Support Vector Machines,” Proc. - 2022 2nd Int. Conf. Electron. Electr. Eng. Intell. Syst. ICE3IS 2022, pp. 365–368, 2022, doi: 10.1109/ICE3IS56585.2022.10010304.
[5] D. İzci, “Comparative performance analysis of slime mould algorithm for efficient design of proportional–integral–derivative controller,” Electrica, vol. 21, no. 1, pp. 151–159, 2021, doi: 10.5152/ELECTRICA.2021.20077.
[6] M. Khushi, “A Comparative Performance Analysis of Data Resampling Methods on Imbalance Medical Data,” IEEE Access, vol. 9, pp. 109960–109975, 2021, doi: 10.1109/ACCESS.2021.3102399.
[7] R. Sun, “A gradient boosting decision tree based GPS signal reception classification algorithm,” Appl. Soft Comput. J., vol. 86, 2020, doi: 10.1016/j.asoc.2019.105942.
[8] H. Azis, D. Widyawati, and ..., “Prediksi potensi donatur menggunakan model Logistic Regression,” Indones. J. …, 2023, [Online]. Available: https://jurnal.yoctobrain.org/index.php/ijodas/article/view/64.
[9] A. Nurul, Y. Salim, and H. Azis, “Analisis performa metode Gaussian Naïve Bayes untuk klasifikasi citra tulisan tangan karakter arab,” Indones. J. Data Sci., vol. 3, no. 3, pp. 115–121, 2022, doi: https://doi.org/10.56705/ijodas.v3i3.54.
[10] U. Zaky, A. Naswin, S. Sumiyatun, and ..., “Performance Analysis of the Decision Tree Classification Algorithm on the Water Quality and Potability Dataset,” Indones. J. …, 2023.
[11] S. K. Jadwaa, “X-Ray Lung Image Classification Using a Canny Edge Detector,” J. Electr. Comput. Eng., vol. 2022, 2022, doi: 10.1155/2022/3081584.
[12] N. A. B. Mary, “Classification of banana leaf diseases using enhanced gabor feature descriptor,” Lecture Notes in Networks and Systems, vol. 145. pp. 229–242, 2021, doi: 10.1007/978-981-15-7345-3_19.
[13] K. U. Devi, “Brain tumour classification using saliency driven nonlinear diffusion and deep learning with convolutional neural networks (CNN),” Journal of Ambient Intelligence and Humanized Computing. 2020, doi: 10.1007/s12652-020-02200-x.
[14] A. Abisha, “Feature Extraction from Plant Leaves and Classification of Plant Health Using Machine Learning,” Lecture Notes in Electrical Engineering, vol. 858. pp. 867–876, 2022, doi: 10.1007/978-981-19-0840-8_67.
[15] Y. Benlachmi, “Fruits Disease Classification using Machine Learning Techniques,” Indones. J. Electr. Eng. Informatics, vol. 10, no. 4, pp. 917–929, 2022, doi: 10.52549/ijeei.v10i4.3907.
[16] S. Kavitha, “Classification of Indian Monument Architecture Styles Using Bi-Level Hybrid Learning Techniques,” Lecture Notes in Networks and Systems, vol. 436. pp. 471–488, 2022, doi: 10.1007/978-981-19-1012-8_32.
[17] A. Naswin and A. P. Wibowo, “Performance Analysis of the Decision Tree Classification Algorithm on the Pneumonia Dataset,” … Artif. Intell. Med. …, 2023.
[18] N. Rismayanti, A. Naswin, U. Zaky, M. Zakariyah, and D. A. Purnamasari, “Evaluating Thresholding-Based Segmentation and Humoment Feature Extraction in Acute Lymphoblastic Leukemia Classification using Gaussian Naive Bayes,” Int. J. Artif. Intell. Med. Issues, vol. 1, no. 2, 2023.
[19] R. A. Azdy, R. F. Syam, E. Faizal, and ..., “Performance Evaluation of Bagging Meta-Estimator in Lung Disease Detection: A Case Study on Imbalanced Dataset,” Int. J. …, 2023.
[20] H. Azis and S. R. Jabir, “Chemical Composition and Aroma Profiling: Decision Tree Modeling of Formalin Tofu,” J. Embed. Syst. Secur. …, 2023.
[21] S. H. Asman, “Decision tree method for fault causes classification based on rms-dwt analysis in 275 kv transmission lines network,” Appl. Sci., vol. 11, no. 9, 2021, doi: 10.3390/app11094031.
[22] F. Manzella, “The voice of COVID-19: Breath and cough recording classification with temporal decision trees and random forests,” Artif. Intell. Med., vol. 137, 2023, doi: 10.1016/j.artmed.2022.102486.
[23] M. A. Hafeez, “Performance improvement of decision tree: A robust classifier using tabu search algorithm,” Appl. Sci., vol. 11, no. 15, 2021, doi: 10.3390/app11156728.
[24] D. Jalal, “Decision Tree and Support Vector Machine for Anomaly Detection in Water Distribution Networks,” 2020 International Wireless Communications and Mobile Computing, IWCMC 2020. pp. 1320–1323, 2020, doi: 10.1109/IWCMC48107.2020.9148431.
[25] R. Hazra, “Machine Learning for Breast Cancer Classification with ANN and Decision Tree,” 11th Annual IEEE Information Technology, Electronics and Mobile Communication Conference, IEMCON 2020. pp. 522–527, 2020, doi: 10.1109/IEMCON51383.2020.9284936.
[26] G. Giri, I. A. Musdar, H. Angriani, and ..., “Enhancing Disease Management in Mango Cultivation: A Machine Learning Approach to Classifying Leaf Diseases,” Indones. J. …, 2023.
[27] A. Faradibah, D. Widyawati, A. U. T. Syahar, and ..., “Comparison Analysis of Random Forest Classifier, Support Vector Machine, and Artificial Neural Network Performance in Multiclass Brain Tumor Classification,” Indones. J. …, 2023.
[28] H. A. Siregar, M. Z. Raditya, A. N. Yesa, and ..., “Comparison of Classification Algorithm Performance for Diabetes Prediction Using Orange Data Mining,” Indones. J. …, 2023.
