A New Method for Classifying Intracerebral Hemorrhage (ICH) Based on Diffusion Weighted –Magnetic Resonance Imaging (DW-MRI)

Stroke is a condition where the blood supply to the brain is cut off. This occurs due to the rupture of blood vessels in the intracerebral area or Intracerebral Hemorrhage (ICH). Examination by health workers is generally carried out to get an overview of the part of the brain of a patient who has had a stroke. The weakness in diagnosing this disease is that deeper knowledge is needed to classify the type of stroke, especially ICH. This study aims to use the Modified Layers Convolutional Neural Network (ML-CNN) method to classify ICH stroke images based on Diffusion-Weighted (DW) MRI. The data used in this study is a DWI stroke MRI image dataset of 3,484 images. The data consists of 1,742 normal and ICH images validated by a radiologist. Because the data used is relatively small and takes into account the computational time, Stochastic Gradient Descent (SGD) is used. This study compares the basic CNN model scenario with the addition of layers to the original CNN model to produce the highest accuracy value. Furthermore, each model is cross-validated with a different k to produce performance in each model as well as changes to batch size and epoch and comparison with machine learning models such as SVM, Random Forest, Extra Trees, and kNN. The results showed that the smaller the number of batch sizes, the higher the accuracy value and the number of epochs, the higher the number of epochs, the higher the accuracy value of 99.86%. Then, four machine learning methods with accuracy, sensitivity, and specificity below 90% are all compared to CNN2. As a summary of this research, the proposed CNN modification works better than the four machine learning models in classifying stroke images.

@article{Nugroho2023,
  title     = {A New Method for Classifying Intracerebral Hemorrhage (ICH) Based on Diffusion Weighted –Magnetic Resonance Imaging (DW-MRI)},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {9},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Andi Kurniawan Nugroho and Jajang Edi Priyanto and Dinar Mutiara Kusumo Nugraheni},
  doi       = {10.14569/IJACSA.2023.0140922},
  url       = {https://doi.org/10.14569/IJACSA.2023.0140922}
}