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Enhancing Melanoma Classification With Graph Attention Layers and Group Method of Data Handling - Based Feature Extraction
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Melanoma, a deadly form of skin cancer, demands accurate and early diagnosis for effective treatment. In this study, we propose a novel approach to improve melanoma classification by integrating Graph Attention Layers (GALs) into the Group Method of Data Handling (GMDH) framework. Our method leverages the power of GMDH to automatically generate and select informative features from complex melanoma-related data. Simultaneously, GALs are employed to capture intricate relationships and dependencies within the data, often overlooked by traditional classification models. We construct a graph representation where nodes represent data elements (patients or genetic markers) and edges signify relationships between them. GALs are applied to the graph, allowing the model to attend to relevant nodes and connections, enhancing its ability to discern subtle patterns indicative of melanoma. We then train a classification model on this enriched feature set, aiming for superior accuracy in melanoma diagnosis. Experimental results on a diverse melanoma dataset demonstrate the effectiveness of our approach. The model consistently outperforms traditional methods in terms of accuracy, precision, and recall. This study highlights the potential of combining GMDH-based feature extraction with GALs in melanoma classification. This approach not only advances diagnostic accuracy but also provides valuable insights into the underlying factors driving melanoma risk. As early detection remains the key to melanoma treatment success, our proposed method holds promise for improving patient outcomes.
Keywords
Melanoma Classification, Graph Attention Layers, GMDH, Feature Extraction, Early Diagnosis
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- M. Uma Maheswari and A. Aloysius, “Sentiment Analysis in Melanoma Cancer Detection using Ensemble Learning Model”, ICTACT Journal on Image and Video Processing, Vol. 13, No. 2, pp. 2859-2862, 2023.
- M. Pinto, A. Ammendolia and A. De Sire, “Quality of Life Predictors in Patients with Melanoma: A Machine Learning Approach”, Frontiers in Oncology, Vol. 12, pp. 843611- 843618, 2022.
- A.R. Khan, “Facial Emotion Recognition using Conventional Machine Learning and Deep Learning Methods: Current Achievements, Analysis and Remaining Challenges”, Information, Vol. 13, No. 6, pp. 268-278, 2022.
- R. Wald, T. Khoshgoftaar and A. Napolitano, “Filter-and Wrapper-based Feature Selection for Predicting user Interaction with Twitter Bots”, Proceedings of IEEE International Conference on Information Reuse and Integration, pp. 416-423, 2013.
- M. Jiang, Y. Liang and X. Feng, “Text Classification based on Deep Belief Network and Softmax Regression”, Neural Computing and Applications, Vol. 29, No. 1, pp. 61-70, 2018.
- Zainab Abbas Abdulhussein Alwaeli and Abdullahi Abdu Ibrahim, “Predicting Covid-19 Trajectory using Machine Learning”, Proceedings of International Symposium on Multidisciplinary Studies and Innovative Technologies, pp. 1-5, 2020.
- D. Haritha, N. Swaroop and M. Mounika, “Prediction of COVID-19 Cases using CNN with X-Rays”, Proceedings of International Conference on Computing, Communication and Security, pp. 23-29, 2020.
- Victor M. Castro, Chana A. Sacks and Thomas H. McCoy, “Development and External Validation of a Delirium Prediction Model for Hospitalized Patients with Coronavirus Disease 2019”, Journal of the Academy of Consultation Liaison Psychiatry, Vol. 62, No. 3, pp. 1-14, 2021.
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