Liver and Tumour Segmentation Using Anchor Free Mechanism-Based Mask Region Convolutional Neural Network

An accurate liver tumour segmentation helps acquire the measurable biomarkers for decision support systems and Computer-Aided Diagnosis (CAD). However, most existing approaches fail to effectively segment tumours in the liver due to the overlapping of liver with any other organ in the image. To solve this problem, this research proposes Anchor Free with Masked Region-based Convolutional Neural Network (AFMRCNN) approach for segmenting liver tumours. The AF attains a precise localization of tumours by directly predicting the tumour location without relying on predefined anchor boxes. Standard datasets like LiTS and CHAOS are utilized to experiment with the efficiency of the proposed method. An EfficientNetB2 is performed to extract the most relevant features from the segmented data. The Deep Neural Network (DNN) is performed for the classification of liver tumours into binary classes by capturing intricate patterns and relationships in the data with the help of a non-linear activation function. The experimental results exhibit the proposed ARMRCNN method’s commendable segmentation performance of 0.998 Dice Similarity Coefficient (DSC), as opposed to the existing methods, UoloNet and UNet++ + pre-activated multiscale Res2Net approach with Channel-wise Attention (PARCA) on the LiTS dataset.

@article{Narasimhulu2024,
  title     = {Liver and Tumour Segmentation Using Anchor Free Mechanism-Based Mask Region Convolutional Neural Network},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {9},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Sangi Narasimhulu and Ch D V Subba Rao},
  doi       = {10.14569/IJACSA.2024.0150959},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150959}
}