@article{100616,
  keywords = {Leprosy detection and classification, ResNet, ensemble learning, GLCM, CAD, Deep spatio-textural feature},
  author = {Jitendra R and Simha JB and Abhi S and Chadha VK},
  title = {Deep Semantic Segmentation Assisted Region-of-Interest Sensitive Deep Spatio-Textural Feature Learning Framework for Leprosy Detection and Classification},
  abstract = {<p>The last few decades have witnessed exponential rise in leprosy also called Hansen diseases globally. Being chronic and infectious in nature, eradicating leprosy has remained a challenge. Despite a few recent efforts employing vision computing-based leprosy detection and classification, most of the at hand solution remain confined due to their inability to address data-imbalance, feature optimality, reliability and scalability. Addressing aforesaid challenges requires a vision-based method to accommodate automated region-of-interest (ROI)-specific information-rich learning and classification. In this paper, a highly robust deep semantic segmentation assisted region-of-interest sensitive deep spatio-textural feature learning framework is proposed for leprosy detection and classification. The proposed method at first applies the different pre-processing methods including contrast adaptive histogram equalization, intensity equalization, resizing. In sync with probable data-imbalance problem, we applied deep semantic segmentation model to obtain the ROI specific regions, which were subsequently processed for RGB conversion. Thus, the obtained ROI-specific RGB images were processed for feature extraction by using deep spatio-textural feature extraction model encompassing GLCM and ResNet101 deep network. Here, GLCM extracted sufficiently large spatio-textural features, while the use of ResNet101 not only provided deep residual features but also alleviated the likelihood of gradient fading problem, which is highly frequent in the state-of-arts like convolutional neural network (CNNs) or other recurrent neural networks (RNNs). Thus, the obtained deep spatio-textual features processed for horizontal concatenation-based feature fusion, which was later trained by using random forest ensemble classifier.The simulation results revealed that the proposed model exhibits accuracy and F-Measure of 97.20% and 97.80%, respectively. Thus, the ability to yield superior accuracy along with the potential to alleviate class-imbalance, feature inferiority and gradient fading makes proposed model more reliable and efficient towards real-time leprosy detection and classification.</p>
},
  year = {2024},
  journal = {SN Computer Science},
  volume = {5},
  publisher = {Springer Science and Business Media LLC},
  issn = {2661-8907},
  doi = {10.1007/s42979-024-03054-2},
  language = {ENG},
}