Abstract
In medical emergencies, one of the areas where immediate and rapid response is required is known as brain hemorrhages. It is a severe condition where a slight delay in diagnosis can result in the loss of life. Thus, for faster processing and taking second opinions, several computer-aided diagnosis (CAD) systems were explored in medical emergencies. The past works, in the machine learning domain, used the single modality-based features analysis for abnormality representation in CAD systems. This feature set was evaluated as a limited feature contributor in machine learning as hemorrhages are not associated with any specific shape, size, and texture information. By this motivation, the presented work explored the large-scale multivariate features pool for representing the hemorrhages properly. The proposed feature extraction phase can extract an overall 44 features of different domains which can be further evaluated by using various machine learning classification models. The experimentations are conducted on the hemorrhage dataset having 200 samples classified in binary form. In experimentation, three machine learning models, i.e., KNN, SVM, and neural network, are tested on various feature set combinations to find relevant features used for better interpretation of hemorrhages. The comparative result of these classifiers on various parameter settings proves the efficacy of the model.
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Malik, P., Vidyarthi, A. (2023). A Large-Scale Multivariate Features-Based Classification of Brain Hemorrhage Using Machine Learning Algorithms. In: Swaroop, A., Kansal, V., Fortino, G., Hassanien, A.E. (eds) Proceedings of Fourth Doctoral Symposium on Computational Intelligence . DoSCI 2023. Lecture Notes in Networks and Systems, vol 726. Springer, Singapore. https://doi.org/10.1007/978-981-99-3716-5_9
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DOI: https://doi.org/10.1007/978-981-99-3716-5_9
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