Spatial Feature Fusion for Biomedical Image Classification based on Ensemble Deep CNN and Transfer Learning

Biomedical imaging is a rapidly evolving field that covers different types of imaging techniques which are used for diagnostic and therapeutic purposes. It plays a vital role in diagnosis and treating health conditions of human body. Classification of different imaging modalities plays a vital role in terms of providing better care and treatment options to the patients. Advancements in technology open up the new doors for medical professionals and this involves deep learning methods for automatic image classification. Convolutional neural network (CNN) is a special class of deep learning that is applied to visual imagery. In this paper, a novel spatial feature fusion based deep CNN is proposed for classification of microscopic peripheral blood cell images. In this work, multiple transfer learning features are extracted through four pre-trained CNN architectures namely VGG19, ResNet50, MobileNetV2 and DenseNet169. These features are fused into a generalized feature space that increases the classification accuracy. The dataset considered for the experiment contains 17902 microscopic images that are categorized into 8 distinct classes. The result shows that the proposed CNN model with fusion of multiple transfer learning features outperforms the individual pre-trained CNN model. The proposed model achieved 96.10% accuracy, 96.55% F1-score, 96.40% Precision and 96.70% Recall values.

@article{Patel2022,
  title     = {Spatial Feature Fusion for Biomedical Image Classification based on Ensemble Deep CNN and Transfer Learning},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {13},
  number    = {5},
  year      = {2022},
  publisher = {The Science and Information Organization},
  author    = {Sanskruti Patel and Rachana Patel and Nilay Ganatra and Atul Patel},
  doi       = {10.14569/IJACSA.2022.0130519},
  url       = {https://doi.org/10.14569/IJACSA.2022.0130519}
}