Novel multiple pooling and local phase quantization stable feature extraction techniques for automated classification of brain infarcts

• Main Authors: Dogan, Sengul, Barua, Prabal Datta, Baygin, Mehmet, Chakraborty, Subrata, Ciaccio, Edward J., Tuncer, Turker, Abd Kadir, Khairul Azmi, Shah, Mohammad Nazri Md, Azman, Raja Rizal, Lee, Chin Chew, Ng, Kwan Hoong, Acharya, U. Rajendra
• Format: Article
• Published: 2022
• Subjects: R Medicine (General); Medical technology
• Online Access: http://eprints.um.edu.my/41879/

This study aims to introduce a hand-crafted machine learning method to classify ischemic and hemorrhagic strokes with satisfactory performance. In the first step of this work, a new CT brain for images dataset was collected for stroke patients. A highly accurate hand-crafted machine learning method is developed and tested for these cases. This model uses preprocessing, feature creation using a novel pooling method (it is named P9), a local phase quantization (LPQ) operator, and a Chi(2)-based selector responsible for selecting the most significant features. After that, classification is done using the k-nearest neighbor (kNN) classifier with ten-fold cross-validation (CV). The novel aspect of this model is the P9 pooling method. The inspiration for this pooling method was drawn from the deep learning models, where features are extracted with multiple layers using a convolution operator applied to the pooling method. However, pooling decompositions have a routing problem. The P9 pooling function creates nine d